{"acronym":"sotm2022","aspect_ratio":"16:9","updated_at":"2026-04-01T11:45:07.070+02:00","title":"State of the Map 2022","schedule_url":"","slug":"conferences/geo/sotm2022","event_last_released_at":"2022-10-16T00:00:00.000+02:00","link":"https://2022.stateofthemap.org/","description":"State of the Map is the annual event for all mappers and OpenStreetMap users. In 2022 the State of the Map conference was a three day hybrid conference taking place in Florence, Italy and online.","webgen_location":"conferences/geo/sotm2022","logo_url":"https://static.media.ccc.de/media/events/sotm/2022/sotm_2022-logo.svg","images_url":"https://static.media.ccc.de/media/events/sotm/2022","recordings_url":"https://cdn.media.ccc.de/events/sotm/2022","url":"https://api.media.ccc.de/public/conferences/sotm2022","events":[{"guid":"673b42ce-e1c8-59fb-9060-c245a143bec5","title":"OpenStreetMap as a tool for skill building","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19544-openstreetmap-as-a-tool-for-skill-building","link":"https://2022.stateofthemap.org/sessions/HSSWBD/","description":"This talk explores the effects of OpenStreetMapping on the mappers. These effects also infer that OSM mapping can be used as a tool for skill-building.\n\nOpenStreetMap, the crowdsourced geospatial database, currently has over eight million registered members [1]. This makes it one of the largest VGI projects with proven multifaceted use cases e.g. post-disaster response, combating female genital mutilation, app development, and navigation. The database is wholly made and maintained by its contributors, making all decisions without a top-down governing authority.  People in OSM contribute in multiple ways, extending databases, onboarding newcomers, building community, exchanging information, and providing public benefit. Within the OSM community, OSM mapping is regarded as a form of volunteering to create freely accessible geodata. However, recent studies suggest that the experience a mapper gains through the mapping process could be equally important as well [2-4]. Building on the existing body of knowledge, in this talk, we will share the findings our research on how mapping in OSM affects the mapper. \n\nBeing a quality OSM mapper requires training and practice. The act of OSM mapping requires transitioning from having an interest in mapping to creating an OSM account, learning how to use the application, developing an understanding of the technical and theoretical dimensions of mapping, and then applying these skills and knowledge to accurately convert satellite imagery into map data. Such a process engages mappers in multiple decision-making processes and continuously exposes them to buildings, topographies, and features of satellite imagery. We suggest that such experiences affect the mapper in multiple ways. \n\nWe studied a youth mapping internship called Digital Internship and Leadership (DIAL) Program conducted in three cohorts. We chose this internship program for its inclusiveness in terms of academia, gender, and the geographical locations that the participants came from. Participant mappers were called through an open invitation on social media. Recent high school graduates and undergraduate students participated in the mapping internship. They were from diverse academic backgrounds (geomatics engineering, architecture, crisis management, management, forestry, geomatics engineering, computer science and engineering, electronics engineering, management, public health, mechanical engineering). The internship aimed to reduce OSM data gaps in rural Nepal through the involvement of Nepali high school graduates. The program was designed and executed by Kathmandu Living Labs (KLL). We studied the self-assessed experiences of the participant mappers at two different points of time: (i) during the mapping program (ii) after two years for Cohorts II and III, and three years for Cohort I. Short-term effects were studied through grounded theory coding of reports and blogs documented during the internship period. For long-term impacts, an online survey administered to identify if the effects persisted. \n \nResults show OSM mapping helps the mappers develop a number of vital skill sets and expand their knowledge in a variety of areas. Some of them are: deepening of civic engagement, development of social identity, expansion of geographic knowledge, spatial awareness, increase in happiness and satisfaction. They retain most of these skills even in the long run, irrespective of differences in gender, academic, or professional backgrounds. Surprisingly, 44.8% of the participants cited ‌they considered being a professional mapper or cartographer at some point in time because of their experience in DIAL. The same people report that OSM mapping increased their belief in their ability to help society.\n\nApart from these individual benefits, we also sense collective benefits. Collective benefits such as network development and an increased sense of civic responsibility hold potential to facilitate broader public good. These might also be applicable for youth mobilization, team building, and collective work.\n\nIt is difficult to pinpoint the exact root cause of this development, however, the benefits of OSM mapping may be in part related to the continuous exposure to satellite imagery, continuous use of technology, the requirement of multiple layers of decision-making, humanitarian aspects of OSM, and the growing global OSM community encouraging conversations around it. \n\nOur findings build upon the studies of the use of OSM in high schools, which was noted to increase creativity and spatial awareness among the students [5, 3, 2]. When compared to Minghini et al.’s (2016) study with ten-year-olds, the similarity in findings suggests ‌these developments might be similar across ages. These developments suggest new directions toward the use of OSM as a tool for youth skill building and youth community engagement and design newer incentive mechanisms for people to join and retain in OSM.\n\nThere is still a huge scope of investigation left in this area, ideally through a longitudinal study with a bigger and more diverse sample and comparisons between different program designs, to fully understand the wide array of effects of OSM mapping on the mappers, as well as the potential to deepen positive outcomes via associated youth learning and leadership programs. There are undoubtedly other categories of benefits of OSM mapping that are yet to be identified. Hence, it is worthwhile to reconsider the idea of participatory mapping and related programs, and their effects on the contributing mappers.","original_language":"eng","persons":["Aishworya Shrestha"],"tags":["sotm2022","19544","2022","OSM","OpenStreetMap"],"view_count":12,"promoted":false,"date":"2022-08-21T14:20:00.000+02:00","release_date":"2022-10-14T00:00:00.000+02:00","updated_at":"2024-10-27T12:00:02.202+01:00","length":319,"duration":319,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19544-673b42ce-e1c8-59fb-9060-c245a143bec5.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19544-673b42ce-e1c8-59fb-9060-c245a143bec5_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19544-673b42ce-e1c8-59fb-9060-c245a143bec5.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19544-673b42ce-e1c8-59fb-9060-c245a143bec5.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19544-openstreetmap-as-a-tool-for-skill-building","url":"https://api.media.ccc.de/public/events/673b42ce-e1c8-59fb-9060-c245a143bec5","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"bf7dee86-e2cb-55d4-897a-2050aa9aa7ad","title":"Understanding and modelling accessibility to public green in large urban centers using OpenStreetMap data","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19543-understanding-and-modelling-accessibility-to-public-green-in-large-urban-centers-using-openstreetmap-data","link":"https://2022.stateofthemap.org/sessions/TA9VAF/","description":"OpenStreetMap data represents a valuable source of information for public green areas in large urban centers and effectively measures the United Nations' Sustainable Development Goal 11.7. Our study provides a threefold contribution in this direction. First, we validate land-use-related tags in OpenStreetMap, through a comparison with satellite data from the European Urban Atlas. We then propose a framework and an interactive tool to measure access to public green areas through several established indices. Finally, we show how the framework can be used to simulate the impact of new green areas and help policymakers design effective interventions.\n\nAs of 2020, around 55% of the worldwide population lives in urban areas and the World Bank estimates forecast an increase of around 1.5 times in the urban population by 2045. Cities are also major contributors to the climate-change, with a consumption of about 78% of the worldwide energy and a production of 60% of greenhouse gas emissions. A transition toward greener cities is often called as one of the solutions to reduce the environmental impact of cities, but also to make the urban environments more liveable, with positive spillovers on the mental and physical health of their population. In this context, the United Nations' Sustainable Development Goals 11.7 [1] indicates the need to make cities more inclusive and safe, but also environmentally sustainable, calling for the universal provision of safe, inclusive, and accessible, green and public spaces.  A proper evaluation of this target requires complementing standard average metrics, looking for instance at the surface of green areas per capita within an urban area, with more sophisticated metrics, that are able to capture the interplay between the spatial distribution of both the population and green areas within a city. \nA few studies on selected cities worldwide highlighted the importance of considering this interplay [2-7].\nA recent study on the city of Seoul [3] shows that vast portions of the parks in the city are located in outer areas so that frequent opportunities to visit them are relatively minimal. In general, urban green areas in Seoul are inadequately distributed in relation to population, land use, and development density. By contrast, in the case of Shanghai [6], the degree of accessibility to green areas appears to decrease as we move from the city core to the urban periphery. The authors also found a negative association between the degree of accessibility to green areas and the housing prices, which translates directly into a large environmental inequality, wherein wealthier communities benefit more from green space accessibility than disadvantaged communities. A similar socio-economic, but also ethnic, stratification is observed in the city of Chicago, where white-majority census tracts generally enjoy a significantly higher degree of accessibility to green areas than minority-dominated census tracts [7]. The former ethnic group also presents a lower income-based green-areas accessibility inequity compared to the other racial-ethnic groups.  \nEfforts to move beyond case studies and provide more accurate cross-country indicators have led to the construction of the 'generalised potential access to green areas’ from the European Commission, which is provided as one of the city-level indicators of the Global Human Settlement - Urban Centers Database [8]. The metric measures the proportion of the urban population for urban centers included in the atlas living in high green areas. Based on satellite data on the Normalized Difference Vegetation Index, the metric is however agnostic with respect to the characteristics of these high green areas - for instance, whether these are public or private green areas - and any accessibility notion, since the metric does not consider that people can move from their residential location. These limitations are accounted for in a recent study for the European Environmental Agency [9], whose geographical coverage is however limited to specific urban hotspots in Europe, for which high-resolution land use data from the Urban Atlas (https://land.copernicus.eu/local/urban-atlas) is available. \nWith its worldwide coverage and detailed mapping, the use of land use and street network data from OpenStreetMap [10] allows to expand the analysis beyond the European boundary. Our study provides a threefold contribution in this direction. First, we compare detailed high-resolution land use data on green uses for European hotspots included in the Urban Atlas with land use-related tags in OpenStreetMap for similar geographical areas. We use similarity indices to assess the degree of completeness of the OSM tags of natural land uses in urban environments and show how the quality varies according to the type of natural use and the size as well as the geographical area of the urban center under consideration. Second, we propose a framework for the monitoring of the target for large urban centers worldwide. In particular, by leveraging data from OpenStreetMap and population estimates from the Global Human Settlement [11], we develop a framework to measure accessibility to public green in large urban centers worldwide at a high resolution. For each urban center, we identify natural green areas using OSM tags on ‘land use’, ’natural’ and ‘leisure’ (e.g.: ‘leisure’:’park’) and extract the walkable street network to measure walking distances. Accessibility indices are then constructed for each populated cell of the population grid. The framework is also used to build an interactive tool to navigate our results, which can be customized to select the type of green of interest, as well as the size of the green area. Following the academic literature on urban accessibility, we build several accessibility indices, from a minimum distance index to exposure metrics. The resulting database represents a valuable source of information for policymakers to identify cities that are missing out and direct attention to those subareas within otherwise well-performing cities where the degree of accessibility is still insufficient. The constructed indices are then used to study the relationship between the measured level of accessibility and the structural characteristics of the cities and unveil the role of small green areas as accessibility enhancers, particularly in densely inhabited urban centers. Thirdly, we show how the framework can be used to simulate the impact of different urban interventions, from the addition of a new public green area to infrastructural interventions to the street network, to help policymakers to shape transitions toward more sustainable and accessible urban environments.","original_language":"eng","persons":["Alice Battiston"],"tags":["sotm2022","19543","2022","OSM","OpenStreetMap"],"view_count":42,"promoted":false,"date":"2022-08-21T14:15:00.000+02:00","release_date":"2022-10-14T00:00:00.000+02:00","updated_at":"2025-09-14T18:15:06.547+02:00","length":293,"duration":293,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19543-bf7dee86-e2cb-55d4-897a-2050aa9aa7ad.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19543-bf7dee86-e2cb-55d4-897a-2050aa9aa7ad_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19543-bf7dee86-e2cb-55d4-897a-2050aa9aa7ad.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19543-bf7dee86-e2cb-55d4-897a-2050aa9aa7ad.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19543-understanding-and-modelling-accessibility-to-public-green-in-large-urban-centers-using-openstreetmap-data","url":"https://api.media.ccc.de/public/events/bf7dee86-e2cb-55d4-897a-2050aa9aa7ad","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"a6d753ce-4b2f-5aef-8c57-aca6e1b6ac2a","title":"Inequalities in the completeness of OpenStreetMap buildings in urban centers","subtitle":null,"slug":"state-of-the-map-2022-academic-track-18598-inequalities-in-the-completeness-of-openstreetmap-buildings-in-urban-centers","link":"https://2022.stateofthemap.org/sessions/GPMSLW/","description":"Albeit the manifold usage of OSM building footprints an adequate investigation into their completeness on the global scale has not been conducted so far. This talk investigates OSM building completeness within all 13,135 urban centers covering about 50% of the global population.\n\nThe collaborative maps of OpenStreetMap (OSM) have become a major source of geospatial baseline data for humanitarian organisations, companies and public authorities. Describing the elements of spatial data quality (e.g. positional accuracy, completeness, temporal quality) for the OSM dataset is a key prerequisite to provide the potential stakeholders with the necessary information to decide on the fitness for use of a data set for their particular application [1]. Without information on spatial data quality there are serious barriers to the adoption and usage of new sources such as OSM.\n\nA large community of researchers has analyzed the quality of OSM data in comparison to authoritative reference data sets, by means of remote sensing and using intrinsic measures [2–4]. It has been acknowledged that the OSM data in general is strongly biased, in part due to a much larger contributor basis in countries in the global North as a consequence of socio-economic inequalities and the digital divide [5, 6]. Albeit the manifold usage of OSM building footprints an adequate investigation into their completeness on the global scale has not been conducted so far. This talk investigates OSM building completeness in regions home to a population of 3.5 billion people (about 50% of the global population). First, we propose a machine learning regression method based on generalized additive models (GAMs) to assess OSM building completeness within all 13,135 urban centers (as defined by the European Commission [7]). The analysis utilizes an extensive collection of open building data from commercial and authoritative sources as training data and builds upon very recent technological advances to utilize OSM full-history data for spatio-temporal data analysis on the global scale [8]. This allow us – for the first time – to present a comprehensive assessment of the evolution of urban OSM building completeness which encompasses all data contributed to OSM since 2008.\n\nFor each urban center we calculated the OSM building completeness using the area ratio method which has been applied55 by several other researchers in the context of urban areas [9–11] . Several measures have been adopted to describe the temporal evolution of inequality in urban OSM building mapping on the global scale and per World Bank region. First, we analyzed the share of population living in urban centers with low completeness (\u003c20%) and high completeness (\u003e80%). Gini coefficient has been utilized to derive the degree of evenness of urban OSM building completeness following an approach proposed by Massey \u0026 Denton (1988) to study residential segregation 12 . Moran’s I has been selected as a measure of global spatial autocorrelation of urban OSM building completeness. Spatial autocorrelation has been proposed as an explicitly spatial indicator of segregation covering the dimension of clustering [12, 13]. These analyses has been conducted for annual snapshots from 2008-01-01 up until 2022-01-01.\n\nOverall, urban OSM building completeness is estimated at 38% globally. Our results emphasize that although the well-examined Global North - Global South bias in OSM still exists, over the past years mapping has spread substantially across the globe and within regions. The analysis of the spatial clustering of high completeness values and low completeness values disclosed that global spatial inequality in OSM building completeness has sharply increased between 2008 and 2014. This shows that although overall OSM building completeness became more even in the same period, mapping activity in that time favoured cities which were located close to other cities which were mapped already. One might interprete this as a reinforcing effect. Ongoing mapping in one area triggered even more mapping in surrounded areas. At the same time this also indicates that up until 2014 the expansion of OSM mapping to distant and un-mapped regions (likely to be located in the Global South) didn’t happen at a significant scale.\n\nNevertheless, since 2014 Moran’s I global spatial autocorrelation declined and was measured at 0.55 as of 2022. Combined with the decrease of the Gini coefficient in the same time, this suggests that OSM building completeness has become more even because mapping activity has been expanded to regions which were previously mapped much less. In that regard, OSM building data as of today was much less segregated in terms of both dimensions (evenness and clustering) compared to the state-of-the-map in 2014. This process was to a limited extend positively influenced but humanitarian mapping activity organized through the HOT Tasking manager, but hardly influenced by corporate mapping activity.\n\nWe developed a typology of urban centers based on a methodology to quantify intra-urban completeness pattern by means of evenness and spatial clustering. For this we utilized a fine-scale 1x1 km resolution dataset. In total this analysis covered 4,722 urban centers each with a minimum area of 25 square kilometers. Urban centers have been classified into five different types utilizing an agglomerative clustering approach. Our proposed typology of urban centers incorporates the fact that OSM mapping is rarely distributed equal within cities. Similar findings have been reported for Haiti, where densely mapped zones of Port-au-Prince co-exist alongside zones that remain entirely unmapped [14]. Here we provided a method to quantify these pattern and compare across cities.\n\nThe results reveal the need to address the remaining stark data inequalities, which could not be turned around so far by humanitarian and corporate organized mapping activities. We conclude with recommendations directed at stakeholders working with OSM data: (1) Multi-scale building completeness measures should be applied before subsequent usage of OSM data to outline the potential negative effect of missing data. (2) Completeness maps should be used in combination with socio-demographic information to guide future mapping activities to ensure that \"nobody is left behind\" as encouraged by the SDGs.","original_language":"eng","persons":["Benjamin Herfort"],"tags":["sotm2022","18598","2022","OSM","OpenStreetMap"],"view_count":58,"promoted":false,"date":"2022-08-21T10:35:00.000+02:00","release_date":"2022-10-11T00:00:00.000+02:00","updated_at":"2026-03-18T12:15:06.630+01:00","length":317,"duration":317,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18598-a6d753ce-4b2f-5aef-8c57-aca6e1b6ac2a.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18598-a6d753ce-4b2f-5aef-8c57-aca6e1b6ac2a_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18598-a6d753ce-4b2f-5aef-8c57-aca6e1b6ac2a.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18598-a6d753ce-4b2f-5aef-8c57-aca6e1b6ac2a.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-18598-inequalities-in-the-completeness-of-openstreetmap-buildings-in-urban-centers","url":"https://api.media.ccc.de/public/events/a6d753ce-4b2f-5aef-8c57-aca6e1b6ac2a","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"533f1d14-81e5-5b87-ad38-8bf0bdb0c9cc","title":"Innovating on derivative OpenStreetMap datasets","subtitle":null,"slug":"sotm2022-18481-innovating-on-derivative-openstreetmap-datasets","link":"https://2022.stateofthemap.org/sessions/VXECJQ/","description":"OpenStreetMap consists of tagged nodes, ways and relations. Many use cases of geographic data, however, need a tabular dataset of points, lines and polygons. Processing OSM into derivative datasets is a crucial task that can benefit from new tools and formats. This talk will cover several topics around this theme, including:\n\n* Existing approaches such as the Export Tool\n* Why FlatGeobuf is a suitable forwards-thinking format\n* Computational challenges for processing global-scale relations\n* A new open-source program, Protoshapes, to generate admin polygons in FlatGeobuf format\n* Efficient approaches for global datasets such as coastlines, oceans, and road connectivity\n* Frequently updating datasets using the open-source OSM Express database","original_language":"eng","persons":["Brandon Liu"],"tags":["sotm2022","18481","2022","Software Development","OSM","OpenStreetMap"],"view_count":64,"promoted":false,"date":"2022-08-19T14:30:00.000+02:00","release_date":"2022-09-21T00:00:00.000+02:00","updated_at":"2025-09-22T00:15:08.211+02:00","length":1556,"duration":1556,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18481-533f1d14-81e5-5b87-ad38-8bf0bdb0c9cc.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18481-533f1d14-81e5-5b87-ad38-8bf0bdb0c9cc_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18481-533f1d14-81e5-5b87-ad38-8bf0bdb0c9cc.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18481-533f1d14-81e5-5b87-ad38-8bf0bdb0c9cc.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18481-innovating-on-derivative-openstreetmap-datasets","url":"https://api.media.ccc.de/public/events/533f1d14-81e5-5b87-ad38-8bf0bdb0c9cc","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"deb26ce9-1a5f-533b-974a-017b4225c69d","title":"Increasing OpenStreetMap Data Accessibility with the Analysis-Ready Daylight Distribution of OpenStreetMap: A Demonstration of Cloud-Based Assessments of Global Building Completeness","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19380-increasing-openstreetmap-data-accessibility-with-the-analysis-ready-daylight-distribution-of-openstreetmap-a-demonstration-of-cloud-based-assessments-of-global-building-completeness","link":"https://2022.stateofthemap.org/sessions/JRN9DN/","description":"A recent release of new scientific datasets generated from OpenStreetMap exemplifies the need for analysis-ready repositories of OSM data that require minimal pre-processing. We created the  Analysis-Ready Daylight OpenStreetMap Distribution to provide researchers with the opportunity for simple cloud-based SQL queries of nearly 1B OSM features. We demonstrate the capabilities with intrinsic and extrinsic data coverage assessments of OSM buildings globally.\n\nDespite being one of the most open and freely available spatial datasets, OpenStreetMap (OSM) data accessibility remains a challenge. Data accessibility measures how easily end-users can access and use a given dataset for their needs [1]. Because OSM data is intended to be rendered as a map or ingested into routing engines, it is often not easily consumable by data analysts. Pre-analysis workflows require OSM data to be downloaded, parsed, and converted into more common formats, which means that novice end-users of OSM may lack the experience to readily access and use OSM data in decision-making.\n\nIncorporating communities into spatial decision-making processes, such as mapping, is important because a). community members are experts on their communities and b). have a larger stake in final solutions which directly impacts their lived-experiences[2]. OSM empowers a variety of communities, including local governments[3], digital humanitarian groups[4], and even student groups [5], to help navigate and understand places of respective importance.\n\nResearch by Nirandjan et al. recently lowered barriers to using OSM data as a reference dataset of critical infrastructure [6]. After categorizing and quantifying particular types of OSM features, the authors released the data in formats more common in geospatial analysis, such as GeoTiffs [6]. This article’s popularity (ranked 90th percentile on the publisher’s website) demonstrates the importance of making OSM data—and datasets derived from OSM—more accessible by means of familiar data structures compatible with common tools. If OSM data were more accessible for analysis, could we see it used in more geospatial research and innovation at large [7]?\n\nWhile many community-maintained tools exist to convert, extract, and download OSM data, each requires domain knowledge of the unique OSM data structure (nodes, ways, and relations). Furthermore, working at the country or planet-scale requires extensive computational resources. To further lower the barriers to entry for OSM data analysis and extraction, we created the Analysis-Ready Daylight OpenStreetMap Distribution (ARD-OSM). ARD-OSM is published on the registry of open data (RODA) on Amazon Web Services (AWS), where it is freely available to anyone [8]. This dataset containing 1B OSM features is optimized for use with Amazon Athena, a serverless interactive query engine on AWS. Additionally, ARD-OSM has  resolved the OSM data format into common geometries such as points, lines, and polygons. Data also includes pre-computed valuable attributes such as length, surface area, quadkeys, and geographic bounding boxes which are stored as additional metadata. To demonstrate the analytical capabilities of this dataset, next, we will perform a global OSM building density assessment.\n\nBuilding density is a common metric in OSM quality research, often used to assess map coverage and completeness, such as studied by Yeboah et al. [9]. Measuring building density requires counting all of the buildings within a defined unit of spatial analysis. We use zoom-level 11 map tiles to create an analysis grid that encompasses the global built environment in fewer than 1M tiles. Then, we divide the building count by the area of each map tile to obtain the number of buildings mapped per square kilometer.\nSince every feature in ARD-OSM includes the zoom-level 15 quadkey of the map tile in which it exists, we can use a SQL GROUP BY expression instead of a geospatial operator for aggregation. Here is the short query used to count the number of buildings in each zoom-level 11 map tile: \n\n```sql\nSELECT \tsubstr(quadkey, 1, 11) as z11_tile,\n\t \tcount(id) as number_of_buildings\nFROM \tanalysis_ready_daylight \nWHERE tags[‘building’] IS NOT NULL AND release = ‘v1.12’\nGROUP BY substr(quadkey, 1, 11)\n```\nIn May 2022, running in AWS region us-east-1, this query took 15 seconds and cost just USD $0.10. The results of this query show the density of mapped buildings in OSM to be highest in Europe with additional areas of high density where Humanitarian mapping campaigns have been active such as Nepal, South Eastern Asia, and isolated parts of Africa. This is consistent with the findings of Herfort et al. [10].\n\nHow should these densities be interpreted? Do denser regions have higher levels of building completeness in which most or all buildings are mapped? Building density is an intrinsic data quality measure, to further contextualize these findings, we need to perform an extrinsic assessment by comparing our results against an external dataset. A recent study confirmed the viability of referencing population data for building density assessment [11]; and Orden et al. demonstrate a three-step methodology using Facebook’s High Resolution Settlement Layer (HRSL) first requiring both vectorization and spatial aggregation to assess building completeness with respect to population in both the Philippines and Madagascar [12].\n\nBecause the HRSL is also published via RODA [13], it can be easily joined to our results. Once HRSL data is incorporated to obtain a measure of buildings mapped per square kilometer per person, we find that parts of Europe remain in the top tiers of density with the most buildings mapped per person. Nepal and many parts of South Eastern Asia, however, are no longer in the same top tier of map coverage. While there are many mapped buildings, the higher populations of these regions reveals that there are still many areas where the buildings have yet to be mapped. This yields a generally lower level of completeness overall than initially identified, which remains consistent with the findings in [10]. Additionally, parts of the United States and New Zealand actually appear more complete with areas of lower density coinciding with regions of lower population, yielding a higher measure of map completeness than before. \n\nThis case study cheaply and easily reproduced popular methods for both intrinsic and extrinsic data quality assessments of OSM building coverage without needing to download nor pre-process any OSM data. The analysis was done completely in the cloud on AWS using free and open data in RODA. Additional metadata in ARD-OSM enabled the query to run efficiently and cost-effectively. The same methodology can be applied to investigations of any other object type in OSM from hospitals to ice cream shops. We also recognize that ARD-OSM does not solve the needs of researchers looking to work with OSM history data. Other tools such as the OpenStreetMap History Database are better suited for those types of historical analyses [14].\n\nThe release of OSM-based datasets, such as Nirandjan et al. [6], shows a desire for more researchers to  use OSM data. While OSM data is freely and openly available, researchers must take many steps to download, transform, and ingest the data into an analysis workflow. To solve this, we have made ARD-OSM available in RODA. This analysis-ready dataset contains 1B features—nearly every OpenStreetMap object—in common geospatial feature types such as points, lines, and polygons. To additionally aid researchers, features are enriched with additional metadata describing their location and physical attributes such as length or surface area. From a data accessibility perspective, we anticipate ARD-OSM in future research and innovation, curated by a wide range of end-users, to readily integrate OSM data for decision-making processes which bring communities closer together.","original_language":"eng","persons":["Jennings Anderson","Timmera Whaley Omidire"],"tags":["sotm2022","19380","2022","OSM","OpenStreetMap"],"view_count":58,"promoted":false,"date":"2022-08-21T09:00:00.000+02:00","release_date":"2022-10-06T00:00:00.000+02:00","updated_at":"2025-10-27T10:30:04.542+01:00","length":1439,"duration":1439,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19380-deb26ce9-1a5f-533b-974a-017b4225c69d.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19380-deb26ce9-1a5f-533b-974a-017b4225c69d_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19380-deb26ce9-1a5f-533b-974a-017b4225c69d.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19380-deb26ce9-1a5f-533b-974a-017b4225c69d.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19380-increasing-openstreetmap-data-accessibility-with-the-analysis-ready-daylight-distribution-of-openstreetmap-a-demonstration-of-cloud-based-assessments-of-global-building-completeness","url":"https://api.media.ccc.de/public/events/deb26ce9-1a5f-533b-974a-017b4225c69d","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"1d1ee80e-c6f4-570a-a58f-96b6dfe023ed","title":"Building an OpenStreetMap Community Playbook","subtitle":null,"slug":"sotm2022-18502-building-an-openstreetmap-community-playbook","link":"https://2022.stateofthemap.org/sessions/U77MUX/","description":"Every day hundreds of people sign up for OpenStreetMap. We have several active OpenStreetMap communities, communities that are struggling to sustain themselves and at the same time,  there are countries with no existing OpenStreetMap communities despite having contributors from those countries. \nThis idea of a community playbook is to act as a guide for persons interested in starting up an OSM community and sustaining OSM communities with  lessons drawn from existing communities.\n\nThe community playbook is based on 4 themes; Identifying local community issues, attracting and engaging students, Connecting contributors motivation to mapping and Training","original_language":"eng","persons":["Sharon Omoja","Geoffrey Kateregga"],"tags":["sotm2022","18502","2022","Community and Foundation","OSM","OpenStreetMap"],"view_count":20,"promoted":false,"date":"2022-08-20T10:00:00.000+02:00","release_date":"2022-09-29T00:00:00.000+02:00","updated_at":"2023-11-27T21:15:05.385+01:00","length":1599,"duration":1599,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18502-1d1ee80e-c6f4-570a-a58f-96b6dfe023ed.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18502-1d1ee80e-c6f4-570a-a58f-96b6dfe023ed_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18502-1d1ee80e-c6f4-570a-a58f-96b6dfe023ed.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18502-1d1ee80e-c6f4-570a-a58f-96b6dfe023ed.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18502-building-an-openstreetmap-community-playbook","url":"https://api.media.ccc.de/public/events/1d1ee80e-c6f4-570a-a58f-96b6dfe023ed","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"24370f9a-2ac4-5467-8fa9-dfe56e7af049","title":"The MapOSMatic APIs - generate printable maps from your own application","subtitle":null,"slug":"sotm2022-18516-the-maposmatic-apis-generate-printable-maps-from-your-own-application","link":"https://2022.stateofthemap.org/sessions/HBBZKV/","description":"The MapOSMatic web frontend allows to create printable maps from OSM\ndata interactively. This is not the only way to use its rendering\nbackend, it is also possible to directly use its ocitysmap Python\nlibrary to render maps from your own Python code and a local\nstylesheet and database setup, or to use the REST-like API of the web\nfrontend to send automated render requests to a MapOSMatic web\ninstance from almost any programming language without any local setup\neffort.\n\nThe presentation will give a short overview of both API variants,\nshowing the different options to interact with the MapOSMatic render\ninfrastructure programmatically.\n\nAs example applications an alternative neighbourhood","original_language":"eng","persons":["Hartmut Holzgraefe"],"tags":["sotm2022","18516","2022","Software Development","OSM","OpenStreetMap"],"view_count":57,"promoted":false,"date":"2022-08-20T10:00:00.000+02:00","release_date":"2022-09-25T00:00:00.000+02:00","updated_at":"2025-12-30T22:30:18.932+01:00","length":1357,"duration":1357,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18516-24370f9a-2ac4-5467-8fa9-dfe56e7af049.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18516-24370f9a-2ac4-5467-8fa9-dfe56e7af049_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18516-24370f9a-2ac4-5467-8fa9-dfe56e7af049.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18516-24370f9a-2ac4-5467-8fa9-dfe56e7af049.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18516-the-maposmatic-apis-generate-printable-maps-from-your-own-application","url":"https://api.media.ccc.de/public/events/24370f9a-2ac4-5467-8fa9-dfe56e7af049","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"ced5974e-89a3-5fed-8640-4716ff5e20ae","title":"Running OpenStreetMap.org - Today and Tomorrow","subtitle":null,"slug":"sotm2022-18162-running-openstreetmap-org-today-and-tomorrow","link":"https://2022.stateofthemap.org/sessions/D8XYDN/","description":"This session will provide an introduction to the OpenStreetMap operations team, what OpenStreetMap.org services we are responsible for building and maintaining.\n\nGrant recently became the OpenStreetMap Foundation's first full-time employee. Grant will present how he is helping improve reliability and security of the project's technology and infrastructure.\n\nGrant will detail how the Operations team are modernising the project's infrastructure by reducing complexity, paying-down [technical debt](https://en.wikipedia.org/wiki/Technical_debt), while reducing the need to maintain [undifferentiated heavy lifting](https://www.factoftheday1.com/p/december-23-undifferentiated-heavy).\n\nIf you’re interested in what powers OpenStreetMap and make it tick, come to this session.","original_language":"eng","persons":["Grant Slater"],"tags":["sotm2022","18162","2022","Community and Foundation","OSM","OpenStreetMap"],"view_count":302,"promoted":false,"date":"2022-08-20T17:00:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2026-03-25T00:00:06.800+01:00","length":1769,"duration":1769,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18162-ced5974e-89a3-5fed-8640-4716ff5e20ae.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18162-ced5974e-89a3-5fed-8640-4716ff5e20ae_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18162-ced5974e-89a3-5fed-8640-4716ff5e20ae.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18162-ced5974e-89a3-5fed-8640-4716ff5e20ae.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18162-running-openstreetmap-org-today-and-tomorrow","url":"https://api.media.ccc.de/public/events/ced5974e-89a3-5fed-8640-4716ff5e20ae","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"2352197e-b9a4-50ea-b591-539272590bd0","title":"OSM and indoor data","subtitle":null,"slug":"sotm2022-18490-osm-and-indoor-data","link":"https://2022.stateofthemap.org/sessions/HEKRDY/","description":"OpenIndoor is an open source SaaS that uses OpenStreetMap indoor data to display a 3D graphical rendering of building interiors. The resulting map offers a gamified experience to meet different types of needs: indoor navigation, data representation, immersive tour etc.\nWe will discuss how we use the available open data and the Maplibre engine to address these different use cases.","original_language":"eng","persons":["Clement Igonet"],"tags":["sotm2022","18490","2022","Software Development","OSM","OpenStreetMap"],"view_count":103,"promoted":false,"date":"2022-08-21T14:30:00.000+02:00","release_date":"2022-10-02T00:00:00.000+02:00","updated_at":"2026-02-27T16:30:06.541+01:00","length":1546,"duration":1546,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18490-2352197e-b9a4-50ea-b591-539272590bd0.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18490-2352197e-b9a4-50ea-b591-539272590bd0_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18490-2352197e-b9a4-50ea-b591-539272590bd0.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18490-2352197e-b9a4-50ea-b591-539272590bd0.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18490-osm-and-indoor-data","url":"https://api.media.ccc.de/public/events/2352197e-b9a4-50ea-b591-539272590bd0","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"f418843d-4f15-562b-bf53-7131f9210a76","title":"Opening Session - Academic Track","subtitle":null,"slug":"state-of-the-map-2022-academic-track-23290-opening-session-academic-track","link":"https://2022.stateofthemap.org/sessions/RBZHX7/","description":"The opening session of the Academic Track at the State of the Map 2022 conference.","original_language":"eng","persons":["Yair Grinberger \u0026 Marco Minghini"],"tags":["sotm2022","23290","2022","OSM","OpenStreetMap"],"view_count":63,"promoted":false,"date":"2022-08-21T08:55:00.000+02:00","release_date":"2022-10-16T00:00:00.000+02:00","updated_at":"2026-03-23T12:00:10.009+01:00","length":208,"duration":208,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/23290-f418843d-4f15-562b-bf53-7131f9210a76.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/23290-f418843d-4f15-562b-bf53-7131f9210a76_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/23290-f418843d-4f15-562b-bf53-7131f9210a76.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/23290-f418843d-4f15-562b-bf53-7131f9210a76.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-23290-opening-session-academic-track","url":"https://api.media.ccc.de/public/events/f418843d-4f15-562b-bf53-7131f9210a76","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"6cfb7e85-f3f1-5488-bda2-0f6be8c9726e","title":"Closing Session","subtitle":null,"slug":"sotm2022-19716-closing-session","link":"https://2022.stateofthemap.org/sessions/LX3EGF/","description":"The closing session of the State of the Map 2022 conference.","original_language":"eng","persons":["SotM Working Group"],"tags":["sotm2022","19716","2022","OSM","OpenStreetMap"],"view_count":17,"promoted":false,"date":"2022-08-21T16:00:00.000+02:00","release_date":"2022-10-03T00:00:00.000+02:00","updated_at":"2025-03-20T17:45:03.242+01:00","length":1931,"duration":1931,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19716-6cfb7e85-f3f1-5488-bda2-0f6be8c9726e.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19716-6cfb7e85-f3f1-5488-bda2-0f6be8c9726e_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19716-6cfb7e85-f3f1-5488-bda2-0f6be8c9726e.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19716-6cfb7e85-f3f1-5488-bda2-0f6be8c9726e.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-19716-closing-session","url":"https://api.media.ccc.de/public/events/6cfb7e85-f3f1-5488-bda2-0f6be8c9726e","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"e64e69ba-0d6b-57de-860e-e982e3668df8","title":"Lightning talks I","subtitle":null,"slug":"sotm2022-19710-lightning-talks-i","link":"https://2022.stateofthemap.org/sessions/ZVLDMZ/","description":"Lightning talks\n\n## AddressForAll Institute\n\n_by Thierry Jean_\n\n## ImproveOSM new data dumps\n\n_by Beata Tautan-Jancso_\n\nThe ImproveOSM platform is a suite of tools to share various mapping tasks containing potentially missing one-way tags, turn restrictions, and roads from the OpenStreetMap.  The ImproveOSM data is also available as frequent data dumps in CSV format.\n\n## Geohash plugin\n\n_by Beata Tautan-Jancso, Nicoleta Viregan_\n\nGeohash is a plugin available in the JOSM tool, which comes to be handy for some of you who work in precise areas based on geohash units. The plugin displays a layer of grids on top of the map layer. In addition to the visualization feature, the plugin offers extra functionalities, and this talk aims to emphasize the diverse usages of this tool.","original_language":"eng","persons":["Various Speakers"],"tags":["sotm2022","19710","2022","OSM","OpenStreetMap"],"view_count":23,"promoted":false,"date":"2022-08-19T14:30:00.000+02:00","release_date":"2022-09-24T00:00:00.000+02:00","updated_at":"2025-07-06T09:45:03.068+02:00","length":973,"duration":973,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19710-e64e69ba-0d6b-57de-860e-e982e3668df8.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19710-e64e69ba-0d6b-57de-860e-e982e3668df8_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19710-e64e69ba-0d6b-57de-860e-e982e3668df8.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19710-e64e69ba-0d6b-57de-860e-e982e3668df8.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-19710-lightning-talks-i","url":"https://api.media.ccc.de/public/events/e64e69ba-0d6b-57de-860e-e982e3668df8","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"05820077-0508-5ee6-9bbd-ffb11555cf7a","title":"usability testing with three people - how to discover why mappers are confused by your software","subtitle":null,"slug":"sotm2022-18517-usability-testing-with-three-people-how-to-discover-why-mappers-are-confused-by-your-software","link":"https://2022.stateofthemap.org/sessions/EHZQXV/","description":"Usability testing can be done without hordes of users to observe, in fact just three people is likely to give very useful hints.\n\nAnd it is almost certainly more useful than expected, and your software is likely not as good as you expect.\n\nThis is based on my experience with using user testing while developing StreetComplete.","original_language":"eng","persons":["Mateusz Konieczny"],"tags":["sotm2022","18517","2022","Software Development","OSM","OpenStreetMap"],"view_count":106,"promoted":false,"date":"2022-08-19T12:30:00.000+02:00","release_date":"2022-09-21T00:00:00.000+02:00","updated_at":"2026-03-12T16:00:10.157+01:00","length":1795,"duration":1795,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18517-05820077-0508-5ee6-9bbd-ffb11555cf7a.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18517-05820077-0508-5ee6-9bbd-ffb11555cf7a_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18517-05820077-0508-5ee6-9bbd-ffb11555cf7a.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18517-05820077-0508-5ee6-9bbd-ffb11555cf7a.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18517-usability-testing-with-three-people-how-to-discover-why-mappers-are-confused-by-your-software","url":"https://api.media.ccc.de/public/events/05820077-0508-5ee6-9bbd-ffb11555cf7a","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"5336bc37-a5a6-5eed-af11-6f54b4bb36a3","title":"Entry-level Mobile Mapping","subtitle":null,"slug":"sotm2022-18529-entry-level-mobile-mapping","link":"https://2022.stateofthemap.org/sessions/UWHAME/","description":"By 2025, HOT aims that communities in 94 countries vulnerable to disaster or experiencing multidimensional poverty are equipped and able to map the locations where they live and work. We believe that accessible mobile mapping tools are key to this effort. Since I started working for HOT in January 2022, I have done informal interviews, observations, focus groups and experiments with more than 100 regional users, primarily in East Africa, looking at the accessibility of current OSM mobile editing tools. Sharing this research with the wider community will help everyone build technology that fits the needs on the ground.\n\nFor 3 months, with the support of OpenMap Development Tanzania (OMDTZ) and the State University of Zanzibar (SUZA) Youth Mappers, I did a variety of informal interviews, observations, focus groups, and experiments with individuals and local OpenMapping organizations, primarily in East Africa. My research included professional data collection campaigns, as well as entry-level community members whose first encounter of OSM was during one of my workshops. \n\nWhile the majority of my research focused on 5 main Open Source applications that are already in widespread use (StreetComplete, OsmAnd, Organic Maps, Vespucci \u0026 ODK Collect), many of the insights gained from this research, including data about hardware availability and current phone use, are applicable to anyone building mapping tools, especially those that will be used in low-resource environments. \n\nThe HOT _tech team aims to do more than just talk. Analysis of the research will be followed by specific suggestions for those doing mobile OSM mapping, especially at the entry-level, as well as technical proposals for tooling improvement. We will be working with the existing OSM ecosystem to design and implement these proposals in a way that is inclusive and sustainable. \n\nFeedback is most welcome.","original_language":"eng","persons":["Kristen Tonga"],"tags":["sotm2022","18529","2022","User Experiences","OSM","OpenStreetMap"],"view_count":83,"promoted":false,"date":"2022-08-19T12:00:00.000+02:00","release_date":"2022-09-21T00:00:00.000+02:00","updated_at":"2025-07-04T12:00:04.973+02:00","length":1318,"duration":1318,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18529-5336bc37-a5a6-5eed-af11-6f54b4bb36a3.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18529-5336bc37-a5a6-5eed-af11-6f54b4bb36a3_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18529-5336bc37-a5a6-5eed-af11-6f54b4bb36a3.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18529-5336bc37-a5a6-5eed-af11-6f54b4bb36a3.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18529-entry-level-mobile-mapping","url":"https://api.media.ccc.de/public/events/5336bc37-a5a6-5eed-af11-6f54b4bb36a3","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"9d5c1aec-546c-5f28-98e9-bee4ff8da14a","title":"MapBuilder - The simplest OSM editorial tool","subtitle":null,"slug":"sotm2022-18440-mapbuilder-the-simplest-osm-editorial-tool","link":"https://2022.stateofthemap.org/sessions/B7VADW/","description":"Every day millions of users experience delightful features on Bing Maps. Each user, regardless of their technical background, possesses a wealth of local knowledge that can help improve map data which for many country regions comes from OpenStreetMap. The common OSM editorial tools iD \u0026 JOSM are far too advanced for our users. Hence, we embarked on a mission to build a very simple tool - MapBuilder - that can guide users to volunteer their local knowledge to update map data via a set of guided screens. This talk will focus on the following: \n - First features \n - Building community engagement \n - Identifying data gaps\n - Potential risks and mitigations","original_language":"eng","persons":["Nemanja Bracko"],"tags":["sotm2022","18440","2022","Mapping","OSM","OpenStreetMap"],"view_count":383,"promoted":false,"date":"2022-08-20T11:30:00.000+02:00","release_date":"2022-09-25T00:00:00.000+02:00","updated_at":"2026-04-01T00:15:05.250+02:00","length":1594,"duration":1594,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18440-9d5c1aec-546c-5f28-98e9-bee4ff8da14a.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18440-9d5c1aec-546c-5f28-98e9-bee4ff8da14a_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18440-9d5c1aec-546c-5f28-98e9-bee4ff8da14a.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18440-9d5c1aec-546c-5f28-98e9-bee4ff8da14a.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18440-mapbuilder-the-simplest-osm-editorial-tool","url":"https://api.media.ccc.de/public/events/9d5c1aec-546c-5f28-98e9-bee4ff8da14a","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"b4c3d273-3591-546b-9a36-5afef963b3e4","title":"Exploring Human Bias and Effects of Training in OSM mapping: A Behavioral Experiment in Singapore","subtitle":null,"slug":"sotm2022-18338-exploring-human-bias-and-effects-of-training-in-osm-mapping-a-behavioral-experiment-in-singapore","link":"https://2022.stateofthemap.org/sessions/RHF3UX/","description":"Human factor is one of the most crucial elements in crowdsourced mapping. This research explores how human bias affects the mapping process and whether such effects can be mitigated through targeted training with a behavioral experiment. The experiment uses a two-group randomized design. The treatment group receives more advanced training than the control group. There are two goals for the experiment. First, we aim to identify the common types of bias that amateurs from a specific demographic community have when using OSM. Second, we plan to explore whether training is helpful for reducing those biases and improve the quality of mapping.\n\nOpenStreetMap (OSM) is one of the VGI platforms that has been curated primarily by volunteers, which indicates that the demographic differences of the backgrounds of volunteers might affect their understanding of mapping and their mapping behavior.\nFor example, contributors with varying skills and experiences of mapping and GIS software might choose different objects to map and trace them in different levels of detail. There is a wide range of factors that could have an impact on how and what individual contributors choose to map: age, gender, expertise, education, income, etc. This study focuses on digging into the mapping behavior of a specific demographic community - residents from Singapore. We have observed changes in terms of tagging and editing behavior in OSM before and after different levels of training. This study has important implications for OSM mapping, especially platforms such as HOTOSM which largely rely on faraway amateur curators to provide up-to-date geographical information of a specific area in case of events such as wars, natural disasters, crimes or humanitarian emergencies.","original_language":"eng","persons":["Shiyue Zhong"],"tags":["sotm2022","18338","2022","Remote Control","OSM","OpenStreetMap"],"view_count":13,"promoted":false,"date":"2022-08-21T10:30:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2024-07-06T15:45:02.253+02:00","length":1485,"duration":1485,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18338-b4c3d273-3591-546b-9a36-5afef963b3e4.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18338-b4c3d273-3591-546b-9a36-5afef963b3e4_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18338-b4c3d273-3591-546b-9a36-5afef963b3e4.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18338-b4c3d273-3591-546b-9a36-5afef963b3e4.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18338-exploring-human-bias-and-effects-of-training-in-osm-mapping-a-behavioral-experiment-in-singapore","url":"https://api.media.ccc.de/public/events/b4c3d273-3591-546b-9a36-5afef963b3e4","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"dbd31ade-265d-5c2b-8f79-43751198e7a6","title":"Lightning talks II","subtitle":null,"slug":"sotm2022-19711-lightning-talks-ii","link":"https://2022.stateofthemap.org/sessions/VJMH9U/","description":"Lighting talks registered during the State of the Map conference.\n\n## UN Maps Learning Hub\n\n_by Séverin Ménard_\n\n## [community.osm.org](https://community.osm.org)\n\n_by Tobias Knerr_\n\n## Using OSM in RPGs\n\n... or better in VTTRPGs.\n\n_by Marco Montanari_\n\n## [tile.openstreetmap.jp](https://tile.openstreetmap.jp/)\n\nPlanet vector/raster tile server.\n\n_by Taro Matsuzawa_","original_language":"eng","persons":["Various Speakers"],"tags":["sotm2022","19711","2022","OSM","OpenStreetMap"],"view_count":41,"promoted":false,"date":"2022-08-19T17:30:00.000+02:00","release_date":"2022-09-25T00:00:00.000+02:00","updated_at":"2026-03-12T17:15:05.291+01:00","length":1364,"duration":1364,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19711-dbd31ade-265d-5c2b-8f79-43751198e7a6.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19711-dbd31ade-265d-5c2b-8f79-43751198e7a6_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19711-dbd31ade-265d-5c2b-8f79-43751198e7a6.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19711-dbd31ade-265d-5c2b-8f79-43751198e7a6.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-19711-lightning-talks-ii","url":"https://api.media.ccc.de/public/events/dbd31ade-265d-5c2b-8f79-43751198e7a6","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"c1af8fa6-df06-554a-ab2f-ef0282dd4479","title":"Opening Session","subtitle":null,"slug":"sotm2022-19715-opening-session","link":"https://2022.stateofthemap.org/sessions/QFUTA7/","description":"The opening session of the State of the Map 2022 conference.","original_language":"eng","persons":["SotM Working Group"],"tags":["sotm2022","19715","2022","OSM","OpenStreetMap"],"view_count":139,"promoted":false,"date":"2022-08-19T10:00:00.000+02:00","release_date":"2022-09-18T00:00:00.000+02:00","updated_at":"2025-10-14T12:30:03.594+02:00","length":1230,"duration":1230,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19715-c1af8fa6-df06-554a-ab2f-ef0282dd4479.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19715-c1af8fa6-df06-554a-ab2f-ef0282dd4479_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19715-c1af8fa6-df06-554a-ab2f-ef0282dd4479.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19715-c1af8fa6-df06-554a-ab2f-ef0282dd4479.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-19715-opening-session","url":"https://api.media.ccc.de/public/events/c1af8fa6-df06-554a-ab2f-ef0282dd4479","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"99f6f28f-2e3d-5134-9362-4f15f2c27512","title":"How to kill OSM? Above all, change nothing","subtitle":null,"slug":"sotm2022-18491-how-to-kill-osm-above-all-change-nothing","link":"https://2022.stateofthemap.org/sessions/A8JLUY/","description":"OSM is almost 20 years old and we already achieved so much. What if the governance of the project as well as our relationship to time and money were the biggest obstacles to ensure a bright future?\nLet’s discuss the priorities to unleash the full potential of our community.","original_language":"eng","persons":["Florian Lainez"],"tags":["sotm2022","18491","2022","Community and Foundation","OSM","OpenStreetMap"],"view_count":167,"promoted":false,"date":"2022-08-20T14:30:00.000+02:00","release_date":"2022-09-23T00:00:00.000+02:00","updated_at":"2025-09-04T03:45:03.329+02:00","length":1960,"duration":1960,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18491-99f6f28f-2e3d-5134-9362-4f15f2c27512.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18491-99f6f28f-2e3d-5134-9362-4f15f2c27512_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18491-99f6f28f-2e3d-5134-9362-4f15f2c27512.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18491-99f6f28f-2e3d-5134-9362-4f15f2c27512.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18491-how-to-kill-osm-above-all-change-nothing","url":"https://api.media.ccc.de/public/events/99f6f28f-2e3d-5134-9362-4f15f2c27512","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"99bb9add-d4f6-55d9-af66-1df78842dbdd","title":"State of OSM in QGIS","subtitle":null,"slug":"sotm2022-18453-state-of-osm-in-qgis","link":"https://2022.stateofthemap.org/sessions/AKYJPG/","description":"QGIS is one of the most used Opensource GIS software with some native functionalities to work with OSM data. Either with raster layer as a basemap, or with vector, QGIS can deal with OSM data. Depending on the amount of data to work with, the need to \"refresh\" the data (from the main OSM database), the extent of the coverage, different plugins or technologies are possible.\nThis presentation will try to give an overview how it's possible to use OpenStreetMap data according to different situations (Geocoding, TMS/WMS, OverpassAPI, PostgreSQL…). The presentation will show how you can contribute to QuickOSM to add some default « mappreset » to QuickOSM on GitHub.","original_language":"eng","persons":["Etienne Trimaille"],"tags":["sotm2022","18453","2022","Cartography","OSM","OpenStreetMap"],"view_count":74,"promoted":false,"date":"2022-08-20T16:30:00.000+02:00","release_date":"2022-10-15T00:00:00.000+02:00","updated_at":"2026-03-09T17:00:18.743+01:00","length":1700,"duration":1700,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18453-99bb9add-d4f6-55d9-af66-1df78842dbdd.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18453-99bb9add-d4f6-55d9-af66-1df78842dbdd_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18453-99bb9add-d4f6-55d9-af66-1df78842dbdd.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18453-99bb9add-d4f6-55d9-af66-1df78842dbdd.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18453-state-of-osm-in-qgis","url":"https://api.media.ccc.de/public/events/99bb9add-d4f6-55d9-af66-1df78842dbdd","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"672a387e-6367-55ac-8c40-4bb214aed887","title":"osm2streets: Street networks with detailed geometry","subtitle":null,"slug":"sotm2022-18478-osm2streets-street-networks-with-detailed-geometry","link":"https://2022.stateofthemap.org/sessions/9NHQQM/","description":"OpenStreetMap has many details about streets, but applications rendering or simulating lane-level detail face many challenges: determining lane properties along one street, calculating geometry of streets and junctions, handling motorway entrances, dual carriageways, dog-leg intersections, placement tags, and parallel sidewalks and cycleways. osm2streets is a new effort to produce a cleaned-up street network graph with geometry. It's a Rust library, designed to be integrated with browser apps like iD or native/Java apps like JOSM. The goal is to consolidate community efforts to solve these data transformation problems, and to produce high-detail vector maps and apps for improving lane tagging with immediate visual feedback.\n\nStart using this at https://osm2streets.org","original_language":"eng","persons":["Dustin Carlino"],"tags":["sotm2022","18478","2022","Software Development","OSM","OpenStreetMap"],"view_count":140,"promoted":false,"date":"2022-08-21T10:00:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2026-02-23T20:45:11.632+01:00","length":1688,"duration":1688,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18478-672a387e-6367-55ac-8c40-4bb214aed887.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18478-672a387e-6367-55ac-8c40-4bb214aed887_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18478-672a387e-6367-55ac-8c40-4bb214aed887.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18478-672a387e-6367-55ac-8c40-4bb214aed887.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18478-osm2streets-street-networks-with-detailed-geometry","url":"https://api.media.ccc.de/public/events/672a387e-6367-55ac-8c40-4bb214aed887","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"282251a7-9210-5c72-a811-7653a74ae985","title":"Corporate editing and its impact on network navigability within OpenStreetMap","subtitle":null,"slug":"state-of-the-map-2022-academic-track-18795-corporate-editing-and-its-impact-on-network-navigability-within-openstreetmap","link":"https://2022.stateofthemap.org/sessions/EZPVPB/","description":"Using intrinsic quality indicators we explore how network quality, in terms of its suitability for navigation, varies across areas with relatively high and low corporate editing in OpenSteetMap. Our work shows areas with relatively high rates of corporate editing exhibit not only an overall increase in data quality, but also increased rates at which quality improves.\n\nOSM (OSM) contributors have traditionally lacked explicit monetary incentives for contribution [1].  Since 2016, a handful of large corporations (including Apple, Facebook, Microsoft, and Uber) have increasingly contributed data to OSM. Corporate editors (CEs) represent a distinct community as their editors are compensated and thus their contributions cannot be labeled as ‘volunteered’. Additionally, corporations employ large editing teams and new state-of-the-art editing techniques aided by artificial intelligence, making them capable of editing large swaths of information in relatively short time [2]. Corporate teams are often led by long-time OSM community members themselves, emphasizing the multifaceted nature of a rapidly growing open mapping platform [3]. While there has been some contention about the quality of edits done by CEs, corporations argue their contributions improve existing data [7]. Our study provides a preliminary quantitative evaluation of data quality impacts of corporate edits  on OSM.\n\nWe assess intrinsic data quality across five regions that have high levels of corporate contributions: Dallas-Ft. Worth, Egypt, Jamaica, Thailand, and Singapore. The quality of these regions is compared to that of Denmark, a region which has witnessed relatively less corporate interest, yet possesses a well-mapped OSM presence due to a well developed local mapping community [4]. These evaluations were performed using measures of intrinsic map quality. While the most straightforward evaluation methods involve comparing against extrinsic sources, such as either ground reference information or authoritative data sources; lack of data availability, licensing terms, and costs often render this comparison untenable [5,6,7]. A transferrable, data driven way of assessing quality remains using Intrinsic Quality Indicators (IQIs), a sub-field of OSM analysis which provides a variety of approaches for evaluating intrinsic OSM data quality. We chose to focus on IQIs that apply to networks, and to evaluate IQIs for land-based transportation networks within OSM. We analyzed networks for our specified locations for every other year between 2014 and 2022.\n\nOSM editing archives were processed using R to extract maps of the relative activity of corporate editors [8]. Our list of corporate editors was sourced from OSM’s publicly available list of corporate editors accounts. We extracted entire networks that represented the first day of each year of interest (2014, 2016, 2018, 2020, 2022) from OSM’s historical archives. For the purposes of this study, we extracted all networks where “OSM WAY = Highway”.\n\nWe evaluated several IQIs for our areas of interest.  We focused on completeness of network, both in terms growth over time and in terms of its navigability. We operationalized “completeness for navigability” as an intrinsic measurement by exploring the percentages of networks that possessed attributes necessary for GPS navigation – street names and speed limits. Navigability was assessed and compared across time points using Origin-Destination matrices. By creating a regular matrix across the area and calculating the ratio between a direct route between points, and a route navigated within our network, we calculated a ratio that can be compared across time to evaluate the changing efficiency of the navigable network. Additionally, when building routing networks, we discovered an additional IQI : the presence and qualities of topological islands within our network. That is, areas which are disconnected from the main network due to mapping errors or incompleteness. \n\nAfter mapping these metrics, we analyzed how they correlate with each other and how they change over time. Overall, IQI trends for the road network reveal consistent patterns across all measures and locations. There is a trend towards increasing data quality in terms of gradual increase of network length, completeness in terms of attributes (name, speed limit, and pedestrian access), the increasing efficiency of ODM routing ratios, and the increasing amount of places that have “navigable” attributes. Importantly, we found differences between our control location (Denmark) and our other areas of interest. The primary difference of note is not with regards to the quality of the data, but with respect to the rate at which data quality improves: Denmark’s rate of quality improvement is slower than other locations. The faster rate of quality improvement in the test areas highlights that the data creation and editing activity by corporate editors and other organized editors in these locations are helping narrow gaps in data quality.\n\nWhile this presentation highlights the trends of data quality increase, it does not tease apart the quality assessment of contributions by corporate teams versus other mapping groups. As a crowdsourcing platform, data in OSM is co-produced by repeated editing of data objects by different members of the community [9]. The appearance of CEs in OSM represents the arrival of another community of ‘produsers’ in the OSM ecosystem, and thus a new evolution in its overall trajectory [2,10,11]. Consequently, there is significant interaction between CEs and non-CEs in data co-production in OSM, further reinforcing the idea that OSM is a ‘community of communities’ [11]. Each location has their own patterns regarding editing communities, what they edit, and the sociopolitical and economic ground truth in the real world.  Each of these factors impact the data, and may make comparing editing patterns difficult, especially given the diversity of motivations both with CE communities and within other OSM communities. Hence, we do not try to pry apart the differences in trends between individual countries. Instead, we focus on the overall trend between our test and control locations. With these caveats, we find that the quality of the network has increased in these areas across all tracked metrics at a faster rate than it has in areas with low rates of corporate edits, indicating that corporate editing may have a positive effect on the overall quality of the map.","original_language":"eng","persons":["Corey Dickinson"],"tags":["sotm2022","18795","2022","OSM","OpenStreetMap"],"view_count":40,"promoted":false,"date":"2022-08-21T10:45:00.000+02:00","release_date":"2022-10-11T00:00:00.000+02:00","updated_at":"2025-02-18T08:45:03.683+01:00","length":474,"duration":474,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18795-282251a7-9210-5c72-a811-7653a74ae985.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18795-282251a7-9210-5c72-a811-7653a74ae985_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18795-282251a7-9210-5c72-a811-7653a74ae985.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18795-282251a7-9210-5c72-a811-7653a74ae985.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-18795-corporate-editing-and-its-impact-on-network-navigability-within-openstreetmap","url":"https://api.media.ccc.de/public/events/282251a7-9210-5c72-a811-7653a74ae985","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"1d67a8c1-17e6-5e30-965e-5d72311cd6b7","title":"maplibre-rs: Cross-platform Map Rendering using Rust","subtitle":null,"slug":"sotm2022-18246-0-maplibre-rs-cross-platform-map-rendering-using-rust","link":"https://2022.stateofthemap.org/sessions/VECREV/","description":"Digital maps are ubiquitous tools in our everyday life. In the early 90s, the idea of browsing the world digitally and visiting any place was groundbreaking. The first solution to this problem is known as \"TerraVision\", which was breathtaking. Today, the idea of exploring your surroundings using digital maps has become normal.\n\nBut how do these maps work? In this talk, I want to provide an overview of the foundations of digital mapping solutions. Differences between maps which use vector data and rasterized satellite imaginary will be outlined. Furthermore, a new and open-source map renderer called maplibre-rs will be presented, which is created using Rust and WebGPU.\n\nLast year I had a lot of spare time and decided to kick-start a project which combines different areas of interest: Rust, 3D rendering, Geo data\nThis project was adopted recently by the [MapLibre](https://maplibre.org/) project and is now known as [maplibre-rs](https://github.com/maplibre/maplibre-rs).\n\nThe maplibre-rs library is a proof of concept which showed me the complexity of mapping solutions. It takes a lot of steps until edits from OpenStreetMap contributors are finally rendered in consumer applications. With this task I want to take listeners on a journey from drawing changes in the OpenStreetMap editor all the way until vectors are uploaded to from memory to GPUs.\n\nLike outlined in the abstract, I want to cover multiple topics:\n\n* Foundations of digital maps (How to determine which data should be loaded? What are vector and raster tiles?)\n* Show the technology stack which allows us to design and develop a cross-platform map renderer (Web, Mobile, Desktop)","original_language":"eng","persons":["Max Ammann"],"tags":["sotm2022","18246","2022","Cartography","OSM","OpenStreetMap"],"view_count":258,"promoted":false,"date":"2022-08-19T17:30:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2026-03-28T15:00:07.224+01:00","length":988,"duration":988,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18246-1d67a8c1-17e6-5e30-965e-5d72311cd6b7.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18246-1d67a8c1-17e6-5e30-965e-5d72311cd6b7_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18246-1d67a8c1-17e6-5e30-965e-5d72311cd6b7.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18246-1d67a8c1-17e6-5e30-965e-5d72311cd6b7.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18246-0-maplibre-rs-cross-platform-map-rendering-using-rust","url":"https://api.media.ccc.de/public/events/1d67a8c1-17e6-5e30-965e-5d72311cd6b7","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"86998c4b-2f4f-5fb3-ba2e-4a20e6588852","title":"The cell size issue in OpenStreetMap data quality parameter analyses: an interpolation-based approach","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19674-the-cell-size-issue-in-openstreetmap-data-quality-parameter-analyses-an-interpolation-based-approach","link":"https://2022.stateofthemap.org/sessions/9HBH3X/","description":"The quality of OSM data is dependent on many different factors and is quite heterogeneous. Therefore, in both intrinsic and extrinsic quality parameter analyses, a common practice is subdividing the study areas into subareas. In this paper, we worked on a method for obtaining the optimal grid cell size for OSM data quality analysis. Furthermore, we proposed that if the quality is homogeneous in a region, it can be estimated using an IDW interpolation. . In this summary, we have done a preliminary analysis for a Brazilian city, Curitiba, with about 28,000 points of known accuracy.\n\nKnowing the quality of a given geospatial data allows measuring how much its use can be viable in specific applications and assist in decision making. ISO 19157 [1] established that the geospatial data quality indicators are positional accuracy, temporal accuracy, thematic accuracy, logical consistency, and completeness. These measures are represented by values that summarize the condition of a product as a whole. These values tend to be homogeneous throughout the evaluated area in traditional mapping. In contrast, in VGI, data quality can be affected by several conditions related to editing history, contribution period, and contributor profiles [8,9]. Given the mentioned aspects, data quality in VGI platforms tends to be heterogeneous, i.e., the results may show significant discrepancies according to the area assessed or even within the same region.\n\nGiven the heterogeneity issues described, several researchers around the world have performed the quality assessment of these types of information based on the principle of subdividing the study area into cells [2,3,4,5,6,7]. Such a procedure has been used in extrinsic quality assessment processes based on ISO 19157 indicators or intrinsic parameters associated with the characteristics of the contributions and contributors. Given the results obtained, the representation of the quality of the data from sub-areas makes it possible to obtain analyses regarding the existence of patterns and establish relationships with other agents and their predominance. The discretization of space into rectangular or hexagonal grids is central to this type of analysis.\n\nThe subdivision can occur regularly or irregularly. The units with irregular dimension cells allow us to perform analyses accepting other features or spatial phenomena that define these dimensions (e.g., neighbourhood border, a river or a railway track, areas with different population densities, and the dichotomy between rural and urban areas). However, these methods make operations difficult because they demand that the area value weigh the values; and the spatial analysis considering the neighbourhood is more complex. Units with regular-sized cells solve these two limitations. However, the problem of the grid of cells not conforming to spatial phenomena or features reappears. In order to conform to them, it is necessary to determine the optimal size of the cells.\n\nHowever, one issue remains little discussed: how to determine the size of such cells. Using too large a cell would treat unequal areas equally. On the other hand, using too small a cell and the increased computational cost of the process, ultimately, the ability to generalize the results is lost. Therefore, in this work, we seek to develop an interactive approach for determining the grid cell size calculation, initially using points of known positional accuracy. The hypothesis here is that when the analyzed subarea is of optimal size, one can interpolate the error within the cell via an IDW and generate minimal residuals at the control points. Furthermore, by consecutively subdividing the grid, the mean squared error versus cell size curve will approach stability, thus revealing the optimal size for a given region.\n\nInverse distance weighted interpolation (IDW) calculates cell values using sample point sets. This method considers that the higher weights in the interpolation should be due to the proximity of the unknown value point. Thus, if we had a homogeneous behaviour of the quality parameter in an individual area, by interpolation, we could estimate the quality of the points where this value was unknown.\n\nThe methodological procedures developed using python in the QGIS environment are:\n\n1. For the study area, points of known positional accuracy are chosen (in our case, intersections of the road system), from which a random subset of 10% is separated as a control set.\n\n2. Definition of a first grid.\n\n3. The points are used for interpolation within each cell by the IDW method. The Root-mean-square deviation (RMSE) is calculated using the control points for each cell and the average of the RMSEs for the entire area;\n\n4. Definition of a second grid with half the resolution of the first grid;\n\n5. Repeat the process described in item 3 for the second grid;\n\n6. Calculate the differences between the average error values of the second grid and the first grid and check their significance ;\n\n7. Repetition of the process described in case there are still values indicated as significant.\n\nIn a first analysis, we did a preliminary study for a Brazilian city, Curitiba, with about 28 thousand points of known accuracy. We separated 2.8 thousand control points, and the city was divided into 8 km to 250 m cells. From the preliminary study performed, it was noted that the method show promise in obtaining the necessary analyses to identify the aspects proposed in this work. Furthermore, it was noticed that, as the cell size decreased, the results tended to be more constant, which corroborates the hypothesis of this relationship with data quality. The next steps are to continue the analyses, starting with the verifications and the representation of the magnitude of the differences between different cell sizes.\n\nAlthough it is a method that still has a relatively high computational cost to be realized, the results are exciting and can be optimized. It is assumed that if it is possible to identify the minimum cell size in which it is possible to estimate the quality of the features, this will help in decision making regarding the incorporation of procedures in different áreas. This method may need even smaller clippings in regions with very heterogeneous characteristics concerning their surroundings (e.g., slums). It is an initial approach to resolve with data a fundamental issue arising from the lack of knowledge of the granularity of discrepancies for each study area.","original_language":"eng","persons":["Silvana Philippi Camboim"],"tags":["sotm2022","19674","2022","OSM","OpenStreetMap"],"view_count":18,"promoted":false,"date":"2022-08-21T10:30:00.000+02:00","release_date":"2022-10-11T00:00:00.000+02:00","updated_at":"2025-07-04T23:15:05.964+02:00","length":414,"duration":414,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19674-86998c4b-2f4f-5fb3-ba2e-4a20e6588852.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19674-86998c4b-2f4f-5fb3-ba2e-4a20e6588852_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19674-86998c4b-2f4f-5fb3-ba2e-4a20e6588852.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19674-86998c4b-2f4f-5fb3-ba2e-4a20e6588852.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19674-the-cell-size-issue-in-openstreetmap-data-quality-parameter-analyses-an-interpolation-based-approach","url":"https://api.media.ccc.de/public/events/86998c4b-2f4f-5fb3-ba2e-4a20e6588852","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"d60cd076-b508-532a-abb8-ebde8be9e473","title":"Admin Boundary Conflation Tool","subtitle":null,"slug":"sotm2022-18467-admin-boundary-conflation-tool","link":"https://2022.stateofthemap.org/sessions/HGFY7Z/","description":"Boundary conflation is a sensitive and difficult problem to solve. When administrative boundary data is available from authoritative sources for OSM, it is imperative that we have the ability to analyze boundaries for import and deduce if conflation with OSM ways can be done hopefully in a semi-automatic fashion. \"Admin Boundary Conflation\" is a special-purpose tool made for the purpose and this talk will introduce the workings of the tool to the audience along with the various output statistics available during the process. Currently, the tool is utilized for reporting geometric area differences of 0.01% in the 99th percentile of 5000 municipalities in Serbia within 20 minutes.","original_language":"eng","persons":["Branko Kokanovic"],"tags":["sotm2022","18467","2022","Software Development","OSM","OpenStreetMap"],"view_count":71,"promoted":false,"date":"2022-08-19T12:30:00.000+02:00","release_date":"2022-09-20T00:00:00.000+02:00","updated_at":"2025-01-29T21:30:08.561+01:00","length":1234,"duration":1234,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18467-d60cd076-b508-532a-abb8-ebde8be9e473.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18467-d60cd076-b508-532a-abb8-ebde8be9e473_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18467-d60cd076-b508-532a-abb8-ebde8be9e473.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18467-d60cd076-b508-532a-abb8-ebde8be9e473.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18467-admin-boundary-conflation-tool","url":"https://api.media.ccc.de/public/events/d60cd076-b508-532a-abb8-ebde8be9e473","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"6000c064-c132-590b-be0b-88f00b07ec75","title":"State of Independence","subtitle":null,"slug":"sotm2022-18484-state-of-independence","link":"https://2022.stateofthemap.org/sessions/URUJH8/","description":"OpenStreetMap is the human-made map of the world. But how can one tiny human still make a difference in a project used by megacorps and crucial to millions of app and website users every day? How does OSM retain its individualism in a world that wants it to be consistent, orderly and predictable? Is it game over for the experimental, iconoclastic, independent map? Richard Fairhurst offers a challenging but upbeat look at the changing landscape for the OpenStreetMap mapper, user and developer.\n\n2004: A crazy hobby project. One street mapped. One small mailing list.\n2013: Edited by thousands every day. Beloved by hobbyists. Trialled by a few adventurous companies.\n2022: The world's map. Worth billions. Used by everyone.\n\nWhat happened?\n\nIs there still a place for the individual in OSM, as mapper, user, or developer? How do you build with OSM when your competitors have free money on tap? Can OSM retain its iconoclasm and individualism when the Silicon Valley behemoths are involved?\n\nRichard Fairhurst is still as idealistic as in 2004 (but maybe a little calmer) and thinks that corporate OSM and independent OSM can co-exist, and better still, benefit from each other. In a wide-ranging, rollicking and occasionally factually accurate talk, he'll look at the OpenStreetMap economy in 2022 and how individuals can still make a difference. Expect: the secretive Kindred of the Kibbo Kift, situationists, books about pubs, bouncing phones.","original_language":"eng","persons":["Richard Fairhurst"],"tags":["sotm2022","18484","2022","Community and Foundation","OSM","OpenStreetMap"],"view_count":186,"promoted":false,"date":"2022-08-19T10:30:00.000+02:00","release_date":"2022-09-18T00:00:00.000+02:00","updated_at":"2025-10-29T16:45:05.691+01:00","length":1712,"duration":1712,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18484-6000c064-c132-590b-be0b-88f00b07ec75.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18484-6000c064-c132-590b-be0b-88f00b07ec75_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18484-6000c064-c132-590b-be0b-88f00b07ec75.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18484-6000c064-c132-590b-be0b-88f00b07ec75.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18484-state-of-independence","url":"https://api.media.ccc.de/public/events/6000c064-c132-590b-be0b-88f00b07ec75","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"9e30959e-20c2-573d-b2a2-0ad93b513154","title":"What you map is not always what you get","subtitle":null,"slug":"sotm2022-18510-what-you-map-is-not-always-what-you-get","link":"https://2022.stateofthemap.org/sessions/NAF9EN/","description":"OSM has an unrestricted tagging model. Mappers can invent and use any tags.\nWhile this is part of OSM's success story, it has lead to a database where\nthe globe is described in ever greater detail. In this talk we want to\nexplore how users of OSM data handle a tagging model with so few constraints.\nRichard, the owner of cycle.travel, and Sarah, maintainer of Nominatim, team\nup to share their experiences of a decade of working with, and occasionally\nfighting against, OSM's ever evolving tagging schema.\n\nThe OSM tagging model has significantly evolved since the first streets of\nLondon were put into the database. Not only are more and more different\nobjects in the database, we also capture more of their properties and want\nto model ever finer nuances. This clashes with our goal to have one\ndatabase for the entire planet. The closer we look the more differences\nbetween different regions there are. And to capture those the tagging has to\nbecome even more complex.\n\nUsing the examples of the cycle tour planning site cycle.travel and the\nsearch engine Nominatim, we explore the evolution of OSM tagging from\na data user's point of view. Looking at questions like\n\n* where do data users get their information about tagging\n* what kind of tagging can be practically used in software\n* how to handle local defaults and assumptions\n* how should OSM ideally document its tagging","original_language":"eng","persons":["Sarah Hoffmann","Richard Fairhurst"],"tags":["sotm2022","18510","2022","Mapping","OSM","OpenStreetMap"],"view_count":273,"promoted":false,"date":"2022-08-19T16:30:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2026-03-30T15:30:07.262+02:00","length":3426,"duration":3426,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18510-9e30959e-20c2-573d-b2a2-0ad93b513154.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18510-9e30959e-20c2-573d-b2a2-0ad93b513154_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18510-9e30959e-20c2-573d-b2a2-0ad93b513154.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18510-9e30959e-20c2-573d-b2a2-0ad93b513154.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18510-what-you-map-is-not-always-what-you-get","url":"https://api.media.ccc.de/public/events/9e30959e-20c2-573d-b2a2-0ad93b513154","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"111c7138-d81d-5358-a6db-c547a30feb30","title":"Public Domain Map: Crowdsourcing the Future of Government Data","subtitle":null,"slug":"sotm2022-18526-public-domain-map-crowdsourcing-the-future-of-government-data","link":"https://2022.stateofthemap.org/sessions/CFVMU7/","description":"It’s easy to see how OpenStreetMap could be leveraged to improve the completeness and freshness of government geospatial datasets. So why aren’t all governments using OpenStreetMap? In the US, the ODbL license has prevented government agencies from using the data. Public Domain Map aims to resolve this (and other challenges) by providing a workflow that allows contributions to be used in both OpenStreetMap and public domain US Government databases. We will share the journey of Public Domain Map, and importantly, how the project is bringing together US federal agencies and open source contributors to meet this goal.","original_language":"eng","persons":["Jess Beutler","James McAndrew"],"tags":["sotm2022","18526","2022","Software Development","OSM","OpenStreetMap"],"view_count":56,"promoted":false,"date":"2022-08-20T17:30:00.000+02:00","release_date":"2022-10-15T00:00:00.000+02:00","updated_at":"2025-03-20T14:45:03.380+01:00","length":1779,"duration":1779,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18526-111c7138-d81d-5358-a6db-c547a30feb30.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18526-111c7138-d81d-5358-a6db-c547a30feb30_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18526-111c7138-d81d-5358-a6db-c547a30feb30.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18526-111c7138-d81d-5358-a6db-c547a30feb30.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18526-public-domain-map-crowdsourcing-the-future-of-government-data","url":"https://api.media.ccc.de/public/events/111c7138-d81d-5358-a6db-c547a30feb30","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"04df6bda-89f2-595a-a177-1bcc383087b3","title":"Women Leadership in Mapping Riverside Communities in the Amazon Forest Using OSM","subtitle":null,"slug":"sotm2022-18402-women-leadership-in-mapping-riverside-communities-in-the-amazon-forest-using-osm","link":"https://2022.stateofthemap.org/sessions/ZUEAUV/","description":"The Amazon Forest, its traditional peoples, and riverside communities represent an immense challenge for official cartography, due to scale and extension factors. This presentation aims to show the experiences of the Unificar Ações e Informações Geoespaciais (UAIGeo) chapter of the Brazilian YouthMappers in mapping riverside communities in the Amazon rainforest region, focusing on the city of Tefé and its islands. Due to its female leadership role and the goals of empowering young women in geospatial and technical skills, the presentation emphasizes the importance of engaging and encouraging female students to link mapping and female empowerment.\n\nIn Brazil, it is still an immense challenge to produce official cartography on the regional and local scale due to the extension of the territory, the number of municipalities (5,568 plus the Federal District), and the expensive cost of producing the mapping. Such factors are more significant in the Northern Region of the Country, where an important part of the Brazilian Amazon Forest and many traditional peoples and riverside communities are located. Many of the riverside communities do not have records in documents about their ancestral knowledge, cultures, territories, etc. Their knowledge and stories are transmitted orally and, despite the importance of this legitimate source of data, an important amount of information can be lost over time [1] The distances between riverside communities, in addition to the small geographic space the built infrastructure occupies, make them invisible in many official mapping. The riverside buildings are not visible in official mappings and have cultivated areas between 0,2 to 3 hectares. In this context, collaborative mapping through open data platforms is one cheaper possibility to mitigate the lack of maps in Brazilian municipalities. The OpenStreetMap (OSM) platform emerges as promising because it has wide territorial coverage of high-resolution images, which makes it possible to view the communities on the local cartographic scale.\nThis presentation has the aim to show the experiences of the Brazilian YouthMappers’ chapter Unificar Ações e Informações Geoespaciais (UAIGeo), in partner with Centro de Estudos Superiores de Tefé-Universidade do Estado do Amazonas - CEST/UEA, within mapping riverside communities in the Amazon rainforest. In its initial phase, the project received support from the Everywhere She Maps program, due to its women leadership role and goals of empowering young women in geospatial and technical skills. Globally, on average 35% of female students entering the university choose fields in science, technology, engineering, and math - STEM [2]. The Global Gender Gap Report 2020 shows that Brazilian women represent just 18% of employees in technological jobs, a percentage below the global average [3]. Additionally, according to a study that has analyzed OSM users and the manifested gender, by 2019 female participation reached 13% [4]. For this reason, this presentation emphasizes how important it is to engage and incentivize women’s participation both from external and local communities. Gender relations are important components of key forest-related issues, such as climate change and the differences in opportunities facing women in these contexts. However, there is little literature on forest and gender in Latin America, particularly in the Amazon Forest [5] The chapter have been coordinating a series of mapping activities focused on the municipality of Tefé and verified that volunteers have mapped so far 11.082 buildings in the Humanitarian OpenStreetMap Tasking Manager platform since last year. Together with local collaborators from CEST/UEA, chapter members validated some names of communities on one of Tefé’s islands. Chapter members carried out fieldwork in Tefé to know the local reality and some places that they had mapped. They also conducted studies in São Luís do Macari, a community that is located on an island in the middle of the Solimões River. Results of remote and fieldwork activities will be presented while at the same time emphasizing the importance of engaging and encouraging female students to link mapping and female empowerment.\n\n* [1] De Magalhães Lima, D.; Ferreira Alencar, E. (2001). A lembrança da História: memória social, ambiente e identidade na várzea do Médio Solimões. Lusotopie, v. 8, n. 1, p. 27-48, (Accessed April 18, 2022).\n* [2] UNESCO. (2019). Descifrar el código: La educación de las niñas y las mujeres en ciencias, tecnología, ingeniería y matemáticas (STEM)—UNESCO Digital Library. https://unesdoc.unesco.org/ark:/48223/pf0000366649 (Accessed April 18, 2022)\n* [3] GGGR. Global Gender Gap Report 2020. World Economic Forum, Geneva. 2020. Available in: https://www3.weforum.org/docs/WEF_GGGR_2020.pdf (Accessed Feb. 23, 2022)\n* [4] Gardner, Z., Mooney, P., De Sabbata, S., \u0026 Dowthwaite, L. (2020). Quantifying gendered participation in OpenStreetMap: Responding to theories of female (under) representation in crowdsourced mapping. GeoJournal, 85(6), 1603–1620. https://doi.org/10.1007/s10708-019-10035-z\n* [5] Chmink, M., \u0026 Arteaga Gomez-Garcia, M. (2016). Embaixo do dossel: Gênero e florestas na Amazônia. https://doi.org/10.17528/cifor/006139","original_language":"eng","persons":["Ana Luísa Teixeira","Silvia Elena Ventorini","Natalia da Silveira Arruda"],"tags":["sotm2022","18402","2022","User Experiences","OSM","OpenStreetMap"],"view_count":24,"promoted":false,"date":"2022-08-20T15:00:00.000+02:00","release_date":"2022-10-01T00:00:00.000+02:00","updated_at":"2024-05-10T16:00:03.451+02:00","length":1432,"duration":1432,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18402-04df6bda-89f2-595a-a177-1bcc383087b3.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18402-04df6bda-89f2-595a-a177-1bcc383087b3_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18402-04df6bda-89f2-595a-a177-1bcc383087b3.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18402-04df6bda-89f2-595a-a177-1bcc383087b3.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18402-women-leadership-in-mapping-riverside-communities-in-the-amazon-forest-using-osm","url":"https://api.media.ccc.de/public/events/04df6bda-89f2-595a-a177-1bcc383087b3","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"0c025aaa-29c7-53fa-84c8-b1a4bd8597ca","title":"Investigating the capability of UAV imagery in AI-assisted mapping of Refugee Camps in East Africa","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19438-investigating-the-capability-of-uav-imagery-in-ai-assisted-mapping-of-refugee-camps-in-east-africa","link":"https://2022.stateofthemap.org/sessions/FRJXCQ/","description":"This pilot project is connected to a larger initiative to open-source the assisted mapping platform for Humanitarian OpenStreetMap (HOTOSM) based on Very High Resolution (VHR) drone imagery. The study test and evaluate multiple U-Net based architectures on building segmentation of Refugee Camps in East Africa.\n\nIntroduction\n\nRefugee camps and informal settlements provide accomodation to some of\nthe most vulnerable population, the majority of which are located in Sub-\nSaharan East Africa (UNHCR, 2016). Many of these settlements often lack\nup-to-date maps of which we take for granted in developed settlements. Hav-\ning up-to-date maps are important for assisting administration tasks such as\npopulation estimates and infrastructure development in data impoverished\nenvironments, and thereby encourages economic productivity (Herfort et al.,\n2021). The data inequality between the developed and developing countries\nare often resulted from a lack of commercial interest, especially with the\nrecent trend of corporate OSM mappers (Anderson et al., 2019, Veselovsky\net al., 2021). Such disparity can be reduced using assisted mapping tech-\nnology. To extract geospatial and imagery characteristics of dense urban\nenviornments, a combination of VHR satellite imagery and Machine Learn-\ning (ML) are commonly used (Taubenböck et al., 2018). Classical ML based\nmethods that exploit the textual (e.g. GLCM), spectral, and morphological\ncharacteristics of VHR imagery are based on the principles of Computer\nVision (CV). Although many have shown promising results in satellite VHR\n(1m to 5m resolution) scenarios such as differentiating slum and non-slum\n(Kuffer et al., 2016 \u0026 Wurm et al., 2021), in VHR drone imagery (5cm to\n10cm resolution) however, results might suffer from noise caused by environ-\nment and drone-based specific problems such as motion artefacts and litter.\nRecent advances in CV based Deep Learning might be able to address these\nissues (Chen et al., 2021 \u0026 Carrivick et al., 2016).\n\nPurpose of the study\n\nThe study is connected to a larger initiative to open-source the assisted\nmapping platform in the current Humanitarian OpenStreetMap (HOTOSM)\necosystem. This study is a pilot-project to investigate the capabilities of\napplying semantic segmentation using community open-sourced VHR drone\nimagery collected by the partner organisation OpenAerialMap. The study\naims to rigourosly assess the various components and inputs that would\ncontribute to the ML based mapping system, and to produce a detailed\nevaluation on class-based accuracy assessment (Congalton \u0026 Green, 2019).\nThis pilot study focuses on 2 camps in East Africa, where data availability\nand the geography of the camps are within a similar savannah ecosystem.\nThis enables highly-detailed method testing and analysis of transferability\nof the results between the two camps.\n\nData and Methodology\n\nThe first camp is located in Dzaleka, Dowa, Malawi, which is sub-divided\ninto the Dzaleka North and Dzaleka main camp. The Dzaleka camps are\nhome to around 40,000 refugees mainly coming from the African Great Lakes\nregion. The Dzaleka North camp is characterised by a newer, spatially well-\nplanned metal-sheeted roofs, while the southern main camp is characterised\nby complex, dense mud-walled building with stone-lined thatched-roofs (UN-\nHCR, 2014). The second camp, the Kalobeyei settlement is part of the sub-\ncamp of Kakuma, located in the rural county of Turkana, North-West Kenya.\nThe Kalobeyei settlement was home to approximately 34,849 refugees as of\n2019. This camp is significantly more spacious and is characterised by spa-\ntiall well-planned metal-sheeted roofs (UNHCR \u0026 DANIDA, 2019). VHR\ndrone imageries were provided for both camps and vector labels produced\nby HOTOSM volunteers were provided for the Dzaleka and Dzaleka North\ncamp.\n\nSince CV based Deep Learning is very dependent on the quality of the\nlabelled referenced data, especially when performing pixel-based semantic\nsegmentaion, it is of crucial importance that care is taken when producing\nhighly accurate labels that ensure sucessful training (Ng A., 2018). A large\nquanitiy of available labels did not have such a task in mind, imperfection\nin labelling around existing drone artefacts could cause the trained model\nto misclassify such pixels. In order to train a model which performs well\non drone imagery, the motion artefact will be a signficant feature for the\nmodel to learn.he combination of data availability have allow a unique set of\nresearch questions concerning the input data quality and experiment setup\nto surface. Therefore, to test out U-Net and a few variation of the U-Net\nperformance, an additional set of label data was created in order to supple-\nment the imperfection in the labelled data of the Dzaleka camps. Initially,\nthe models will be trained on the pixel-perfect and less complex Kalobeyei\ndataset, this will be then be followed by introducing the Dzaleka datasets\nof higher complexity. A comparison of baseline performance between the U-\nNet variations (Ronneberger et al., 2015) and the Open-Cities-AI-Challenge\n(OCC) winning model is conducted. The baseline experiement aims to keep\nthe hyperparameters (e.g. optimiser, learning rate, weight decay etc.) con-\nstant to obtain an objective view of the architectual responses on the same\ndataset setup. This will provide a clear picture of the feasibility and how to\ntake this project further, so that further resources could be justified to scale\nfuture experiments.\n\nFindings and Discussion\n\nInitial baseline experiments on the Kalobeyei dataset and Kalobeyei with\nthe Dzaleka(s) seem to suggest limited transferability from the OCC model.\nThis suggests that the OCC model is perhaps over-generalised to the compe-\ntition test dataset. Despite achieving very high confidence on metal-sheeted\nrooftops, it does not detect any of the more complicated thatched roofs com-\nmon in the Dzaleka camp. The OCC model also struggle with\nsome of the more obscure drone motion artefacts occuring at the edge of the\nimagery in the Kalobeyei camp. Meanwhile, the Precision and Recall statis-\ntics favour other variations or further transfer training on the OCC model,\nand the EfficientNet B1 header U-Net pretrained with ImageNet weights.\nHowever validation loss suggests there might be little room for improvement\nin the further transfer training of the OCC model.\n\nPrecision and Recall have both reached above 0.7 in most experiments,\nwhich outline the general capability of the strategies used. However there\nare still significant variations among different architectures and setups. The\nnext step is to perform systemic fine-tuning to increase the confidence level\nof the appropriate architectures.","original_language":"eng","persons":["Christopher Chan"],"tags":["sotm2022","19438","2022","OSM","OpenStreetMap"],"view_count":44,"promoted":false,"date":"2022-08-21T10:40:00.000+02:00","release_date":"2022-10-11T00:00:00.000+02:00","updated_at":"2026-01-14T10:30:12.201+01:00","length":356,"duration":356,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19438-0c025aaa-29c7-53fa-84c8-b1a4bd8597ca.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19438-0c025aaa-29c7-53fa-84c8-b1a4bd8597ca_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19438-0c025aaa-29c7-53fa-84c8-b1a4bd8597ca.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19438-0c025aaa-29c7-53fa-84c8-b1a4bd8597ca.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19438-investigating-the-capability-of-uav-imagery-in-ai-assisted-mapping-of-refugee-camps-in-east-africa","url":"https://api.media.ccc.de/public/events/0c025aaa-29c7-53fa-84c8-b1a4bd8597ca","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"df213ca2-5675-5b51-b419-001ca869ec38","title":"Returning the favor - Leveraging quality insights of OpenStreetMap-based land-use/land-cover multi-label modeling to the community","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19407-returning-the-favor-leveraging-quality-insights-of-openstreetmap-based-land-use-land-cover-multi-label-modeling-to-the-community","link":"https://2022.stateofthemap.org/sessions/EKEZ7R/","description":"The fitness of OSM for multi-label classification is proven. A workflow to enhance OSM-based multi-labels using machine learning is established. The results are provided to the OSM community via the HOT Tasking Manager.\n\n# Introduction\n\nLand-use and land-cover (LULC) information in OSM is a challenging topic. On the one hand, this information provides the background for all other data rendered on the central map and is used by applications like https://osmlanduse.org. On the other hand, this information has a difficult position within the OSM ecosystem. LULC information can be quite cumbersome or even difficult to map e.g. due to natural ambiguity. The growing tagging scheme provides a collection of sometimes ambiguous or overlapping tag definitions that are not fully compatible with any official LULC legend definition [1]. Furthermore, the data is highly shaped by national preferences and imports.\n\nThis diversity of the LULC data in OSM is a fundamental principle of OSM that enabled the success of the project. Yet, this can create considerable usage barriers or at least caveats for data users unfamiliar with the projects' ecosystem. The remote sensing community for instance has started to use OSM LULC information as labels in their classification models. Frequently, OSM LULC data has thereby been taken at face value without critical reflection. And, while the quality and fitness for purpose of OSM data has been proven in many cases (e.g. [2,3]) these analyses have also unveiled quality variations e.g. between rural and urban regions. The quality of OSM therefore can be assumed to be generally high, but remains unknown for a specific use-case.\n\nThe proposed work first assesses the impact of these challenges on a use-case of multi-label remote sensing (RS) image classification and then provides a machine learning (ML) based workflow to overcome and finally mitigate them. Multi-labels are a type of image classification where a satellite image is labeled with multiple containing LULC classes. In the presented study these labels are extracted from OSM and used to train the ML algorithm.\n\n\n# Methods and Results\n\nThe fitness for purpose of OSM for multi-label RS image classification was tested on a Sentinel 2 scene with a resolution of 10m and four bands in south west Germany recorded in June 2021. The area was chosen for its estimated high completeness and low amount of imported data. OSM data was grouped by its tags into the four LULC classes 'forests', 'agricultural areas', 'build-up area' and 'water bodies'. 18 tags that could unequivocally be mapped to these classes were used and small elements below the image resolution or the classes minimal mapping unit were filtered. The chosen scene was then tiled into a 1.22 x 1.22 km grid of 8100 image patches. Zero to four labels were assigned to each patch, based on the OSM LULC elements therein. Evaluation was performed manually on 910 random patches, of which 80% had a correct OSM-based multi-label, thereby proving the assumed high completeness and quality in the region.\n\nThe proposed workflow provides a method to enhance this OSM-based RS image multi-label classification and extend it to areas of lower OSM quality and completeness using ML (specifically deep learning (DL)). The main obstacle for ML and especially DL is the required amount of labeled training data. Volunteered geographic information (VGI) like OSM offers a potential solution to this challenge by providing an overabundance of LULC information that is suitable for this purpose if data quality is sufficiently high. The workflow uses the multi-label information extracted from OSM for training and then detects discrepancies between its predictions and OSM.\n\nUsing this information and pinpointing the exact location of error within the patches provides valuable OSM data quality information. Apart from facilitating a fast quality estimation for large areas, the workflow can make its findings automatically available to the OSM community in a feedback loop using the HOT Tasking Manager framework. Thereby the valuable service by the OSM community of providing large amounts of free and generally high quality training data is recognised in the form of quality feedback including mapping hints to the OSM community. \n\nThe five workflow stages are: 1) RS data collection and preprocessing, 2) OSM data collection and preprocessing, 3) LULC multi-label modeling, 4) OSM data issue flagging and 5) the community feedback loop. While each step is an atomic use case and application, the combination of all four steps creates a tool that is useful for the RS and the OSM community likewise. The tool is openly available at https://gitlab.gistools.geog.uni-heidelberg.de/giscience/ideal-vgi/osm-multitag under the GNU Affero General Public License v3 including example datasets. Manual input was kept as low as possible while enabling the 'human in the loop' to take full control over all input and output.\n\nThe workflow extracts multi-label training data in stages 1) and 2) as described. Stage 3) then trains a DL model to predict multi-labels using solely RS imagery. For demonstration, the model was trained on the described Sentinel 2 scene in Germany. The models' performance was validated on the manually labeled 910 patches where it outperformed OSM in terms of multi-label accuracy by 7%. When additional errors were manually introduced to the training labels to simulate areas of lower OSM quality or completeness, the model maintained an overall prediction accuracy above the noisy training labels. Alternatively, in cases where OSM LULC multi-label accuracy is suspected to be low, pretrained models from comparable regions with higher OSM data quality can be applied, making the workflow widely applicable.\n\nAny patches where the models' multi-label prediction contradicts the OSM-based multi-label are then detected in stage 4). Multi-labels can be incorrect if either a label is missing (omission), meaning data is missing in OSM, or if a label is wrongly assigned, meaning OSM data is falsely mapped within the tile. The data error type and location within the patch is then extracted using explainable AI [4].\n\nThe final stage 5) uses these localised potential OSM data errors to create HOT Tasking Manager projects via the public API. These projects provide additional correction hints. Yet, no automatic editing takes place. The community is kept in full control of all mapping actions as a 'human in the loop'.\n\n# Discussion\n\nThe high quality but diverse nature of OSM has been proven for the use-case of multi-label RS image classification. The proposed tool provides an automated OSM multi-label extraction, modeling and verification procedure including a return of results to the OSM community. A major challenge of the approach is the tiled view on the data. If OSM assigns correct multi-labels to a patch, more fine grained data issues will not be detected. Yet, this approach allows large scale data assessments, before the topic of more detailed data improvement is tackled. It also allows to run repeated OSM LULC quality and completeness estimations for large areas over time.\n\nAnother major benefit is the usage of local OSM data for modeling, thus making regionalised models the standard procedure. This is required for OSM LULC information as regional data structures and communities exist, that need to be preserved. The model can lead to regional homogenisation and data cohesion within these regional communities.","original_language":"eng","persons":["Moritz Schott"],"tags":["sotm2022","19407","2022","OSM","OpenStreetMap"],"view_count":41,"promoted":false,"date":"2022-08-21T10:50:00.000+02:00","release_date":"2022-10-11T00:00:00.000+02:00","updated_at":"2025-04-29T05:15:03.000+02:00","length":337,"duration":337,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19407-df213ca2-5675-5b51-b419-001ca869ec38.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19407-df213ca2-5675-5b51-b419-001ca869ec38_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19407-df213ca2-5675-5b51-b419-001ca869ec38.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19407-df213ca2-5675-5b51-b419-001ca869ec38.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19407-returning-the-favor-leveraging-quality-insights-of-openstreetmap-based-land-use-land-cover-multi-label-modeling-to-the-community","url":"https://api.media.ccc.de/public/events/df213ca2-5675-5b51-b419-001ca869ec38","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"81c580f7-30f4-508f-8456-1b4d7412c184","title":"Routing not only for Prams","subtitle":null,"slug":"sotm2022-18470-routing-not-only-for-prams","link":"https://2022.stateofthemap.org/sessions/LKZYJ7/","description":"What must be mapped to make routing for prams and wheelchairs practical? Three years ago, the local meet-up in Dortmund, Germany, started a campaign to make step-free routing available for the general public.\n\nThe lessons learned mean that such routing is possible, but there is a lot missing to map - both in Dortmund and in all other parts of the world.\n\nMap the essential where fellow mappers are sparse. And codify the full ground truth where the passion allows it. I hope to encourage mappers for the quest to get their neighbourhood ready for wheelchairs, prams and all the other pedestrians!\n\nRouting for pedestrians is a much broader challenge than the well-known car routing.\nCars all over the world are mostly uniform, but pedestrians vary widely in their capabilities.\nThis means that a lot of details that a sportive person might not even notice can be literally a roadblocker for people with prams, for wheelchair users or simply lesser-abled people with not enough strength for a complete stairway.\n\nBecoming a father has been a good opportunity to check in practice what is and what is not feasible for a pedestrian with a pushed vehicle. It turns out that the first step is to get aware of the various kinds of obstacles that get in the way. Beside the obvious steps and kerbs, there are impassable surfaces, too narrow or too steep sections. Or simply sidewalks missing completely on the ground.\n\nAs of now, OpenStreetMap data does not even suffice to figure out where one or both sidewalks actually exist. This puts into perspective the discussions about how to map best details of both detached ways and sidewalks. A couple of tagging approaches are compared to allow educated guesses which level of detail will allow for good results rather in weeks and months than in years or decades. I even dare to give suggestions what tagging practices we should additionally adopt to be able to map faster.\n\nThe background of this talk is an initiative from the Dortmund meet-up: For the large event Kirchentag 2019, we mapped at least the city center sufficiently well for wheelchair mapping. The whole city with its 1500 km streets has turned out to be simply too much. Given that a city with a local meet-up is in a relatively good position to be mapped, it was no surprise that also elsewhere the data is simply not yet good enough for wheelchair routing. The hope is that simple suggestions what helps is getting more traction than a sophisticated mapping hierarchy.","original_language":"eng","persons":["Roland Olbricht"],"tags":["sotm2022","18470","2022","Mapping","OSM","OpenStreetMap"],"view_count":38,"promoted":false,"date":"2022-08-21T11:30:00.000+02:00","release_date":"2022-10-01T00:00:00.000+02:00","updated_at":"2025-06-23T16:45:04.068+02:00","length":1691,"duration":1691,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18470-81c580f7-30f4-508f-8456-1b4d7412c184.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18470-81c580f7-30f4-508f-8456-1b4d7412c184_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18470-81c580f7-30f4-508f-8456-1b4d7412c184.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18470-81c580f7-30f4-508f-8456-1b4d7412c184.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18470-routing-not-only-for-prams","url":"https://api.media.ccc.de/public/events/81c580f7-30f4-508f-8456-1b4d7412c184","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"1e03a43d-5a75-50f7-b4db-3d4ca0d34a91","title":"Mapping crises, communities and capitalism on OpenStreetMap: situating humanitarian mapping in the (open source) mapping supply chain","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19572-mapping-crises-communities-and-capitalism-on-openstreetmap-situating-humanitarian-mapping-in-the-open-source-mapping-supply-chain","link":"https://2022.stateofthemap.org/sessions/NWB9QF/","description":"This proposal expands an understanding of humanitarian mapping from an ethnographic perspective, seeking to understand the complex mechanics behind this confluence of humanitarianism, technology, and crowdsourced labor. It seeks to scaffold a notion of the “open source mapping supply chain”, situating both humanitarian mapping and OpenStreetMap itself within a larger ecosystem of commercial, humanitarian, open source, government, and other actors in developing geospatial-related technologies.\n\nThis presentation presents a selection of a MA dissertation project, pursued over the course of more than 1.5 years of immersive fieldwork on OpenStreetMap. This presentation will focus on humanitarian mapping through qualitative study, seeking to expand an understanding of humanitarian mapping (particularly that which has emerged from mappers associated with the Humanitarian OpenStreetMap Team - also known as HOT) through the use of ethnographic tools, seeking to understand the complex mechanics behind this confluence of humanitarianism, technology, and crowdsourced labor, asking how and why people contribute to open-source platforms like OSM, and what role humanitarian mapping plays within the wider ecosystem of geospatial and mapping technologies. Ultimately however, it seeks to scaffold a notion of the “open source mapping supply chain”, situating both humanitarian mapping and OpenStreetMap itself within a larger ecosystem of commercial, humanitarian, open source, government, and other actors in developing geospatial-related technologies.\n\nFounded in the aftermath of the 2010 earthquake in Haiti, the Humanitarian OpenStreetMap Team (HOT) helps both globally remote and local in-person volunteers to identify roads, buildings, and other features on the OpenStreetMap (OSM) platform. Created as a “free, editable map of the world,” OSM has enabled the mass-creation of volunteered geographical information (VGI) on a scale that is now more accurate than proprietary maps in many places, particularly as “crisis-mapping” has emerged as a means to gather real-time data on areas that have been affected by natural disasters or socio-political conflicts. OSM has also become also a site of resistance, where local and indigenous communities have engaged in mapping projects to reclaim autonomy, agency, and space through the historically contested practice of (digital) mapping. For these reasons, such crowdsourced maps have increasingly been used by humanitarian organisations to facilitate aid and disaster relief, and as open training data for algorithms learning how to automatically detect features through Artificial Intelligence (AI). As a key partner of humanitarian, corporate, and local actors, and having mobilised over 200,000 volunteers since 2010, HOT lies at the crux of these ongoing entanglements and contestations, both within and around the field of OSM. \n\nPrevious studies of crowdsourced geographical information and crisis-mapping have generally revolved around quantitative analyses of OSM’s data, focusing on the credibility of the data itself, the makeup of the communities that contribute to it, the effects of “event-centric” crowdsourcing, or “newcomer retention” in humanitarian mapping (Dittus et al., 2016a, 2016b, 2017; Haklay, 2010; Haworth et al., 2018; Sui et al., 2013). Alternatively, they have also focused on the “spatial knowledge”, “hacker political imaginary”, and gender composition of mappers themselves (Brandusescu \u0026 Sieber, 2018; McConchie, 2015; Stephens, 2013).\n \nParallel studies of other volunteer-driven communities like “Wikipedians” have taken similar approaches, analysing “user-generated content” and the motivations behind them (Nov, 2007; Yang \u0026 Lai, 2010). Both hacking and free and open source software (F/OSS) have also been explored ethnographically (Coleman, 2012; Kelty, 2008). While automated detection of features on OpenStreetMap has only recently become an important topic of research, ongoing studies have primarily focused on the accuracy or credibility of this endeavour (Brovelli et al., 2017; Resor, 2016).\n \nWhile existing studies of digital communities have focused on the socialities they engender or labor they require, they tend to forget the bureaucratic apparatuses that have emerged to govern them, both implicitly and explicitly (Coleman, 2012; Kelty, 2008). Similarly, studies of humanitarianism have focused on the ethics they operationalize, or the technologies that are mobilized in turn, but often at the expense of engaging in the wider spectrum of social and economic life that they enable (Cross, 2013; Redfield, 2012, 2016a; Scott-Smith, 2013, 2016a, 2019; Ticktin, 2014a). While this project draws upon these overlapping strains of research, it seeks to push the debate in an ethnographic direction, scaffolded by theories of bureaucratic technology, political economy, and humanitarianism.\n \nThis research draws from participation in over 40 online events over 1.5 years, including mapathons, conferences and online lectures with OSM mappers, as well as semi-structured interviews conducted with 27 key-informants, alongside watching more conference videos, and reading blogs, mailing list emails, Twitter exchanges, and other internet archives. While empirically influenced by studies of hacking and open source software, this work ultimately focuses on the mechanisms and means through which this “free and open map” is created, and ultimately the ways of seeing and doing that it enables (Coleman, 2012; Kelty, 2008). Ultimately, it was the “supply chains” heuristic that emerged as a means to understand and illustrate this process.\n\nSimilar to how supply chains “link ostensibly independent entrepreneurs, making it possible for commodity processes to span the globe”, the OSM project relies upon a series interconnected processes that enable the creation of the world’s crowdsourced map in a process that is far more precarious, and much less secure than promotional material might have one think (Tsing, 2009). Similar to how the satellite, computer, and software industries converged to create the conditions that allowed for OSM’s creation, so do people – and their associated institutions create map data through an almost miraculous collision of circumstances, assured precedent, and training. The Humanitarian OpenStreetMap Team, which was the initial entry point into open source mapping through, made its name by optimizing the mapping value chain: that is, by making it easier to contribute to OSM. But it also extended outwards: contributing to OSM was enabled not only by the wider socio-economic forces that coalesced to produce the project in the first place, but also by a series of digital value chains – both past and present.\n\nBy delineating this supply-chains approach, this study hopes to scaffold a mental model of humanitarian mapping and the OpenStreetMap more broadly, to be employed in future studies – both quantitative and qualitative. Practically, it hopes to provide a heuristic and application of ethnographic tools, and present questions and queries directly to the community more broadly.","original_language":"eng","persons":["Anne Lee Steele"],"tags":["sotm2022","19572","2022","OSM","OpenStreetMap"],"view_count":102,"promoted":false,"date":"2022-08-21T15:30:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2026-02-25T11:00:08.608+01:00","length":1623,"duration":1623,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19572-1e03a43d-5a75-50f7-b4db-3d4ca0d34a91.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19572-1e03a43d-5a75-50f7-b4db-3d4ca0d34a91_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19572-1e03a43d-5a75-50f7-b4db-3d4ca0d34a91.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19572-1e03a43d-5a75-50f7-b4db-3d4ca0d34a91.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19572-mapping-crises-communities-and-capitalism-on-openstreetmap-situating-humanitarian-mapping-in-the-open-source-mapping-supply-chain","url":"https://api.media.ccc.de/public/events/1e03a43d-5a75-50f7-b4db-3d4ca0d34a91","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"f89aac54-42a8-5d74-9044-d19d3a270298","title":"OSM \u0026 Trails: New Collaborations for Responsible Recreation","subtitle":null,"slug":"sotm2022-18527-osm-trails-new-collaborations-for-responsible-recreation","link":"https://2022.stateofthemap.org/sessions/CUV9H7/","description":"Sparked by concerns about OpenStreetMap's role in how the public accesses and recreates on protected lands, OpenStreetMap US volunteers, navigation app developers, national agencies and public land managers formed the OpenStreetMap US Trails Working Group in 2021. Bringing together a diversity of perspectives on trail mapping practices, trail safety, and protecting the environment, this group is working to address on-the-ground challenges, tagging schemes, authoritative data, and other topics related to mapping trails in OSM. Learn how this group is collaboratively developing solutions for responsible trail mapping in OpenStreetMap.","original_language":"eng","persons":["Maggie Cawley"],"tags":["sotm2022","18527","2022","User Experiences","OSM","OpenStreetMap"],"view_count":16,"promoted":false,"date":"2022-08-21T12:00:00.000+02:00","release_date":"2022-10-01T00:00:00.000+02:00","updated_at":"2023-12-20T17:00:04.639+01:00","length":1610,"duration":1610,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18527-f89aac54-42a8-5d74-9044-d19d3a270298.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18527-f89aac54-42a8-5d74-9044-d19d3a270298_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18527-f89aac54-42a8-5d74-9044-d19d3a270298.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18527-f89aac54-42a8-5d74-9044-d19d3a270298.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18527-osm-trails-new-collaborations-for-responsible-recreation","url":"https://api.media.ccc.de/public/events/f89aac54-42a8-5d74-9044-d19d3a270298","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"e3889993-d4e7-5034-b3eb-fabf069e6fd3","title":"Linking OpenStreetMap and Wikidata: Case study of Taiwan's villages and rivers dataset","subtitle":null,"slug":"sotm2022-18353-linking-openstreetmap-and-wikidata-case-study-of-taiwan-s-villages-and-rivers-dataset","link":"https://2022.stateofthemap.org/sessions/TJMAQT/","description":"Open data is a trend in Taiwan, and some community members of OpenStreetMap or Wikidata are importing or merging information they obtain from government sources into the corresponding OpenStreetMap and Wikidata Database. The village dataset is available by sharp file and detains metadata with reference numbers, and the river dataset covered big rivers in Taiwan. In this talk, I will talk about the process of importing data, maintaining data, and linking each data not only with the government source but also to OpenStreetMap and Wikidata.\n\nI will not only talk about dealing with the village and river datasets but also with other import project in Taiwan, like Taichung address dataset import, schools, kindergarten, etc. And I will also have a short introduction of Taiwan community activities, both OpenStreetMap and Wikidata. Recently OpenStreetMap Taiwan got funded by Wikimedia Foundation to improve Taiwan mapping data and also Wikimedia related projects like Wikidata, Wikipedia, Wikivoyage, Wiki Commons, etc.","original_language":"eng","persons":["Dennis Raylin Chen"],"tags":["sotm2022","18353","2022","Remote Control","OSM","OpenStreetMap"],"view_count":42,"promoted":false,"date":"2022-08-20T10:30:00.000+02:00","release_date":"2022-09-29T00:00:00.000+02:00","updated_at":"2026-03-02T16:45:08.055+01:00","length":1714,"duration":1714,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18353-e3889993-d4e7-5034-b3eb-fabf069e6fd3.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18353-e3889993-d4e7-5034-b3eb-fabf069e6fd3_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18353-e3889993-d4e7-5034-b3eb-fabf069e6fd3.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18353-e3889993-d4e7-5034-b3eb-fabf069e6fd3.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18353-linking-openstreetmap-and-wikidata-case-study-of-taiwan-s-villages-and-rivers-dataset","url":"https://api.media.ccc.de/public/events/e3889993-d4e7-5034-b3eb-fabf069e6fd3","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"f805ddc8-96c7-52e1-bced-5b8b907682a2","title":"OpenStreetMap in schools: The case study of Bari","subtitle":null,"slug":"sotm2022-18433-openstreetmap-in-schools-the-case-study-of-bari","link":"https://2022.stateofthemap.org/sessions/GCLYZE/","description":"OpenStreetMap has proven to be a really interesting and valuable classroom instrument. Making students work with OSM allows them to develop the soft skills entailed in cooperation, to interact with something bigger than their class or school and to give their contribution for the sake of the collectivity. In this particular experienced, we worked on the local area of Bari with a class with 17 female students of a \"Liceo Economico-Sociale\", a school not particularly engaged in the STEM field. Thus, we also wanted to help them get more confident with the STEM field, which desperately need more diversity. Furthermore, focus has been on accessibility.\n\nDuring the talk, we will explain what we did during the project and the outcomes. \n\nThe activities involved:\n* an introduction to OpenStreetMap, ID editor, licenses, but also Wikimedia projects\n* a phase of physical mapping, in which we visited the city centre of Bari using field papers and StreetComplete to map shops/restaurants/cafes and their accessibility status, accessibility ramps and other useful information to add both on OpenStreetMap and on Wikivoyage\n* the editing of OpenStreetMap and Wikivoyage to add the information gathered. \n\nIn addition to what explained in the abstract, we would like to underline how the experience benefited equally the territory, thanks to the improved geographical information and also tourism information, the students, because of the worthwhile experience they have gained and the rediscovery of the STEM field, and the community, not only because of the new information added but also due to the increment (even if little) of diversity in contributors.\n\nAs explained, we did not contribute only to OpenStreetMap, but also to Wikivoyage, the official, non-commercial and freely-licensed (CC-BY-SA) sister site of Wikipedia for tourism guides, and to Wikimedia Commons, the free multimedia database of Wikimedia projects. The two of us, Ferdinando Traversa and Rosa Colacicco, are respectively the regional coordinator for Apulia and the OpenStreetMap coordinator for Apulia of Wikimedia Italy/OpenStreetMap Italy, so the project was endorsed and sponsored by the organisation. This project is consequently also an example of synergy between OpenStreetMap and Wikimedia projects, strenghtened by the fact that in Italy chapters for WMF and OSMF coincide. Rosa Colacicco is also the president of the YouthMappers@UniBA, the local group of YouthMappers which collaborates with the department of Geology of the University of Bari.\n\nIn conclusion, we would like to present this experience as a case study in order to enable others to try a similar pathway.","original_language":"eng","persons":["Ferdinando Traversa","Rosa Colacicco"],"tags":["sotm2022","18433","2022","User Experiences","OSM","OpenStreetMap"],"view_count":36,"promoted":false,"date":"2022-08-20T11:30:00.000+02:00","release_date":"2022-09-29T00:00:00.000+02:00","updated_at":"2026-03-19T22:45:06.659+01:00","length":1336,"duration":1336,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18433-f805ddc8-96c7-52e1-bced-5b8b907682a2.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18433-f805ddc8-96c7-52e1-bced-5b8b907682a2_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18433-f805ddc8-96c7-52e1-bced-5b8b907682a2.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18433-f805ddc8-96c7-52e1-bced-5b8b907682a2.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18433-openstreetmap-in-schools-the-case-study-of-bari","url":"https://api.media.ccc.de/public/events/f805ddc8-96c7-52e1-bced-5b8b907682a2","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"93f68732-6e4e-51fa-95f4-379a85115f35","title":"OSM Sidewalkreator - A QGIS plugin for automated sidewalk drawing for OSM","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19556-osm-sidewalkreator-a-qgis-plugin-for-automated-sidewalk-drawing-for-osm","link":"https://2022.stateofthemap.org/sessions/JNJKYR/","description":"Sidewalks are a relevant part of the living space in urban environments, but there are still few mapped sidewalks. In recent years, the mapping of sidewalks has grown in importance among the OSM and academic communities. To cover up this gap, we propose a Github-hosted, fully open-source QGIS Plugin entitled \"OSM SidewalKreator\" to automatically draw for OSM the geometries of sidewalks crossings and kerb crossing interfaces. Furthermore, the tool gives the user the capacity to control the process. Then, deepen, improve, and increase the amount of sidewalk mapping in OpenStreetMap to improve accessibility and mobility worldwide.\n\nSidewalks are a relevant part of the living space in urban environments. The existence of sidewalks and their condition is fundamental to locomotion in general and is critically important in mobility groups such as cyclists, wheelchair users, blind people, the elderly, and children. Also, the displacement along sidewalks can ensure safety from traffic, contributing to the well-being of citizens [1].\nThere is still an open debate about the best way to represent sidewalks in the OpenStreetMap community. Some users claim that they should be represented only as tags of the streets, using compound tags such as \"sidewalk:left/right:surface=*\", arguing that over-representation can pollute the map and create unnecessary complexity [2]. Biagi [3] and many OSM users nowadays [4] have been showing the representation of sidewalks as separated geometries as allegedly their best representation in OSM. There are many listed advantages [4]: crossings may be represented as lines, with the kerb interfaces as points (8 in a regular 4-way intersection); the actual traversing length will be represented (as it will include block corners and crossings); independence from the digitizing direction, as \"left\" and \"right\" may swap if someone inverts the way direction; ease of representation for pedestrian islands. Moreover, some cases cannot be represented correctly using the tag scheme or will need some cumbersome solutions, as it shall represent the portion of the sidewalk that is orthogonally projected from the street. Therefore, if a property is different on both sides, one may need to split the highway into four segments to represent it correctly. There are also other issues, e.g. geometric properties such as the distance from the sidewalk to the street will stay unclear.\nRegardless of the form chosen for representation, there are still few mapped sidewalks. For example, according to Taginfo [5], as of April of 2022, there are approximately 201 million ways with the \"highway\" key, but only 16.8 million (7.61%) are tagged as \"highway=footway\", considering the key \"footway\", there are only 4.8 million (2.45% of 201 million) ways tagged as such (58% sidewalk, 41% crossing), considering the tag \"sidewalk=*\", there are only 2.6 million (1.3% of 201 million) ways tagged. So, considering that most features are located in urban environments [6;7], where the major part of streets may have a sidewalk, the sidewalks are underrepresented in both schemes. Historically, it has been an issue, as[8] showed that in Berlin, only 5.6% of the Highways have a \"sidewalk\" tag, growing to only 8.2% in 2017 [9].\nRecently, the mapping of sidewalks has grown in importance among the OSM and academic [10;11;12;13] communities. For example, the Open Sidewalks Initiative [14]. They are both a community and a project, providing dedicated mapping with an elaborate scheme on how to map in a pedestrian-centered way, but only manually. Drawing sidewalks and crossings is time-consuming and can be error-prone. This effort can be inferred by examining the OpenSidewalk's own Tasking Manager [15], where in the most near-completion project [16], each task has taken 9.4 minutes to be mapped, but 22.7 minutes be validated. Thus, considering just crossing mapping, for the 1046 existing tasks, it will take approximately 163.9 hours of mapping and 395.7 hours of validation. This total encompasses an area of just approx. 6.17 km2, only 0.65% of the urban area of the city of Sao Paulo, for example.\nTo cover up this gap, we propose a Github-hosted, fully open-source QGIS Plugin entitled \"OSM SidewalKreator\" [17], which aims to automatically draw for OSM the geometries of sidewalks, crossings and kerb crossing interfaces, along with the basic descriptive tags. This tool gives the user the capacity to control and supervise the entire process. It contains a user-friendly GUI (Guided User Interface) that enables and disables the buttons according to the step in the process. The plugin methodology, encompassing the steps that the plugin runs through are basically: 1) Fetch Interest OSM data (highways and optionally buildings and addresses, when available) from a bounding polygon given by the user; 2) Generate a table for standard widths for the values for the \"highway=*\" tag, to provide widths to highways that have no \"width=*\" key; 3) remove ways that aren't streets, according to a value of width below 0.5m in the table; and split into segments at road intersections; 4) generate the sidewalk geometries based on a per-segment buffer (optionally checking if it doesn't overlaps buildings), dissolve, and a negative then positive buffer to create rounded corners (if wanted by the user) and then finally extract line geometries (inner holes outlines); 6) generate the crossings geometry, using vector that that grows iteratively in a direction perpendicular to the segment or parallel to adjacent segments (according to user's choice), until an intersection with the sidewalks (dissolved as a single multipart geometry) is found, there are also filtering options, to avoid badly generated crossings, such as too long geometries or too close to other crossings; 7) split the sidewalk geometries, since sidewalks properties (smoothness, surface material, etc.) may differ many times in the same block, it can split based on voronoi polygons of building centroids and/or addresses, or with a minimal length, minimal number of segments, only block façades or don't split; 8) output all features, to a single .geojson ready to be imported at JOSM editor, where more inspection on generated data can be carried out. The plugin also outputs other auxiliary files as a .txt with a pre-filled changeset comment and intermediary files for debugging. The tool's first use test was performed in April 2022 to map the Campus Centro Politécnico of the Federal University of Paraná, with transportation engineering students, for the Horus Nav Project [18] an open-source tool for route optimization for blind people.\n    The key idea that guides the present work is to provide a tool to help intermediary to advanced users to speed up the tedious task of manually drawing sidewalks, crossings and kerbs. The tool does not intend to be a fully automated (a challenging task [12;13]) one-click solution but always calls the user to check out the results, step-by-step, using the resourcefulness from QGIS and JOSM. After the data importing, the user is also encouraged to carry out a validation project on a Tasking Manager. The present work also advocates for the representation of sidewalks as separate geometries as an ideal way to represent the sidewalk network. However, the tag scheme still can be helpful in some specific situations, such as representing explicitly that some highway does not have a sidewalk on one or both sides. Taking advantage of previously available data, including tag schema, is one of the features that might be included in future releases of OSM SidewalKreator and integrating properly with previously drawn sidewalks and crossing restrictions. Finally, by joining awareness in mapping communities, detailed instructions, and tools for automation, we can deepen, improve, and increase the amount of sidewalk mapping in OpenStreetMap and present a solid foundation for improving accessibility and mobility in cities around the world.","original_language":"eng","persons":["Kauê de Moraes Vestena"],"tags":["sotm2022","19556","2022","OSM","OpenStreetMap"],"view_count":261,"promoted":false,"date":"2022-08-21T09:30:00.000+02:00","release_date":"2022-10-06T00:00:00.000+02:00","updated_at":"2026-03-03T16:00:08.382+01:00","length":1711,"duration":1711,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19556-93f68732-6e4e-51fa-95f4-379a85115f35.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19556-93f68732-6e4e-51fa-95f4-379a85115f35_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19556-93f68732-6e4e-51fa-95f4-379a85115f35.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19556-93f68732-6e4e-51fa-95f4-379a85115f35.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19556-osm-sidewalkreator-a-qgis-plugin-for-automated-sidewalk-drawing-for-osm","url":"https://api.media.ccc.de/public/events/93f68732-6e4e-51fa-95f4-379a85115f35","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"864dae83-ca37-5dac-8ef4-6def1641e6cb","title":"Digital Champions fighting Gender Based Violence in rural Tanzania with maps","subtitle":null,"slug":"sotm2022-18291-digital-champions-fighting-gender-based-violence-in-rural-tanzania-with-maps","link":"https://2022.stateofthemap.org/sessions/FJWKRV/","description":"Our digital champions project in Tanzania has transformed the lives of 353 women who had never used a smartphone before into confident advocates of mapping in their extremely marginalised communities. They have delivered training to over 9000 women in these villages and reported over 470 cases of gender based violence in their villages to the police and social services who have then used the maps to find and protect these women. Giving local women and youth the digital tools to protect their sisters in their communities is an extremely cost effective, long term solution to build up our mapping community and make it more inclusive, and share lessons learnt\n\nWe will talk about the digital champions project which has transformed the lives of 353 women who had never used a smartphone before into confident advocates of mapping in their extremely marginalised communities. They have delivered training to over 9000 women in these villages and reported over 470 cases of gender based violence in their villages to the police and social services who have then used the maps to find and protect these weomen. We will show giving local women and youth the digital tools to protect their sisters in their communities is an extremely cost effective, long term solution to build up our mapping community and make it more inclusive, and share lessons learnt","original_language":"eng","persons":["Janet Chapman"],"tags":["sotm2022","18291","2022","Community and Foundation","OSM","OpenStreetMap"],"view_count":13,"promoted":false,"date":"2022-08-20T14:30:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2025-07-25T21:00:05.966+02:00","length":1515,"duration":1515,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18291-864dae83-ca37-5dac-8ef4-6def1641e6cb.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18291-864dae83-ca37-5dac-8ef4-6def1641e6cb_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18291-864dae83-ca37-5dac-8ef4-6def1641e6cb.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18291-864dae83-ca37-5dac-8ef4-6def1641e6cb.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18291-digital-champions-fighting-gender-based-violence-in-rural-tanzania-with-maps","url":"https://api.media.ccc.de/public/events/864dae83-ca37-5dac-8ef4-6def1641e6cb","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"baca812f-a2de-5420-a4fe-b5c51cbbb3b9","title":"OSM Carto as vector tiles","subtitle":null,"slug":"sotm2022-18369-osm-carto-as-vector-tiles","link":"https://2022.stateofthemap.org/sessions/3EREXZ/","description":"Presentation of the work we've done to adapt the OSM Carto style to vector tiles. We will take you through the making-of the cartography, step by step, scale by scale.\nAvailable on MapTiler Cloud (https://cloud.maptiler.com/maps/openstreetmap/), the style will be integrated into the next version of the OpenMapTiles project.\n\nWe will also show you how to use this style in QGIS and how to display it on 3D maps with MapLibre.","original_language":"eng","persons":["Wladimir Szczerban","Jiri Komarek"],"tags":["sotm2022","18369","2022","Cartography","OSM","OpenStreetMap"],"view_count":238,"promoted":false,"date":"2022-08-19T12:00:00.000+02:00","release_date":"2022-09-21T00:00:00.000+02:00","updated_at":"2026-03-29T01:00:05.025+01:00","length":1884,"duration":1884,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18369-baca812f-a2de-5420-a4fe-b5c51cbbb3b9.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18369-baca812f-a2de-5420-a4fe-b5c51cbbb3b9_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18369-baca812f-a2de-5420-a4fe-b5c51cbbb3b9.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18369-baca812f-a2de-5420-a4fe-b5c51cbbb3b9.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18369-osm-carto-as-vector-tiles","url":"https://api.media.ccc.de/public/events/baca812f-a2de-5420-a4fe-b5c51cbbb3b9","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"c146c865-5941-5a50-b317-39fbc7e34a70","title":"Crowdsourcing and virtual reality applications for peacekeeping: study cases in Mogadishu and Tripoli","subtitle":null,"slug":"sotm2022-18520-crowdsourcing-and-virtual-reality-applications-for-peacekeeping-study-cases-in-mogadishu-and-tripoli","link":"https://2022.stateofthemap.org/sessions/GNG37Y/","description":"The United Nations Global Service Center (UNGSC) is developing Virtual Reality applications utilizing OpenStreetMap data. Through a Virtual Reality (VR) pilot project, UNGSC aims to provide UN peacekeepers on the field with 3D digital replicas of the cities in which they are operating, building a sandbox that enhances operational planning with simulations. In order to develop such applications, OpenStreetMap buildings are collaboratively edited, validated and ingested. Field mapping and street-level imagery are also extremely important to add details to the rendered buildings. There could be the possibility to organise a live demo through VR headset.\n\nThe United Nations Global Service Center (UNGSC) is developing Virtual Reality (VR) applications utilizing OpenStreetMap data. Through the Virtual Operation Center (VOC) pilot project, UNGSC aims to provide UN peacekeepers on the field with 3D digital replicas of the cities in which they are operating, building a sandbox that enhances operational planning with simulations.\nUnder the umbrella of the UN Mappers community, the areas of interest get mapped by means of Tasking Manager projects. Differently from other building mapping campaigns, those are different, as higher quality of the footprints is needed, as well as better placement at the ground floor level and understanding of tall buildings. Furthermore mapping can be complemented with field mapping with street-level imagery as well as smartphone applications, in order to add details on the buildings characteristics as height, levels, roof shape, color and material.\nAfter validation of all the edited data, the buildings are ingested into Virtual Reality software development tools as ArcGIS CityEngine and Unity to develop the sandbox application accessible through VR headset. Buildings get eventually rendered utilizing OSM tags which define the 3D model of each object.\nThe activity has been already tested and developed in Mogadishu, Somalia, and Tripoli, Libya, where the mapping took place engaging with the local communities (OSM Somalia and OSM Libya). At the end of the talk, there could be the possibility to organise a live demo through VR headset.","original_language":"eng","persons":["Michael Montani"],"tags":["sotm2022","18520","2022","Software Development","OSM","OpenStreetMap"],"view_count":24,"promoted":false,"date":"2022-08-21T09:30:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2024-12-27T20:30:05.830+01:00","length":1195,"duration":1195,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18520-c146c865-5941-5a50-b317-39fbc7e34a70.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18520-c146c865-5941-5a50-b317-39fbc7e34a70_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18520-c146c865-5941-5a50-b317-39fbc7e34a70.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18520-c146c865-5941-5a50-b317-39fbc7e34a70.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18520-crowdsourcing-and-virtual-reality-applications-for-peacekeeping-study-cases-in-mogadishu-and-tripoli","url":"https://api.media.ccc.de/public/events/c146c865-5941-5a50-b317-39fbc7e34a70","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"84782692-c90c-5c19-a971-803e06bf80c7","title":"Landmarks for accessible space – promoting geo-literacy through geospatial citizen science","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19251-landmarks-for-accessible-space-promoting-geo-literacy-through-geospatial-citizen-science","link":"https://2022.stateofthemap.org/sessions/3YQRDX/","description":"Geo-literacy provides skills to read, interpret and use geospatial information, where little evidence exists regarding the potential and capacity of new education programs in advancing these skills. We present a citizen science project held in 13 high schools in Israel, where the students practice participatory mapping with OpenStreetMap to map features relevant to the navigation of visually impaired pedestrians. We show that students improve their geospatial thinking and reasoning skills, including their self-esteem. We believe that this research contributes to various pedagogic and education levels, in terms of theoretical knowledge about the integration of innovative geo-literacy programs.\n\nThe 21st Century dictates that people have a good spatial and geographic understanding and knowledge. Geo-literacy is aimed to provide skills to read, interpret and use geospatial information. This is achieved by acquiring critical spatial thinking, reasoning, and analysis, and presenting understanding of the world using geographical terms and spatial language. Recent years have led to the development of new geo-literacy education programs specifically designed to nurture and promote these skills. These education programs build on geographical education that promotes spatial thinking and active citizenship. Still, little evidence exists regarding the potential and capacity of these programs in advancing civic and geographic skills and knowledge in the 21st Century, and on its contribution to - and advancing of - the individual and the society.\n\nThe aim of this research is to gain a better understanding of the development of geo-literacy in the framework of a citizen science project in high schools. The citizen science project implemented in several schools in Israel – landmarks for accessible space, advances scientific research that aims to make the urban environment more accessible for visually impaired pedestrians. The participating high school students practice participatory mapping with OpenStreetMap (OSM) to map features relevant to the navigation of visually impaired pedestrians. These map features are used for the automatic calculation of optimal walking routes. The project combines social involvement, learning through geographic information systems, and familiarity with the field of urban accessibility for visually impaired people. The project includes the following stage:\n1.\tPre-stage that includes a) the design of the modular learning environment, b) the organizational and pedagogical preparation of the project integration in schools, and c) questionnaires examining the current level of geographic literacy of the participating students and their perspective regarding the integration of citizen science in schools.\n2.\tIntervention program that includes guest seminars (including YouTube videos), lectures and learning activities, exposure to the world of visually impaired people, and the need for accessible environments and learning activities in the field of geoinformation with emphasis on OSM, crowdsourcing, and participatory mapping.\n3.\tMapping missing data into OSM. This stage is carried out in the field with a designated app developed for this project. The app - “Mundi” - allows the mapping of specific geographic features (mapping elements) used for the calculation of accessible routes designed specifically for visually impaired pedestrians. The features include, among others, sidewalks, crossings, accessibility aids, and handrails. The app includes gamification and tasks to encourage the students to map the missing features in their area of residence.\n4.\tPost-stage questionnaires aimed to investigate and analyze the development of spatial skills in the context of participation in this program, examining whether students’ level of geographic literacy improved and whether they gained new knowledge on urban accessibility and the navigation proficiencies of visually impaired pedestrians. This stage also included a quantitative analysis of the students’ contributions in terms of OSM mapping, among others, the number of map edits, type of mapped features, the spatial coverage and temporal extent of their mapping activity.\n\nThe study was conducted in the last two years in 13 high schools, including 25 classes and 460 students. The intervention model was implemented for three months in each class. The participating students implemented this project within their Cyber Geography studies, enabling them to learn through various geographic information systems. In total, close to 10,000 OSM edits were made by the students, which included more than 3,000 new crossings (and attributed tags), 400 new sidewalks, and 7,000 new street objects and obstacles (e.g., bus stations, light poles, trees, gates, bicycle parking).\n\nPreliminary analysis showed that participation in the citizen science project increased the students’ geospatial thinking and reasoning. For example, according to the questionnaire variables, on a score scale of 0-100, the geospatial thinking score has increased from 31 to 56, while the spatial awareness score has increased from 34 to 73 (p \u003c .001). The geographic skills knowledge has increased from 3 to 3.9 (scale of 1-5). Moreover, the students' self-esteem with respect to their knowledge and use of geographic skills has improved considerably. In addition, results show that the broad and in-depth intervention model increased the students' appreciation of the scientists' contribution to the project, the contribution of the program in general, and the satisfaction with their participation in the project.\n\nThis is the first project to introduce the use of OSM-based learning to the study of geography in Israel. Based on its outcome and analysis, we believe that this research contributes to various pedagogic and education levels, in terms of theoretical knowledge about the integration of innovative geo-literacy programs. These promote the drawing of operational and applicable actions regarding the planning of future projects and serving stakeholders in academia and the education system in terms of integrating scientific projects that increase students’ involvement in science and society and promote geo-literacy.","original_language":"eng","persons":["Sagi Dalyot"],"tags":["sotm2022","19251","2022","OSM","OpenStreetMap"],"view_count":18,"promoted":false,"date":"2022-08-21T15:00:00.000+02:00","release_date":"2022-10-15T00:00:00.000+02:00","updated_at":"2025-06-24T18:30:04.218+02:00","length":1566,"duration":1566,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19251-84782692-c90c-5c19-a971-803e06bf80c7.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19251-84782692-c90c-5c19-a971-803e06bf80c7_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19251-84782692-c90c-5c19-a971-803e06bf80c7.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19251-84782692-c90c-5c19-a971-803e06bf80c7.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19251-landmarks-for-accessible-space-promoting-geo-literacy-through-geospatial-citizen-science","url":"https://api.media.ccc.de/public/events/84782692-c90c-5c19-a971-803e06bf80c7","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"7aeac689-afe0-5b00-a161-c280b8595a2d","title":"OSMF Board AMA","subtitle":null,"slug":"sotm2022-18386-osmf-board-ama","link":"https://2022.stateofthemap.org/sessions/AT3YM7/","description":"OpenStreetMap Foundation Board Ask Us Anything (i.e. AMA). We will take questions from the audience, or other questions that people can submit before the event, and we will talk about and answer them. We can talk about the past actions of the board, and what future plans we have.\n\nThis is a chance to ask the OSM Foundation Board questions, to engage with the board. Let’s have a conversation about the Foundation, the Board and how all the parts work together. If you know nothing about what the board is doing, this is a chance to find out. Find out what the Foundation does and doesn’t do, what it can and can’t do. Find out how you can help, how you can get involved. The Board is committed to openness and wants to engage with the community.","original_language":"eng","persons":["Amanda McCann"],"tags":["sotm2022","18386","2022","Community and Foundation","OSM","OpenStreetMap"],"view_count":71,"promoted":false,"date":"2022-08-20T15:00:00.000+02:00","release_date":"2022-09-24T00:00:00.000+02:00","updated_at":"2025-03-10T21:45:05.516+01:00","length":3275,"duration":3275,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18386-7aeac689-afe0-5b00-a161-c280b8595a2d.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18386-7aeac689-afe0-5b00-a161-c280b8595a2d_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18386-7aeac689-afe0-5b00-a161-c280b8595a2d.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18386-7aeac689-afe0-5b00-a161-c280b8595a2d.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18386-osmf-board-ama","url":"https://api.media.ccc.de/public/events/7aeac689-afe0-5b00-a161-c280b8595a2d","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"f88ca309-d9b5-57aa-aa79-0087de35c60a","title":"Wikimedia Italia - What is it doing for the Italian OSM community?","subtitle":null,"slug":"sotm2022-18498-wikimedia-italia-what-is-it-doing-for-the-italian-osm-community-","link":"https://2022.stateofthemap.org/sessions/MRK3C8/","description":"Wikimedia Italia, the Italian OpenStreetMap Local Chapter of the OSM Foundation, presents its activities, online infrastructure developed to support OpenStreetMap in Italy and the Italian community. The talk will share the experience, situations and factors that have influenced agreat collaboration with the local contributors and institutions during the last years.\n\nThe presentation will go through different areas of the Local Chapter’s activities. The recently updated infrastructure, composed by the Tasking Manager and the OSM extracts for Italy. Those tools are available and used by the Italian OSM community. Moreover, the official new Italian OSM website, the OSM licences tracking process and other tools developed to support the community will be presented.\nOther experiences that will be shared are the collaborations with local institutions, with the scope of strengthening local communities and increase the data in OSM. The keys to success are the volunteer coordinators. They are a point of contact important to establishing collaborations with individuals and institutions throughout the national territory","original_language":"eng","persons":["Anisa Kuci"],"tags":["sotm2022","18498","2022","Community and Foundation","OSM","OpenStreetMap"],"view_count":35,"promoted":false,"date":"2022-08-21T15:30:00.000+02:00","release_date":"2022-10-02T00:00:00.000+02:00","updated_at":"2025-11-13T12:15:05.220+01:00","length":1539,"duration":1539,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18498-f88ca309-d9b5-57aa-aa79-0087de35c60a.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18498-f88ca309-d9b5-57aa-aa79-0087de35c60a_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18498-f88ca309-d9b5-57aa-aa79-0087de35c60a.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18498-f88ca309-d9b5-57aa-aa79-0087de35c60a.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18498-wikimedia-italia-what-is-it-doing-for-the-italian-osm-community-","url":"https://api.media.ccc.de/public/events/f88ca309-d9b5-57aa-aa79-0087de35c60a","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"c68c193e-cecb-5310-a5e6-48c6b36bf293","title":"The OpenStreetMap Use for Medical Humanitarian Operations by Médecins Sans Frontières","subtitle":null,"slug":"sotm2022-18323-the-openstreetmap-use-for-medical-humanitarian-operations-by-mdecins-sans-frontires","link":"https://2022.stateofthemap.org/sessions/XJZBFH/","description":"Follow the OSM journey of the Médecins Sans Frontières (MSF) from a mapathon in Berlin in 2014 to creating and contributing geodata for numerous MSF operations through the Missing Maps project. This talk will be about how MSF uses OpenStreetMap internally and how we contribute through remote and field mapping. We will also share the lessons learned and reflect on the biggest challenges for MSF in creating and using the OSM data.\n\nThe OpenStreetMap journey of Médecins Sans Frontières (MSF) started in 2014 with a mapping party in Berlin and field mapping in Lubumbashi. Eight years later, OpenStreetMap is the reference geographical dataset for most of MSF operations on the ground.\n\nOpenStreetMap has been used in public health interventions, disease outbreaks, mortality studies and to support large logistical operations. Still every day we are learning how geographical information can support us in doing our job better, in reaching more people, in saving more lives. \n\nMSF is not only using OpenStreetMap, but it also actively contributes to the map through the Missing Maps project launched in 2014 together with the American Red Cross, the British Red Cross, and the Humanitarian OpenStreetMap Team. Since then, MSF has trained dozens of Missing Maps champions, co-organized hundreds mapathons and involved thousands of volunteers in some 30 countries.\n\nIn this presentation we will talk about how MSF uses OpenStreetMap internally and how we contribute through remote and field mapping. We will also share the lessons learned and reflect on the biggest challenges for MSF in creating and using the OSM data.","original_language":"eng","persons":["Jana Bauerova"],"tags":["sotm2022","18323","2022","User Experiences","OSM","OpenStreetMap"],"view_count":46,"promoted":false,"date":"2022-08-21T15:00:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2025-12-19T23:30:05.474+01:00","length":1691,"duration":1691,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18323-c68c193e-cecb-5310-a5e6-48c6b36bf293.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18323-c68c193e-cecb-5310-a5e6-48c6b36bf293_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18323-c68c193e-cecb-5310-a5e6-48c6b36bf293.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18323-c68c193e-cecb-5310-a5e6-48c6b36bf293.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18323-the-openstreetmap-use-for-medical-humanitarian-operations-by-mdecins-sans-frontires","url":"https://api.media.ccc.de/public/events/c68c193e-cecb-5310-a5e6-48c6b36bf293","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"14990654-5de1-5f83-b792-a7bd7bd1abab","title":"Inferring default speed limits","subtitle":null,"slug":"sotm2022-18524-inferring-default-speed-limits","link":"https://2022.stateofthemap.org/sessions/YWH3XD/","description":"Coverage of `maxspeed` data in OpenStreetMap is very sketchy (about 12%). This situation is unlikely to change because the limits are often not signed explicitly. So, data consumers such as router software need to compensate huge holes in the data with more or less rough estimates based on other data.\n\nThis talk shall explore a method how to infer default speed limits for different vehicle and road types more precisely for each country.","original_language":"eng","persons":["Tobias Zwick"],"tags":["sotm2022","18524","2022","Software Development","OSM","OpenStreetMap"],"view_count":747,"promoted":false,"date":"2022-08-19T17:30:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2026-04-01T11:45:07.063+02:00","length":1843,"duration":1843,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18524-14990654-5de1-5f83-b792-a7bd7bd1abab.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18524-14990654-5de1-5f83-b792-a7bd7bd1abab_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18524-14990654-5de1-5f83-b792-a7bd7bd1abab.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18524-14990654-5de1-5f83-b792-a7bd7bd1abab.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18524-inferring-default-speed-limits","url":"https://api.media.ccc.de/public/events/14990654-5de1-5f83-b792-a7bd7bd1abab","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"4c36c9c7-de50-58dc-b5b9-9a9c2e9fb329","title":"A review of Mapillary-generated map data and how accuracy compares across devices","subtitle":null,"slug":"sotm2022-18509-a-review-of-mapillary-generated-map-data-and-how-accuracy-compares-across-devices","link":"https://2022.stateofthemap.org/sessions/DKLT7X/","description":"Mapping is time-consuming and requires a high workforce when it comes to keeping maps up-to-date. Mapillary brings a different approach to geospatial data collection with street-level imagery. This approach allows communities to collect geospatial data faster and cheaper. But can Mapillary-generated data be useful for enriching OpenStreetMap? In this study, Mapillary-extracted map data will be examined against ground truth to assess data quality on contributing to OpenStreetMap.\n\nIn this case study the positional accuracy and completeness are assessed by benchmarking Mapillary-generated data against ground truth for street light provided by Ordu Metropolitan Municipality in the borough of Altinordu, which covers an area of 9 km2. A total number of 323 street lights were recorded in the authoritative dataset.\n\nCompleteness and positional accuracies are evaluated for each street light for different camera setups. In order to compare the impact of camera type on positional accuracy, street-level imagery was collected with three different cameras: iPhone 11, GoPro Hero 7 Black and GoPro Max. Collected street-level imagery was uploaded to Mapillary by using the Desktop Uploader. Imagery which are captured from different cameras is isolated during the upload to compare map data accuracy, completeness and correctness comparison based on camera type.\n\nIn this experiment, we validate the effectiveness of Mapillary extracted map data by focusing on streetlights by evaluation results based on completeness and positional accuracy key performance indicators. The best result of completeness is achieved with GoPro Hero 7 Black with 87.57% in the working area and it is followed by GoPro MAX with 77.30% for Mapillary extracted street light data . Lastly, the completeness of iPhone 11 acquired data is 71.89%.\n\nIn terms of positional accuracy, our experiment shows GoPro MAX captured street-level imagery can be extracted with 2.02 m of positional error and it is followed by GoPro Hero 7 Black with 2.17 m of positional error. The average positional error of street light which is extracted from iPhone 11 captured street-level imagery is 2.21 m. This positional error is close to the precision of a single frequency GPS receiver.\n\nThis experimental study shows that positioning accuracy is highly related to the GPS accuracy of the capture device, and in general, a large part of the final positional error can be attributed to this. Mapillary's 3D reconstruction is able to mitigate some of these effects. Additionally, capturing with a large field of view has a positive effect on accuracy. In this study we also validate that positional accuracy depends on various factors of the capture process; precision of GPS receiver and additionally positioning hardware, resolution and quality of the images, image capture frequency of the camera, imagery density in the working area and type of photo such as flat or 360.\n\nThe overall positional accuracies are under 5m which can be a promising solution for enriching street lights data on OpenStreetMap and collecting streetlights inventory for the municipalities and governmental bodies if this data will not be used for surveying purposes or reference data, however Mapillary-generated data can be useful and time effective as complementary data with low cost collection expenses.","original_language":"eng","persons":["Said Turksever"],"tags":["sotm2022","18509","2022","Mapping","OSM","OpenStreetMap"],"view_count":137,"promoted":false,"date":"2022-08-20T15:30:00.000+02:00","release_date":"2022-10-01T00:00:00.000+02:00","updated_at":"2026-01-31T09:30:10.195+01:00","length":1916,"duration":1916,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18509-4c36c9c7-de50-58dc-b5b9-9a9c2e9fb329.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18509-4c36c9c7-de50-58dc-b5b9-9a9c2e9fb329_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18509-4c36c9c7-de50-58dc-b5b9-9a9c2e9fb329.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18509-4c36c9c7-de50-58dc-b5b9-9a9c2e9fb329.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18509-a-review-of-mapillary-generated-map-data-and-how-accuracy-compares-across-devices","url":"https://api.media.ccc.de/public/events/4c36c9c7-de50-58dc-b5b9-9a9c2e9fb329","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"b55fbb53-3993-525c-8296-d1984573c4b5","title":"Floor plan extraction from digital building models","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19570-floor-plan-extraction-from-digital-building-models","link":"https://2022.stateofthemap.org/sessions/ZUXTN8/","description":"As part of a larger endeavour to make floor plan representations from building models available for indoor map and navigation services, we study the integration of  IFC and OSM.\n\n# Introduction, background, motivation\n\nOfficial geo data is increasingly published not only in the form of 2D maps,\nbut also in 3D, mainly as city models in CityGML. Usually the outer shell of\nbuildings is captured in such models, but they may also involve more intricate\ndetail. Even more detailed building models are generated during the planning\nprocess for new buildings and renovations. These are nowadays produced in\ndigital form, archived in as-built phase by owners and operators for the life\ntime of a building and, in the future, may even be required to be submitted for\nbuilding permits.\n\nAt the same time there is an increasing public interest in detailed information\nabout public and semi-public interior spaces, for example about their\naccessibility, localization of barriers or targets (e.g. contact persons in\npublic administration, shops in malls, booths on fairs, markets or larger info\nevents, departments or hospital wards) or resources (e.g. books in libraries,\ncharging stations, fire equipment or defibrillators) or to get a first\nimpression in advance (e.g. virtual open day). The interest and the points of\ninterest may be temporary or permanent.\n\nSince the context of creating and capturing geo data and building data is\nfundamentally different, there is hardly any integration. Indoor data for maps\nand navigation models is manually captured or at best derived in undocumented\nmulti-step semi-automatic workflows.\n\n\n# Aim and purpose of the study\n\nThe project \"Level Out\" sets out to develop automated methods and services to\nmake detailed indoor data from digital building models selectively available\nfor the population of city models, map and navigation services  (in the form of\n2,5 D floorplans).\n\nTowards this end, we are developing a platform to check building models whether\nthey are suitable and contain required data, extract selected and simplified\nindoor data and convert it into various formats: CityGML LOD0 (Indoor),\nIndoorGML and OSM Indoor.  As input we rely on data in the format IFC (Industry\nFoundation Classes), the most widespread standard format for digital building\nmodels.  Indoor OSM, in particular geometry with Simple Indoor Tagging, is one\nof the various extraction targets. The data created may not be directly fed\ninto OpenStreetMap, but serve as a viable base for further mapping.\n\nThere are already older solutions, e.g. BIMServerOsmSerializer\n(\u003chttps://github.com/BIMDataHub/BIMServerOsmSerializer\u003e), which are only built\nfor a version of IFC, which has been a long time standard version, but\ncurrently approaches towards its end of life: IFC2x3.  There are also solutions\nunder active development, e.g. the JOSM plugin \"Indoor Helper\"\n(\u003chttps://wiki.openstreetmap.org/wiki/JOSM/Plugins/indoorhelper\u003e), which,\nhowever, lack some general approach on the IFC side and coverage of the\nheterogeneous options to represent geometry in the IFC schema.  With this\nresearch and development we aim to provide a workflow and software to\nsystematically access floorplan data in IFC.\n\n\n# Methodology\n\nWe start from both ends of integration by looking at the detailed structures of\nthe source and target models in parallel.\n\nFrom the group of target models, we derive a common model, which will have, at\nbest, near-trivial mappings to OSM Indoor, CityGML, IndoorGML. Although not\nstrictly necessary for the IFC-to-OSM conversion case or any other bilateral\nintegration, the intermediate model will not only allow to tackle integration\nof IFC with multiple targets besides OSM, but also integration of OSM with\nmultiple sources besides IFC.\n\nNext, we identify relevant information in the source model. IFC exposes a wide\nvariety of geometry modelling constructions from CAD software, mainly following\nthe modelling paradigm of constructive solid geometry (CSG). So far, we found\nthe following principle representation options:\n\na) Direct floorplan representation in 2.5D: Here we have 2D representations\n   located in 3D space, usually located at the level of the floor finish for a\n   particular storey. There are two versions to be distinguished: space\n   boundaries versus abstract representations of space-defining elements.\n\nb) Extraction from CSG: Spaces (as well as constructive elements) are often\n   represented as solids resulting from extrusion of a planar shape. If extruded\n   in z-direction, the base shape can be extracted and used as 2.5-D\n   representation.\n\nc) Projection onto floor level: If the geometry is not in CSG-form with\n   extrusions, but in BREP (boundary representation), then projection followed by\n   a simplification of the projection result is a possible way to extract.\n\nIn addition to the geometric elements, there are semantic elements connected to\nthe geometry that are connected themselves and can be used to charge the\ngeometric model elements with meaning. Depending on the geometry extraction\nmethod, correlation and consideration of semantic elements is more evident or\ncomplicated - hence possible to different degrees. The paper will discuss these\nimplications.\n\nAfter identification of the relevant entities, we are developing a three stage\nprocess for the actual population of target models from IFC.\n\n1. Building model enrichment: Information that can be represented in IFC will\n   be played back to the building model instead of being promoted to the generic\n   model only.\n2. Building to intermedite model: This essential step is coved with a flexible\n   rule-based mapping.\n3. Intermediate model to target models: Following a careful design of the\n   generic model, this step should be simple.\n\nWe are testing the processes with data from public buildings, two sets of\nuniversity campus buildings as well as one newly built municipal administration\ncentre. From assessment of the original building data, we will also develop\nmodelling and export guidelines for BIM software. As far as possible, the demo\ndata will be made available publicly as open data. More important, the\nconversion procedures will be published open source and a respective conversion\nservice will be offered online.\n\n\n# Discussion\n\nIn summary, our work provides practical benefit in terms of tools to support\nthe mapping process as well as a scientific contribution in terms of spatial\ndata integration and expert involvement via domain-specific languages.\n\nThe practical benefit of the conversion seems obvious: Building owners can\npublish data of their publicly accessible spaces to help with volunteer mapping\nwork. In the future we will also tackle update, checking and comparison with\nexisting OSM indoor data.\n\nScientific contributions are also made in different ways: First, an application\nscenario for the OGC Indoor Feature Model is provided and - interesting for the\naudience of this conference - evaluation of how OSM data fits with the\ngeneralized model. Further, we explore methods for flexible data integration\nwith domain specialist and expert community involvement. Finally, but beyond\nthe scope of this conference, the applicability of integration methods for\nbidirectional integration with  multiple sources and targets via intermediary\nformats is evaluated.","original_language":"eng","persons":["Helga Tauscher","Subhashini Krishnakumar"],"tags":["sotm2022","19570","2022","OSM","OpenStreetMap"],"view_count":99,"promoted":false,"date":"2022-08-21T14:05:00.000+02:00","release_date":"2022-10-14T00:00:00.000+02:00","updated_at":"2026-01-26T01:00:07.569+01:00","length":319,"duration":319,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19570-b55fbb53-3993-525c-8296-d1984573c4b5.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19570-b55fbb53-3993-525c-8296-d1984573c4b5_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19570-b55fbb53-3993-525c-8296-d1984573c4b5.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19570-b55fbb53-3993-525c-8296-d1984573c4b5.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19570-floor-plan-extraction-from-digital-building-models","url":"https://api.media.ccc.de/public/events/b55fbb53-3993-525c-8296-d1984573c4b5","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"d46319a7-2e37-5f5e-804b-109fa153a120","title":"Automated derivation of public urban green spaces via activity-related barriers using OpenStreetMap.","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19554-automated-derivation-of-public-urban-green-spaces-via-activity-related-barriers-using-openstreetmap-","link":"https://2022.stateofthemap.org/sessions/ASADTB/","description":"Urban green spaces serve people for active and passive recreation. On the basis of OpenStreetMap data, suitable green spaces are to be derived in order to incorporate them as recreation destinations in a location-based service (the “meinGruen” app) as polygons. The modelling approach focuses on activity-related barriers in the context of urban green, transitions between different land use classes, and public accessibility. The case study was implemented for the city of Dresden in Germany.\n\nIn addition to important ecosystem services such as clean air or local climate regulation, green spaces provide peace and recreation, contributing to a good quality of life for the population. In high-density urban areas, publicly accessible green spaces are used for a variety of recreational activities, which has become even more important, not least because of the COVID-19 pandemic [1]–[4]. In this context, the research project \"Information and Navigation on Urban Green Spaces in Cities - meinGruen\" examined publicly accessible green spaces with regard to a variety of criteria in order to assess their suitability for the pursuit of leisure activities, such as going for a walk or playing soccer [3], [5], [6]. The aim of this study is to derive a suitable polygon dataset to describe the spatial distribution of publicly accessible urban green spaces. The presented approach favors the use of OpenStreetMap data and intrinsic knowledge. Advantages of the use of OpenStreetMap data are the global availability, the often high completeness in urban areas as well as the unified open data license ODbL 1.0. In this way, problems with data availability and heterogeneity due to different responsible authorities can be avoided. Ludwig et al. [7] describe an approach to mapping public green spaces based on OpenStreetMap and Sentinel-2 satellite imagery in which barriers and land use changes are considered based on a priori (expert knowledge) assumptions for polygon generation. In the approach presented here, spatial delimitation is to be refined by describing barriers by probability values. The term \"barrier\" is first analyzed in an interdisciplinary way in order to then work out its meaning for the spatial delimitation of a green space. Here, barriers describe the action space of a recreational activity. While there are a number of object types (such as walls, fences, rivers, roads or railroad lines) can be assumed to be barriers with certainty, there are others (such as paths or the change of land use) for which knowledge is still lacking. The study area includes the city of Dresden in Germany, plus a buffer of five kilometers. OpenStreetMap represents the main data source. For training and validation, official cadastral data (ALKIS) as well as a dataset on cadastral parcels owned by the city of Dresden were used.\nThe methodology consists of six steps: First, according to defined rules, types of barriers were extracted from OpenStreetMap data. Second, we derived a land use layer without overlaps and holes from OpenStreetMap. Here, two options were compared regarding different target schemes for land use classification. Third, a mapping in terms of a “ground-truth“ in selected areas in Dresden followed in order to be able to evaluate the existence of a barrier on site for the extracted paths and changes of land use. Fourth, generic probabilities for the existence of a barrier were determined based on path type or land use change type. Fifth, a polygon mesh was created by applying thresholds to the determined barrier probabilities. Sixth, the generated polygons were enriched with attributes on the number of green space-related POI, such as benches, trash cans, or trees. Models for \"greenness\" and \"accessibility\" are thereby trained.\nFor the technical implementation mainly Docker, PostgreSQL/ PostGIS, Python (Geopandas, Scikit-Learn) and Jupyter Notebook were used. Data import was performed by osm2pgsql and ogr2ogr. For mapping we used the app QField.\nLand use layers were successfully generated for the study area using a residual class. The results indicated that the land use classification according to the area scheme of the IOER-Monitor (option B) has a higher thematic accuracy with a maximum of 33 classes (433 original OSM tags were assigned) than the option A based on a classification according to osmlanduse.org/ Schultz et al. (up to 13 classes, based on 61 OSM tags) [8], [9]. The classes of arable land (A: 28.40% / B: 28.06% share of area) as well as forest (A: 21.81% / B: 23.33%) are dominant in both variants. While the residual class takes up 6.29% of the area in option A, it is only 4.88% in option B. For the “ground-truth”, a total of approximately 82.3 km of paths (with 408 line objects) and approximately 64.2 km of land use changes (1720 line objects) were evaluated for the presence of a barrier in two selected areas in Dresden. The land use changes are based on variant B. Data were collected on 61 different land use transitions and four different trail types. While bike lanes can be safely assumed to be a barrier, the \"track\" (96.8%), \"footway\" (92.7%), and \"path\" (86.0%) trail types have a slightly lower barrier probability. Among land use transitions, the forest-meadow (12.6%), meadow-sports facility (22.8%), meadow-park (24.6%), and forest-grassland (26.7%) transitions have the lowest barrier probabilities. Together with the barriers assumed to be safe at the beginning, a line pool is formed, from which different polygon meshes are generated based on different intervals for the barrier probability (p ≥ 0%; p ≥ 20%; p ≥ 40%; p ≥ 60%; p ≥ 80%; p = 100%). The lower the probability threshold, the higher the number of polygons created (whose area decreases). For the \"accessibility\" model, the number of benches, trash cans, public toilets and public internet were considered per polygon. The logistic regression achieved 76.7% accuracy here, similar to a Support Vector Classifier (SVC). The \"greenness\" model is based on number of benches, picnic tables, trees, and trash cans per polygon. The accuracy is about 92.3% (for logistic regression and also Support Vector Classifier). \nThis work successfully demonstrates a new approach to derive publicly accessible green spaces based on OpenStreetMap data considering different qualities of barriers in contact of green spaces.  Based on the examined barrier probability of path types and land use transitions, more realistic spatial delineations of green spaces were made possible. The chosen approach is globally applicable due to the use of OpenStreetMap. In each case, locally prevailing climatic and cultural influences must be taken into account. The knowledge collected here can be applied in the Central European region. For other areas, a renewed “ground-truth” may have to be carried out on site. The schematic transformation of the land use into the area scheme of the IOER-Monitor leads to a reduction of classes compared to the original data. In addition to benefits in capturing barrier probability, it also simplifies comparability and transferability. Thus, other data could also be migrated into this scheme. The determined barrier probabilities correspond to the expectations. The polygon generation based on different barrier probabilities allows here a differentiated setting of the desired action space for the relevant leisure activities. The quality of the trained models is good, but can be improved. A variety of additional features can be calculated for each potential green space (polygon), such as path network density or density of path network intersections (see also Ludwig et al. [7]). Questions about the perception and use of green spaces can also be part of interdisciplinary research in the future.","original_language":"eng","persons":["Theodor Rieche"],"tags":["sotm2022","19554","2022","OSM","OpenStreetMap"],"view_count":48,"promoted":false,"date":"2022-08-21T11:30:00.000+02:00","release_date":"2022-10-15T00:00:00.000+02:00","updated_at":"2025-10-16T19:00:04.563+02:00","length":1580,"duration":1580,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19554-d46319a7-2e37-5f5e-804b-109fa153a120.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19554-d46319a7-2e37-5f5e-804b-109fa153a120_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19554-d46319a7-2e37-5f5e-804b-109fa153a120.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19554-d46319a7-2e37-5f5e-804b-109fa153a120.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19554-automated-derivation-of-public-urban-green-spaces-via-activity-related-barriers-using-openstreetmap-","url":"https://api.media.ccc.de/public/events/d46319a7-2e37-5f5e-804b-109fa153a120","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"284d9435-df01-5d03-9b0b-695240788571","title":"Lightning talks V","subtitle":null,"slug":"sotm2022-21683-0-lightning-talks-v","link":"https://2022.stateofthemap.org/sessions/HLTKHD/","description":"Pre-recorded lightning talks submitted in advance by people around the world.\n\n## Our Top 10 Data Quality Aspects\n_Samson Ngumenawe_\n\n[HOT](https://www.hotosm.org/) works with a number of organizations and communities that generate data through different contribution mechanisms, including desktop digitization, data collection surveys and local knowledge experiences. Each of these mechanisms lead to different aspects and issues that affect the quality of data contributed to OSM. We are prioritizing on minimizing the impacts of the top 10 aspects on the list through a number of ways, including mapper outreach programs, training, and developing technical tools.\n\n## Community Mapping Initiatives in Tanga\n_Antidius Kawamala_\n\nPromoting a data-driven decision making culture in Tanga under living lab initiative, we are complementing the global objective of OSM i.e. to create a free editable geographic database of the world by various mapping activities around the region. The initiative uses local member by building capacity on how to use OSM and contributing to it as well as University Students. These mappings have been putting the missing pieces of the community on the map and addressing challenges they face so far. The usage of GIS data as a tool for community as a decision-making data tool can help in timely action and on-time problem-solving.\n\n## Mapping all the worlds open data.\n_Christopher Brown_\n\nA showcase of how at mapstack we are building on top of OSM to create an ecosystem that will host all of the worlds open data as free to use web maps.\n\n## Route shields of the world in OpenStreetMap-Americana\n_Clay Smalley_\n\n[OpenStreetMap-Americana](https://wiki.openstreetmap.org/wiki/OpenStreetMap_Americana) is an alpha-stage OpenStreetMap renderer intended to resemble a North American road atlas. Despite the focus on the United States, OSM-Americana has had contributions from people all over the world interested in adding support for highway route shields of their home countries. See a showcase of these contributions, and find out how you can put your country on the map.\n\n## Bad or Good OpenStreetMap, What and how do you map?\n_Enock Seth Nyamador_\n\nOpenStreetMap editing is all welcoming, diverse and very forgiving but this leading to issues. Many of these issues which should not be occurring in the first place but happens. This is a quick talk giving you the tip of what I have come across in my short term as an OSM Contributor and why we should follow best practices.\n\n## OSGeoLive: Your Open Source Geospatial toolbox\n_Enock Seth Nyamador_\n\n[OSGeoLive](https://live.osgeo.org) is a self-contained bootable DVD, USB thumb drive or Virtual Machine based on Lubuntu, that allows you to try a wide variety of open source geospatial software without installing anything. It is composed entirely of free software, allowing it to be freely distributed, duplicated and passed around. It provides pre-configured applications for a range of geospatial use cases, including storage, publishing, viewing, analysis and manipulation of data. It also contains sample datasets and documentation. OSGeoLive is an [OSGeo](https://wiki.openstreetmap.org/wiki/OSGeo) project used in several workshops and cases at FOSS4Gs and around the world.\n\n## AI4Mapping: Earth Observation Data for Rapid Map Generation and DRRM\n_Neyzielle Ronnicque Cadiz_\n\nThe Remote Sensing and Data Science: DATOS Help Desk by the Philippines' Department of Science and Technology- Advanced Science and Technology Institute builds on initiatives on disaster mitigation by providing a help desk pre-, during, and post-disaster events. The team provides remote sensing and data science applications support to critical activities on disaster mitigation, analysis, and advice. The project developed the Artificial Intelligence for Earth Observation (AI4EO) initiative to complement the DRRM efforts of mandated agencies in the Philippines with the goal of rapid map generation for disaster risk reduction and emergency response using earth observation and OpenStreetMap data.\n\n## I Map Tallinn\n_Ilya Zverev_\n\nA video installation about Tallinn and Every Door editor.\n\n## OSM checks and completeness estimations with Disaster Ninja\n_Pavel Pashagin_\n\nDisaster Ninja is an open-source tool that helps the Humanitarian OpenStreetMap Team search for unmapped areas when a disaster strikes. It can help to estimate the coverage of OSM data, and check if OSM data is up-to-date and consistent for population data.\n\n## Women Participation in OpenStreetMap\n_Nadaraj Saranya_\n\n\n\n## OpenStreetMap in Geographic Information Science\n_Rajendean Keerthana_\n\n## State of the Map Asia 2022 Trailer Video\n_Mikko Tamura_\n\nA 1-minute teaser video about State of the Map Asia 2022.","original_language":"eng","persons":["Various Speakers"],"tags":["sotm2022","21683","2022","OSM","OpenStreetMap"],"view_count":80,"promoted":false,"date":"2022-08-19T16:30:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2025-01-08T20:45:06.540+01:00","length":3743,"duration":3743,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/21683-284d9435-df01-5d03-9b0b-695240788571.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/21683-284d9435-df01-5d03-9b0b-695240788571_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/21683-284d9435-df01-5d03-9b0b-695240788571.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/21683-284d9435-df01-5d03-9b0b-695240788571.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-21683-0-lightning-talks-v","url":"https://api.media.ccc.de/public/events/284d9435-df01-5d03-9b0b-695240788571","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"19c9a8fd-0c94-5762-b541-bdfe1ef186f4","title":"Mapping a Small Town","subtitle":null,"slug":"sotm2022-18447-mapping-a-small-town","link":"https://2022.stateofthemap.org/sessions/SA77RH/","description":"Christopher Beddow takes us on a journey of how to map a small town using a variety of tools. The goal was to test and prove how much OpenStreetMap can be enriched using RapiD buildings and roads, and Mapillary map features, traffic signs, and imagery.\n\nHe evaluates a small town in the western United States that is far from any mapping community and has very little data, and demonstrates how a vivid dataset can be added to OSM. In addition, he compares this to a small town in Switzerland, demonstrating how new details can still be added to a place that is heavily mapped by a strong local community.\n\nMapping is a time consuming task, and challenging for an individual to do without a community or a team. However, many tools exist to enhance the capabilities of any lone map contributor, and also multiply the mapping power of a team. \n\nChristopher Beddow takes us on a journey of how to map a small town using a variety of tools. The goal was to test and prove how much OpenStreetMap can be enriched using RapiD buildings and roads, and Mapillary map features, traffic signs, and imagery.\n\nHe evaluates a small town in the western United States that is far from any mapping community and has very little data, and demonstrates how a vivid dataset can be added to OSM. In addition, he compares this to a small town in Switzerland, demonstrating how new details can still be added to a place that is heavily mapped by a strong local community.\n\nLeaving this talk, you will understand how to use RapiD, Mapillary, and your own creativity to adapt your own locality into a comprehensive part of OpenStreetMap.","original_language":"eng","persons":["Christopher Beddow"],"tags":["sotm2022","18447","2022","Mapping","OSM","OpenStreetMap"],"view_count":196,"promoted":false,"date":"2022-08-19T16:30:00.000+02:00","release_date":"2022-09-24T00:00:00.000+02:00","updated_at":"2025-10-14T16:15:04.814+02:00","length":1531,"duration":1531,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18447-19c9a8fd-0c94-5762-b541-bdfe1ef186f4.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18447-19c9a8fd-0c94-5762-b541-bdfe1ef186f4_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18447-19c9a8fd-0c94-5762-b541-bdfe1ef186f4.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18447-19c9a8fd-0c94-5762-b541-bdfe1ef186f4.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18447-mapping-a-small-town","url":"https://api.media.ccc.de/public/events/19c9a8fd-0c94-5762-b541-bdfe1ef186f4","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"d1dacee8-15c8-56d5-800f-2910f28f3d9d","title":"OpenStreetMap data for climate change response initiatives","subtitle":null,"slug":"sotm2022-18477-openstreetmap-data-for-climate-change-response-initiatives","link":"https://2022.stateofthemap.org/sessions/N9VWB9/","description":"We would like to share our feedback on facilitating OSM participatory mapping workshops for climate change adaptation projects with OSM local communities \nSince 2015, CartONG has been expanding its work into the domain of participatory mapping, first by partnering with the international Missing Maps project, and then by working to develop a wider set of tools and methodologies. \nOver the last 12 months, we had the opportunity to support two climate change adaptation projects, one in Tajikistan for the German Corporation for International Cooperation (GIZ) and the other in Madagascar in the Morombe region for Secours Islamique France.\n\nFor these projects, we used geographic data and open source technologies, especially OpenStreetMap, and based on a participatory approach by mobilizing and fully involving local communities. We also worked to promote cooperation between the different actors in the region. \nAt this conference, we want to share our field experience and approaches to increase the production and availability of geographic data in the concerned regions and initiate new groups of local contributors to enhance and maintain the OpenStreetMap database.","original_language":"eng","persons":["Luc Kpogbe"],"tags":["sotm2022","18477","2022","Mapping","OSM","OpenStreetMap"],"view_count":75,"promoted":false,"date":"2022-08-19T15:00:00.000+02:00","release_date":"2022-09-24T00:00:00.000+02:00","updated_at":"2026-01-18T21:15:15.604+01:00","length":1331,"duration":1331,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18477-d1dacee8-15c8-56d5-800f-2910f28f3d9d.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18477-d1dacee8-15c8-56d5-800f-2910f28f3d9d_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18477-d1dacee8-15c8-56d5-800f-2910f28f3d9d.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18477-d1dacee8-15c8-56d5-800f-2910f28f3d9d.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18477-openstreetmap-data-for-climate-change-response-initiatives","url":"https://api.media.ccc.de/public/events/d1dacee8-15c8-56d5-800f-2910f28f3d9d","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"d1b51263-8029-50b1-89fe-435d608e4068","title":"10 Years iD Editor – The Road Ahead","subtitle":null,"slug":"sotm2022-18513-10-years-id-editor-the-road-ahead","link":"https://2022.stateofthemap.org/sessions/C9AKZK/","description":"The last 10 years were quite turbulent for the iD editor: After the initial idea from Richard Fairhurst was quickly picked up by a developer team at Mapbox, the editor became OSM’s default map editor almost exactly 9 years ago today. Since then, different Maintainers have managed the project, constantly enhancing its functionality and data models like iD’s built in tagging presets.\n\nThis talk will present a condensed overview of the evolution of the iD editor since 2012 and, more importantly, showcase what still lies ahead of it: Small and large improvements to the user interface, performance, data validation, customization, integration of external services and more.\n\nAlmost exactly 9 years ago, the iD editor became OSM's default editor. But its core idea was first presented even a year before that.\n\nLooking back at the past 10 years history shows that the project has undergone quite a few different phases: The initial sparks and the coining of the name by Richard Fairhurst were followed up shortly by a rapid development phase by a team of Mapbox engineers resulting in iD being ready to stand in as OSM’s default map editor. Since then, different Maintainers have managed the project, constantly enhancing its functionality and data models like iD’s built in tagging presets..\n\nIn the last couple of years, the iD editor has also seen some usage outside of the main OSM website, for example in the form of Forks like the RapiD editor, advancing the applicability in their own specialized use cases and scenarios.\n\nThis talk will present a condensed overview of the development of the iD editor since 2012 and, more importantly, showcases what still lies ahead of it: Small and large improvements to the user interface, performance, data validation, customization, integration of external services and more. All with the goal to make iD fit for next 10 years and more to come.","original_language":"eng","persons":["Martin Raifer"],"tags":["sotm2022","18513","2022","User Experiences","OSM","OpenStreetMap"],"view_count":114,"promoted":false,"date":"2022-08-20T12:00:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2025-12-07T15:30:08.964+01:00","length":1428,"duration":1428,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18513-d1b51263-8029-50b1-89fe-435d608e4068.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18513-d1b51263-8029-50b1-89fe-435d608e4068_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18513-d1b51263-8029-50b1-89fe-435d608e4068.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18513-d1b51263-8029-50b1-89fe-435d608e4068.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18513-10-years-id-editor-the-road-ahead","url":"https://api.media.ccc.de/public/events/d1b51263-8029-50b1-89fe-435d608e4068","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"3df78372-0001-5659-b021-a2bddc10af6d","title":"OpenStreetMap in the Cloud","subtitle":null,"slug":"sotm2022-18354-openstreetmap-in-the-cloud","link":"https://2022.stateofthemap.org/sessions/HEHHCH/","description":"The geospatial world is moving to cloud-first and cloud-native approaches. Movements like STAC and COG have transformed how people use raster data in the last couple of years. OpenStreetMap has much to gain from thinking about different cloud infrastructure architectures. This talk will discuss what it takes to run the OpenStreetMap ecosystem in the cloud and present the history and work on a project called [OSM Seed](https://github.com/developmentseed/osm-seed/). We learned so much while building OSM Seed and think it can be a blueprint for running OpenStreetMap on cloud infrastructure.\n\nThe OpenStreetMap ecosystem contains many open source software projects beyond the Rails application. This includes tools that are used by mappers on a daily basis, like, Overpass, Tasking Manager, iD, JOSM, tile servers, data processing applications, Nominatim and so on. It’s a complex ecosystem of growing tools. These are maintained by different individuals and organisations. OSM Seed started in 2018 after several attempts to containerise OSM software for easy installation. [I talked about this vision](https://2015.stateofthemap.us/openstreetmap-software-for-more-than-openstreetmap) in 2015 at State of the Map US. Since then, OSM Seed has grown to be a mature and heavily tested software project that now powers projects like OpenHistoricalMap and data projects at Humanitarian OpenStreetMap Team.\n\nWe learned so much while building OSM Seed and think it can be a blueprint for running OpenStreetMap on cloud infrastructure. OSM Seed uses cloud agnostic approaches through Kubernetes to bring together projects that make up what OSM is today. These projects continue to get updates and are maintained how they always have been. OSM Seed provides an interface to bring the ecosystem a little closer through better infrastructure orchestration.\n\nIn this talk, I’d like to discuss a brief history of OSM Seed, some of our lessons trying to move OSM software to the cloud, and open a conversation on what could be useful for OSM going forward.","original_language":"eng","persons":["Sajjad Anwar"],"tags":["sotm2022","18354","2022","Software Development","OSM","OpenStreetMap"],"view_count":148,"promoted":false,"date":"2022-08-20T09:30:00.000+02:00","release_date":"2022-09-25T00:00:00.000+02:00","updated_at":"2026-01-23T14:15:10.478+01:00","length":1383,"duration":1383,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18354-3df78372-0001-5659-b021-a2bddc10af6d.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18354-3df78372-0001-5659-b021-a2bddc10af6d_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18354-3df78372-0001-5659-b021-a2bddc10af6d.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18354-3df78372-0001-5659-b021-a2bddc10af6d.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18354-openstreetmap-in-the-cloud","url":"https://api.media.ccc.de/public/events/3df78372-0001-5659-b021-a2bddc10af6d","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"68d59b0e-d536-5bef-88a0-fab85c32d3b0","title":"Leveraging OpenStreetMap to investigate urban accessibility and safety of visually impaired pedestrians","subtitle":null,"slug":"state-of-the-map-2022-academic-track-18818-leveraging-openstreetmap-to-investigate-urban-accessibility-and-safety-of-visually-impaired-pedestrians","link":"https://2022.stateofthemap.org/sessions/MXS9R8/","description":"Cities worldwide encourage urban active mobility by advocating policy and planning. Although contribution is evident, in practice, these actions disregard population parts that have mobility impairments. This research suggests using OpenStreetMap data in customized analytical models to assess the accessibility level of the urban environment for visually impaired pedestrians. Models results show the existence and spatial distribution of existing accessibility problems, including challenging street network connectivity and dangerous walking areas. These models can be used to enable decision makers, city stakeholders and practitioners to enrich management, monitoring and development of their cities, and support sustainable, livable lifestyles and walkability equality.\n\nMany efforts that include city policy and planning strategies are implemented to encourage urban active mobility. The outcome of these actions is measured by how transportable and accessible the city is. Although contribution is evident, in practice, the commonly used measures mostly disregard a huge part of the population that have mobility impairments, which require specific accessibility needs, preventing them to be an equal part of the sustainable city vision.\n\nThis research suggests using OpenStreetMap (OSM) data in customized analytical models to assess the accessibility level of the urban environment for visually impaired pedestrians. In principle, the models analyze the city on two levels: routing and accessibility. These are evaluated, correspondingly, based on possible routes, e.g., how long the optimal route is for visually impaired pedestrians compared to the shortest one, and on area, e.g., what is the overall accessibility and safety of a predefined urban extent. The play of both measures enables us to quantify the level of mobility and accessibility of the analyzed city. To do so, we implement the following steps:\n1.\tWe examine the navigation preferences of visually impaired pedestrians in the urban space. This allows a better understanding of the various environmental and morphological factors and characteristics of the urban form that promote safe and accessible navigation. These are translated into spatial and temporal criterion: a) Way Type, which quantifies how suitable the path is in terms of usage and safety; b) the existence of Vision Impairment Assistive Landmarks that support safe wayfinding and navigation; c) Way Complexity, which measures the level of linearity of the path; and d) Crowdedness, which measures the overall pedestrian traffic volume.\n2.\tWe transform OSM’s street network into a weighted graph, where for each graph edge we calculate the cost according to the above criteria. Cost is derived from segments that facilitate safe and accessible walking for visually impaired pedestrians (e.g., separated sidewalks and straight paths), and segments that hinder safe and accessible walking for visually impaired pedestrians (e.g., shared and overcrowded streets).\n3.\tWe develop three analytical models that measure the accessibility level of the urban environment for visually impaired pedestrians: a) street-based, which relies on averaging the costs of all graph edges for a given area, hence it can be implemented for different urban levels (spatial extents); b) centrality-based, which adds on the street-based the centrality indices betweenness and closeness that consider the significance of each graph edge in the street network in respect to all other edges (high centrality values mostly signify streets that attract large pedestrian traffic flow); c) route-based, a navigational method, in which numerous routes are generated on the graph for location tuples, and then the weight ratio of the optimal route for visually impaired pedestrians and the shortest route (commonly used for seeing pedestrians) is evaluated. The smaller the weight value, the more accessible the route.\n\nThe developed models are evaluated for Greater London, the UK. 33 boroughs with their wards are analyzed, resulting in processing 421,107 streets, 377,164 OSM nodes and 634, 871 OSM ways. Results show the existence and spatial distribution of accessibility problems for visually impaired pedestrians. The street-based model highlights the fact that urban nature and green spaces, which are typically considered as contributing to wellbeing and encourage walking, are less accessible for visually impaired people, mostly due to the existing road types, e.g., gravel and dirt roads or shared spaces (bikes and pedestrians that share the same path), which are less accessible for this population. The centrality-based model shows that central streets are mostly more accessible, meaning that borough centers are considered in general as accessible, but as distance from city centers grows, the urban environment becomes less accessible. The route-based model, where more than 1,500,000 routes (with length shorter than 1,000 meters) were calculated, showed that on average the optimized routes are 11% longer and 17.5% more accessible than the shortest ones. Some optimal walking routes are twice as long as the shortest ones, where some impose safety issues that critically endanger visually impaired pedestrians. Wards that have a large proportion of street segments with poor accessibility evenly distributed throughout the ward tend to show less efficient route planning in terms of optimal routes that are considerably longer. In general, the route-based model produces clearer results to understanding the city’s morphology in terms of accessibility for visually impaired pedestrians.\n\nTo a large extent, these models depend on the quality of OSM data, such that feature completeness and tag correctness should be investigated. In terms of completeness, we found that sidewalks and crossings, which are two important model features, are not always mapped in OSM, mostly in the outskirts of London. One solution is to use learning methods and prediction models to complete missing data. In terms of tag correctness, we found that some inconsistencies exist with certain tags. One solution can be to make tag definitions in, e.g., OSM Wiki, more inclusive and clear, with a focus on accessibility aspects.\n\nResults show how various accessibility levels for visually impaired pedestrians might be assessed and where they are found in the city, pointing to the existing problems this community faces today when navigating. These include challenging street network connectivity and dangerous walking areas. The results also demonstrate that the current practice of urban planning and design worldwide still suffers from lack of democratization, limiting the mobility and navigation of certain groups. The accessibility models developed in this research can be used for better city planning and design, enhancing the city mobility and walkability equality and improving quality of life for these vulnerable road users. Our findings provide analytical tools to enable decision makers, city stakeholders and practitioners to enrich management, monitoring and development of their cities, and support sustainable, livable lifestyles and walkability equality. These, in turn, will ease navigation and mobility of visually impaired pedestrians, overall improving health outcomes and their integration into society.","original_language":"eng","persons":["Sagi Dalyot"],"tags":["sotm2022","18818","2022","OSM","OpenStreetMap"],"view_count":49,"promoted":false,"date":"2022-08-21T14:10:00.000+02:00","release_date":"2022-10-14T00:00:00.000+02:00","updated_at":"2025-11-25T09:15:05.145+01:00","length":322,"duration":322,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18818-68d59b0e-d536-5bef-88a0-fab85c32d3b0.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18818-68d59b0e-d536-5bef-88a0-fab85c32d3b0_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18818-68d59b0e-d536-5bef-88a0-fab85c32d3b0.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18818-68d59b0e-d536-5bef-88a0-fab85c32d3b0.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-18818-leveraging-openstreetmap-to-investigate-urban-accessibility-and-safety-of-visually-impaired-pedestrians","url":"https://api.media.ccc.de/public/events/68d59b0e-d536-5bef-88a0-fab85c32d3b0","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"48d7305f-11f4-5ac8-90e3-4395a005201f","title":"OSM for sustainable transport planning","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19448-osm-for-sustainable-transport-planning","link":"https://2022.stateofthemap.org/sessions/CEMMTQ/","description":"OpenStreetMap (OSM) data has the potential to facilitate bottom-up approach to transport planning which is essential for localized data-driven policy interventions. Given this, OpenInfra project is exploring the potential of OSM data in transport research with a focus on active travel. The exploration showed that currently missing data limits the applicability of OSM data. Nevertheless, we argue that the potential and relevance of OSM data can be demonstrated by recategorizing OSM data to provide more actionable insights to policy-makers. This, therefore, could encourage the uptake of open data leading to more transparent, reproducible, and participatory transport planning.\n\nOne of the key domains in which OpenSteetMap (OSM) data has been utilized is transport research [1]. OSM has been used in agent-based transport simulation [2] and routing [3], including cycling [4], walking [5], wheeling [6], and blind pedestrian routing [7]. Another application of OSM data is in transport infrastructure planning. Nelson et al. [8] argue that OSM has the potential to become a primary source of data on infrastructure across the globe. \n\nRegardless of OSM’s potential to become a primary source of data on infrastructure, its potential in active travel infrastructure planning is yet to be realized. One of the potential reasons behind this lag might be linked to the perceived unreliability of open-access crowdsourced data [9]. The quality of OSM has received extensive examination [1] in which the question concerning data completeness plays a significant role because, it is argued, the mappers are not coordinated to guarantee systematic coverage [10]. To address this issue, Barrington-Leigh and Millard-Ball [11] assessed OSM road completeness and found that globally over 80% of roads are mapped. Problematically, however, their assessment focused on roads designed for motor traffic, thus excluding other modes of transport. This gap has been partially addressed by Ferster et al. [12]who examined and compared OSM cycling infrastructure in Canada. They have not, however, considered the infrastructure from the perspective of accessibility. Moreover, there seems to exist no equivalent study using OSM data in the context of pedestrian infrastructure planning. \n\nNevertheless, open-access crowdsourced data, such as OSM, can support an increasing need for local evidence to inform transport policies. This is important in the context of the UK in which a shift from provision for motorised modes towards more sustainable active modes of travel, such as walking, wheeling, and cycling, takes place [13]. The importance of localizing interventions to meet the needs of local communities has been outlined in both policy [15] and academic [16] papers. A potential way to engage citizens in the decision-making is to encourage “produsage” – a model in which citizens both produce and use data [17]. \n\nAcknowledging the potential of OSM to boost citizen participation, OpenInfra project, run at the University of Leeds (UK), aims to address the gap of literature regarding the potential OpenStreetMap in transport research. The project started by examining the existing OSM tags relevant to active travel infrastructure in England with a focus on West Yorkshire, Greater Manchester, Greater London, and Merseyside. The data has been queried using osmextract [18], a package in R, and explored using exploratory data analysis (EDA) approach. A reproducible code containing all the figures discussed here can be found on GitHub: https://github.com/udsleeds/openinfra/tree/main/sotm2022 \n\nGiven the extensive use of OSM data in transport research, it is not surprising that OSM provides a comprehensive active travel network, yet there is a lack of specification concerning the type of infrastructure that is present (e.g. is it a cycle lane or a cycle track?). For instance, cycleways and footways constitute about 1/3 of all the mapped highways on which one can legally walk, wheel or cycle but only a few percent of the cycleways and footways have tags detailing their type. The data gets even scarcer in the context of accessible infrastructure planning. For example, there is a lot of missing information on the presence and type of kerbs – a street element that might make the movement of a wheelchair user more challenging [19]. \n\nThe missing data currently limits the use of OSM data in active travel planning, however this does mean that the use of OSM data should be dismissed. Following Nelson et al.’s [8] argument that it is important to make crowdsourced data more actionable, we decided to recategorize OSM data based on Inclusive Mobility (IM) [15], a guide that outlines the best practices in creating inclusive pedestrian infrastructure in the UK. For this, a function has been written (documentation can be found here: https://udsleeds.github.io/openinfra/articles/im_get.html). It takes an OSM dataframe, recategorizes its tags based on the definitions outlined in the guide, and returns an OSM dataframe with new columns to use in further analysis. However, the function provides a simplification of the IM guide for a couple of reasons. The first one could be considered in terms of definitional discrepancies. For instance, the guide defines footways as “pavements adjacent to roads”, yet this is not easily extracted from the OSM in which highway=footway is a generic tag and often there is no further refinement (e.g., sidewalk=*) to determine if it is a pavement adjacent to a road. Another reason is linked to assigned values. For example, the guide identifies six tactile paving surfaces but OSM focuses on the presence/absence of tactile paving, thus limiting how much information can be extracted from the data. \n\nOne potential application of the IM function could be to explore the existence and geographic distribution of accessibility indicators, such as the presence of a flush kerb. Yet, more interesting results can be produced by using recategorised OSM data in conjunction with other datasets that would help to improve the understanding of the accessibility of streets. As an illustration for this, an open-access Leeds Central Council Footfall data was used [20]. We reasoned that the locations at which footfall data were collected are heavily used by pedestrians, thus demonstrating the need to ensure inclusive spaces. 5 unique streets were identified, which resulted in 35 linestrings in OSM. Then, a basic index of accessibility, ranging from 0 to 5, was created. For example, if a linestring is classified as a footway, footpath, or implied footway based on the IM guide, then it received 1, otherwise 0. If a flush kerb is mapped, it received 1, otherwise (e.g., not flush or NA), 0 is given. Finally, the values were added and a final index produced. Following this, the highest index score is 2 (19 linestrings), while the rest scored 1. This example does not necessarily show that the streets are inaccessible because the missing data make it hard to make a fair judgement (e.g., in this case not a single linestring has data on kerbs). However, we would argue that this is a space for OSM to produce more readily actionable insights regarding transport infrastructure, especially if joined with other (open) datasets that would help to overcome some of its current data limitations. \n\nThe following steps of the OpenInfra project are focused on scaling up. The goal is to produce ‘OSM transport infrastructure data packs’ for transport authorities in England to support the uptake of open-access data, such as OSM, in transport planning. We believe that the utilization of open-access data could make transport planning more transparent, reproducible, and participatory which, consequently, would support an uptake of sustainable modes of travel. OSM specifically has the potential to provide localized insights on the existing transport infrastructure and facilitate more inclusive and accessible transport planning.","original_language":"eng","persons":["Greta Timaite","James Hulse","Robin Lovelace"],"tags":["sotm2022","19448","2022","OSM","OpenStreetMap"],"view_count":86,"promoted":false,"date":"2022-08-21T12:30:00.000+02:00","release_date":"2022-10-15T00:00:00.000+02:00","updated_at":"2025-12-28T11:45:13.789+01:00","length":1788,"duration":1788,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19448-48d7305f-11f4-5ac8-90e3-4395a005201f.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19448-48d7305f-11f4-5ac8-90e3-4395a005201f_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19448-48d7305f-11f4-5ac8-90e3-4395a005201f.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19448-48d7305f-11f4-5ac8-90e3-4395a005201f.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19448-osm-for-sustainable-transport-planning","url":"https://api.media.ccc.de/public/events/48d7305f-11f4-5ac8-90e3-4395a005201f","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"22ea6b83-4305-5f06-9996-b2f0e4a5408e","title":"Evolving the OSM Data Model","subtitle":null,"slug":"sotm2022-18479-evolving-the-osm-data-model","link":"https://2022.stateofthemap.org/sessions/W3AGY8/","description":"The OSM data model with its nodes, ways, and relations has done an amazing job for us over the years. It has seen very little changes since relations were introduced 15 years ago. But there are some real problems with the data model. With the experience of those 15 years behind us, its time to tackle some improvements.\n\nThis talk will outline the problems with the data model, show ideas for improvements, and discuss possible ways that can move us forward step by step.\n\nThe OSM data model is quite different from the \"Simple Feature\" data model used by most \"geo\" software. It has some great features, like the open tagging model, which has proven to be a great enabler for all sorts of innovations. But the data model also has its problems. Most often named are the missing area data type and the cumbersome geometry building needed for ways and relations based on their member nodes. This makes use of the OSM data more difficult, more expensive, and slower than it needs to be.\n\nThere has been some discussions on these topics over the years, mostly after my talk in 2018 at the SotM in Milano, but work on this has stalled. Recently the OSMF Engineering Working Group has taken up this topic and payed me to do a study on the problems with the data model and possible ways forward. This talk will present the findings and should jumpstart the discussions around this in the OSM community.","original_language":"eng","persons":["Jochen Topf"],"tags":["sotm2022","18479","2022","Data Analysis \u0026 Data Model","OSM","OpenStreetMap"],"view_count":259,"promoted":false,"date":"2022-08-19T15:30:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2026-01-24T15:15:13.356+01:00","length":1954,"duration":1954,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18479-22ea6b83-4305-5f06-9996-b2f0e4a5408e.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18479-22ea6b83-4305-5f06-9996-b2f0e4a5408e_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18479-22ea6b83-4305-5f06-9996-b2f0e4a5408e.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18479-22ea6b83-4305-5f06-9996-b2f0e4a5408e.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18479-evolving-the-osm-data-model","url":"https://api.media.ccc.de/public/events/22ea6b83-4305-5f06-9996-b2f0e4a5408e","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"f3da9307-52fe-59e9-b85e-8b043f37c0cb","title":"Pedestrian and Bike Mapping in New York City","subtitle":null,"slug":"sotm2022-18511-pedestrian-and-bike-mapping-in-new-york-city","link":"https://2022.stateofthemap.org/sessions/YAYNSB/","description":"This talk will cover the growing New York City OpenStreetMap community and our efforts at coordinating mapping our cities’ quirks into the OSM data model. New York City (and much of America) has sidewalks that end abruptly, intersections without proper pedestrian control, uncontrolled slip lanes, bike paths that lead into stairways, crossings without curb cuts. Mapping these features helps NYC pedestrians analyze conditions, report and advocate for changes.\n\nhe New York City community is interested in keeping track of pedestrian features throughout the five boroughs. Both to improve routing and to keep track of dangerous or poorly designed infrastructure. While NYC is required by law to create accessible  conditions, our sidewalk mapping today helps find areas that do not yet meet those standards.\n\nOpenStreetMap contains the most up to date resources for the city’s bike lane network, including planned projects. Discussions helped standardize when to draw bike lanes separated from the road lane (when there is a barrier) and coordinate responses to new construction (the race to survey the new Brooklyn Bridge bike lane.) While the city may consider a certain segment protected, OpenStreetMap’s “on the ground rule” brings our maps closer to reality.\n\nIn 2021 the community came together to focus on completing sidewalks in Flushing, Queens. In 2021 and 2022 the community mapped bicycle racks using fieldpapers. These efforts show how to organize support to improve a single area, and bring in new mappers with an activity focused on a single goal. Completed areas can then drive analysis.\n\nI will share examples of areas that have been mapped and where they brush up against the guidelines for bike and pedestrian mapping. I will also share analysis driven by this mapping. Finally there will be anecdotes having reported issues to the city and keeping track of progress with OpenStreetMap.","original_language":"eng","persons":["Ariel Kadouri"],"tags":["sotm2022","18511","2022","Mapping","OSM","OpenStreetMap"],"view_count":108,"promoted":false,"date":"2022-08-20T10:30:00.000+02:00","release_date":"2022-09-25T00:00:00.000+02:00","updated_at":"2025-12-18T10:00:08.101+01:00","length":1750,"duration":1750,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18511-f3da9307-52fe-59e9-b85e-8b043f37c0cb.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18511-f3da9307-52fe-59e9-b85e-8b043f37c0cb_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18511-f3da9307-52fe-59e9-b85e-8b043f37c0cb.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18511-f3da9307-52fe-59e9-b85e-8b043f37c0cb.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18511-pedestrian-and-bike-mapping-in-new-york-city","url":"https://api.media.ccc.de/public/events/f3da9307-52fe-59e9-b85e-8b043f37c0cb","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"decf8c0d-09b1-5527-80d1-f413272b8e94","title":"Comparative Integration Potential Analyses of OSM and Wikidata – the Case Study of Railway Stations","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19388-comparative-integration-potential-analyses-of-osm-and-wikidata-the-case-study-of-railway-stations","link":"https://2022.stateofthemap.org/sessions/YU9JHN/","description":"In this work, we present analyses using a series of comparative data insights that help to better understand the potential and implications of integration between knowledge graphs and OSM.\n\nOpenStreetMap(OSM) is one of the richest and most diverse sources of geographic information. However, it lacks a fundamental property vital for spatio-semantic analyses: hierarchical structure and semantic linkage. OSM provides links to existing knowledge graphs (structured data that conforms to a specific ontology) e.g., via the wikidata=* tags. The usage of these link-tags is currently limited to a small percentage of both OSM and Wikidata objects. Efforts were undertaken to enhance the geographic linking, linking nearby objects of the same type and semantic linking [1-3]. On the side of the hierarchical and semantic structuring of OSM, the WorldKG knowledge graph[4] provides a semantic mapping of a large subset of OSM. While the free and open OSM tagging scheme is a fundamental part of the OSM project that enabled its success, WorldKG overcomes the inherent lack of structure this tagging scheme represents, paving the way for a knowledge-graph integration of the OSM dataset. Still, open knowledge graphs and OSM are not fully integrated. \n\nThe following analyses provide a series of comparative data insights that help to better understand the potential and implications of integration between knowledge graphs and OSM. In this work, OSM is compared to Wikidata, one of the largest open knowledge graph projects from the Wikimedia Foundation that provides structured storage to other Wikimedia projects such as Wikipedia. Wikidata can, in many aspects, be compared to OSM by its community structure, its free and open nature, and simple contribution framework. In this work, the two datasets are first compared in size, data structure, and distribution. Later, we extend our analyses with a community comparison. The presented analyses also examine how two separate online communities with similar interests have evolved.    \n\nGrasping the size of the two projects is a straightforward task and visible on their websites: OSM features around 1 billion elements [5], while Wikidata is much smaller with over 97 million objects, of which approximately 9 million have geographic coordinates. The topic of railway stations was chosen because these objects have a comparable definition and are well represented in both datasets with ca. 130k and 100k elements in OSM and Wikidata, respectively, indicating integration potential. In OSM, railway stations are mapped by the tags 'railway=station' or 'railway=halt'. In Wikidata, the 'instance of (P31)' property containing 'Q55488' value represents Railway Station (object type).    \n\nBy defining generalizable comparison indicators, the presented work provides a framework and source code (available at https://gitlab.gistools.geog.uni-heidelberg.de/giscience/ideal-vgi/osm-wikidata-comparison under the GNU Affero General Public License v3) for VGI project description, comparison, and monitoring. Similar approaches have been established for OSM contributors [6], for single OSM elements [7], and for small geographic regions [8]. For data collection in Wikidata, Wikidata API (https://www.wikidata.org/w/api.php) and Wikidata SPARQL endpoint were used. For Wikidata objects mapped with 'Railway Station', their revision history containing user information, timestamps, and a number of properties was collected. Overall contributions were collected from all users who have contributed to at least one object typed 'Railway Station'. OSM data collection was done using the ohsome API (https://ohsome.org) to extract all railway stations mapped in OSM, including their history and all edits made by the users who edited these railway stations. In addition to a general comparison between the datasets, we derived five sets for a more detailed comparison: OSM with links to Wikidata (59,441 elements), OSM without links to Wikidata (74,659), Wikidata that have links from OSM and are typed as railway stations (45,050), Wikidata without links to OSM but with geocoordinates (54,594) and Wikidata without links to OSM and without geocoordinates (6,714).   \n\nOur first analysis regarding the growth rate of the two sources showed that OSM has reached a saturated state regarding the number of railway stations, where only a few stations were added since mid-2020. Wikidata, on the other hand, still experiences a stable number of new stations that are added to the project. The two datasets depict no clear temporal correlation hinting towards two independent communities, meaning that edits in OSM are not followed by edits in Wikidata and vice versa. Despite the similar size of the two datasets at a global scale, the two datasets show significant discrepancies on a country level. For example, in China, Wikidata features only 39% of the stations present in OSM while having more than double the amount of stations in the United Kingdom. While the lack of stations seems reasonable considering the overall lack of stations in Wikidata, the overabundance of stations in the UK hints towards a data issue that needs more detailed analyses before integration.  \nIn terms of properties/tags of each object, we observed that Wikidata has, on average more properties per object than OSM. Since Wikidata is a knowledge graph, it also contains links to other objects that can help enrich existing objects increasing this discrepancy even further. OSM objects with links to Wikidatda have almost double the tags compared to those without links. This could either be a quality indicator or an indicator that only famous stations, which are very well mapped in OSM, are also linked to Wikidata. Wikidata objects without links from OSM and geocoordinates have the least number of properties, hinting at their lower quality.    \n\nNext, we present the community analysis. There were around 8.4 million contributors in OSM in total, and 48k unique users have contributed to either creation, deletion, or updating of the railway station objects. In Wikidata, the number of overall contributors is much smaller, i.e., 24k out of which 14k have contributed to Railway objects. The revisions for Wikidata objects are around 11 times higher than that of OSM revisions. This is evident as Wikidata railway stations have more properties than OSM railway stations. This could also be because OSM contributors have a wide variety of objects to map, whether a bench or a tree. In contrast, Wikidata contributors may focus on details and enrichment of prominent objects of public interest. In OSM, adding a new object to the map may take priority over extensive tagging of existing objects. A similar trend is observed for average stations created by each contributor wherein, on average, Wikidata contributors have created five times and, with median statistics, two times more objects than OSM contributors. This may be due to the higher number of bots and imports in Wikidata. While OSM users generally map a specific area that can only feature a limited number of railway stations, Wikidata users may import railway stations from other sources without limiting themselves to a certain geographical unit.   \n\nTo conclude, we notice that both communities have great potential to integrate these sources on the topic of railway stations. This potential increases daily with other topics reaching a mature data state in Wikidata and other knowledge graphs. OSM can benefit from the wide range of semantic information linked to objects, while Wikidata can benefit from the precise geoinformation and completeness OSM offers. Yet, care needs to be taken to take both communities on board as each user base exhibits unique data collection styles that need to be respected.","original_language":"eng","persons":["Alishiba Dsouza","Moritz Schott"],"tags":["sotm2022","19388","2022","OSM","OpenStreetMap"],"view_count":33,"promoted":false,"date":"2022-08-21T10:00:00.000+02:00","release_date":"2022-10-11T00:00:00.000+02:00","updated_at":"2025-07-16T23:15:03.562+02:00","length":1605,"duration":1605,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19388-decf8c0d-09b1-5527-80d1-f413272b8e94.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19388-decf8c0d-09b1-5527-80d1-f413272b8e94_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19388-decf8c0d-09b1-5527-80d1-f413272b8e94.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19388-decf8c0d-09b1-5527-80d1-f413272b8e94.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19388-comparative-integration-potential-analyses-of-osm-and-wikidata-the-case-study-of-railway-stations","url":"https://api.media.ccc.de/public/events/decf8c0d-09b1-5527-80d1-f413272b8e94","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"39ed1b8c-4853-538f-9bf9-3e322e4514b6","title":"Satellite Imagery for Social Good - Our Reflections","subtitle":null,"slug":"sotm2022-18643-satellite-imagery-for-social-good-our-reflections","link":"https://2022.stateofthemap.org/sessions/GNEJ9G/","description":"During the 2019-2020 pilot supported by Microsoft, 18 million building footprints were automatically extracted from satellite imagery for all of Tanzania and Uganda. HOT found that on average, mappers working without AI assistance could map between 1000-1500 buildings per working day. For areas with high-quality AI output, providing mappers with AI-generated building footprint suggestions increased this rate to up to 2500-3000 buildings per day approximately doubling the rate at which building data could be added to OpenStreetMap, which is the crucial link for making it available to the humanitarian information management community.\n\nWhile doubling mapping efficiencies (100% efficiency gain) are promising, one of the greatest challenges is making sure data is converted from AI/ML to OpenStreetMap in a rapid yet responsible community-centric way (respecting existing data contributions already in OpenStreetMap). This project enabled us to take the ‘next step’ after receiving the building predictions from Microsoft by building better tools for data conflation. This is expected to dramatically reduce manual intervention. By doing this, human mappers can focus their time and skills on value-added activities: ground-truthing and validating predictions and adding local knowledge to the map not visible from satellite imagery (such as place names, location of key lifeline infrastructure, etc. \n\nWe’ll describe our mapping workflow and progress updates for Kenya and Nigeria, and the specific challenges met with this dataset extracted through machine learning will be explained. Considering the growing availability of such AI-related datasets, we’ll review common errors and how we adapted to them and to other issues such as imagery offsets, heterogeneity of existing data and other context specific challenges. Eventually we’ll propose recommendations regarding this type of editing and aspects to consider before starting such imports and how to get the community involved.","original_language":"eng","persons":["Shamilah Nassozi"],"tags":["sotm2022","18643","2022","Mapping","OSM","OpenStreetMap"],"view_count":53,"promoted":false,"date":"2022-08-20T17:00:00.000+02:00","release_date":"2022-10-01T00:00:00.000+02:00","updated_at":"2026-03-22T22:45:07.955+01:00","length":1482,"duration":1482,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18643-39ed1b8c-4853-538f-9bf9-3e322e4514b6.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18643-39ed1b8c-4853-538f-9bf9-3e322e4514b6_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18643-39ed1b8c-4853-538f-9bf9-3e322e4514b6.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18643-39ed1b8c-4853-538f-9bf9-3e322e4514b6.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18643-satellite-imagery-for-social-good-our-reflections","url":"https://api.media.ccc.de/public/events/39ed1b8c-4853-538f-9bf9-3e322e4514b6","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"d13397f0-860f-56a7-82f1-f99fcf0ff2d4","title":"Null Island - a node of contention in OpenStreetMap","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19417-null-island-a-node-of-contention-in-openstreetmap","link":"https://2022.stateofthemap.org/sessions/LTA77E/","description":"Null Island is where the prime meridian meets the equator at (0,0) longitude and latitude. While Null Island is a fictitious, dimensionless, point object, its existence stimulates vigorous debate making it worthy of serious consideration. Many examples exists illustrating how Null Island impacts OSM discourse. Our study considers what the geographic oddity of Null Island means for OSM. The main contribution is a structured knowledge-based resource facilitating understanding of Null Island’s impact on OSM. This socio-technical and philosophical investigation of Null Island can become a catalyst for deeper discussions and debates in OSM around mapping practices.\n\nNull Island refers to the location where the prime meridian meets the equator at 0o longitude and 0o latitude. With coordinates (0, 0), it is the origin of the WGS84 geographic coordinate system. It has been argued that Null Island can be considered a real place that is a product of our digital age [1]. Null Island’s significance comes from the fact that it is erroneously associated with large amounts of geographic data that spans across geo-social media, location-based services and map databases. Even though Null Island is a fictitious, dimensionless, point object, its existence stimulates debate that elevates Null Island into a global issue worthy of serious consideration (a detailed description of associated issues is given in [1]). Members of the OpenStreetMap (OSM) project often interact with this location in various ways, and therefore understanding what Null Island means for OSM is relevant. We can find several examples of Null Island affecting OSM, such as a recent debate that arose in the talk mailing list in January 2022 with the title “Was the deletion of Null Island reasonable?” [2], where contributors argued for or against the deletion of Null Island. In addition, a web search for the term “Null Island” on the openstreetmap.org domain [3] reveals that Null Island was mentioned across the entire OSM ecosystem, including mailing lists, forums, user diaries, notes, features, changesets, wiki pages, help articles, blogs and even the Ruby on Rails codebase of the OSM website uses Null Island for testing (https://tinyurl.com/OSM-Ruby-Null). These suggest that Null Island already has a widespread reach within the OSM project. \nThe purpose of this study is to consider both qualitatively and quantitatively what the geographic oddity of Null Island means for OSM. No research works exist which tackle this issue in depth. Previous studies mentioning Null Island do so in a simplistic way and use the term to refer to the (0, 0) location (see e.g. [4]–[6]). Only a few studies recognize it as a special location and unique phenomenon ([7], [8]), and to our knowledge, only one study tackles the issue in depth [1]. In addition to contributing a robust academic study of Null Island, this work will produce a structured knowledge-based resource for the community to understand Null Island’s impact on OSM. \n\tBuilding on [1] we investigate the various ways Null Island is represented in the OSM project subsequently contributing an evidence-based narrative history on the evolution of Null Island. This includes the qualitative review of various OSM communications channels (e.g. mailing lists, discussion boards and wikis) for mentions and references to Null Island. We believe these channels help provide insights about how the OSM community contextualizes, describes  and deals with Null Island. The history of special map features related to Null Island, such as node #1 (https://tinyurl.com/osm-first-node) and the node located at (0, 0) (https://tinyurl.com/OSM-Center) will also be reviewed to illustrate what actions the OSM community took in terms of adding and removing Null Island to the database. In addition to these qualitative approaches, we utilize the ohsome API [9] to extract and analyze map edits made on or near Null Island, which provides a quantitative way to assess the frequency of erroneous data added to OSM near (0, 0) as well as the semantics of such data.\nInteresting patterns have already emerged from the preliminary analysis of data. The most recent mailing list debate mentioned above [2] can be summarized as follows. 17 individuals contributed 45 e-mails to the discussion between January 3 and January 10, 2022. One of the (very few) rules of OSM is that data should be verifiable, meaning that others can visit the real location of a map object and see for themselves if the data is correct. This is also known as the “ground-truth rule” [10]. Null Island as a fictional place violates this rule, therefore a popular stand in the debate is that it should not be part of OSM. This was explicitly expressed by five individuals, including a member of the authoritative Data Working Group. A counter argument is that Null Island is fundamentally similar to localities and neighborhoods, that might not exist as political or physical entities, but are known only informally to a group of people inhabiting that area. In this sense, Null Island is a place that exists in the collective consciousness of people and the name refers to the same geographic area. This justifies tagging the (0, 0) location as place=locality and name=”Null Island” in OSM. This view was explicitly supported by seven members on the mailing list. The remaining five individuals that contributed to the discussion did not take a clear stand on whether to remove or keep Null Island, but have provided arguments both for and against the deletion of it.\nThe full history of OSM data was extracted from the elementsFullHistory endpoint of the ohsome API [9] within the geographic bounding box defined by the southwest point of (-0.001, -0.001) and the northeast point of (0.001, 0.001) between January 1, 2012 and January 1, 2022. During this 10-year-long period, a feature was added, deleted or modified every three days on average within this bounding box, resulting in 1323 unique features (nodes, ways or relations). In addition, map Notes as well as GPS traces are also constantly being created, which makes Null Island and its surrounding a busy area in terms of OSM data activity.\nNull Island is a socio-technological concept that has only been sparsely present in the GIScience literature so far. Our novel approach highlights how a seemingly lighthearted topic like Null Island can generate serious debates that are technological, social and even philosophical in nature. OSM and Null Island have a long tradition together with sometimes heated mapping debates resurfacing from time to time with no apparent resolution in sight. While resolving these debates is entirely in the hands of the OSM community, our research contributes to the potential resolution of them in a meaningful way by providing a factual, detailed, and accurate account of Null Island in OSM. Furthermore, while Null Island is potentially the most prominent example of a fictional place affecting maps and mapping practices, other examples also exist. For example, the most remote location on Earth, Point Nemo (which is the point in the ocean that is farthest from land) [11] is also present in OSM (https://tinyurl.com/OSM-PointNemo). Our OSM specific investigations together with a more general introduction of Null Island from both technological and social perspectives presented in [1] will help demystify the abstract concept of a fictional place that is present in real databases. Increased understanding will potentially help OSM members resolve mapping debates about “real fictional places”. Discussion around Null Island and other fictional places is unlikely to end with this work. Our work will contribute in a technical, socio-technical and philosophical way to the Null Island story in OSM with the potential to become a catalyst for further discussions related to wider debates in OSM around mapping practices.","original_language":"eng","persons":["Peter Mooney","Levente Juhász"],"tags":["sotm2022","19417","2022","OSM","OpenStreetMap"],"view_count":201,"promoted":false,"date":"2022-08-21T12:00:00.000+02:00","release_date":"2022-10-15T00:00:00.000+02:00","updated_at":"2026-01-20T03:15:05.002+01:00","length":1647,"duration":1647,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19417-d13397f0-860f-56a7-82f1-f99fcf0ff2d4.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19417-d13397f0-860f-56a7-82f1-f99fcf0ff2d4_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19417-d13397f0-860f-56a7-82f1-f99fcf0ff2d4.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19417-d13397f0-860f-56a7-82f1-f99fcf0ff2d4.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19417-null-island-a-node-of-contention-in-openstreetmap","url":"https://api.media.ccc.de/public/events/d13397f0-860f-56a7-82f1-f99fcf0ff2d4","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"96eed09e-0832-5cde-9777-c54fa1f461e6","title":"YouthMappers: A Hybrid Movement Design for the OpenStreetMap Community of Communities","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19305-youthmappers-a-hybrid-movement-design-for-the-openstreetmap-community-of-communities","link":"https://2022.stateofthemap.org/sessions/THYCMA/","description":"The YouthMappers experience lends itself to explore interesting questions about the cultural and organizational aspects of data production and usage practices in OpenStreetMap, in order to improve them. First, this study aims to identify what are some of the qualitative and quantitative characteristics distinguishing the performance of YouthMappers as an academic-based community within OSM. Second, this study aims to better understand how the design approach taken by and on behalf of YouthMappers reinforces an identity as unique contributors.\n\nIncreasingly ubiquitous open spatial technologies offer the opportunity for new actors to participate in creating knowledge about the places where they live and work, and where they connect to others around the world. University students are one set of actors who have grown significantly in their visibility and contributions to OpenStreetMap, in part through the establishment of YouthMappers in 2015. This inclusive international network of university-based, youth-led, faculty-mentored chapters on more than 320 campuses in 66+ countries works to mobilize and support university student mapping action that responds to humanitarian and development needs by creating and using an ecosystem of data and tools centered on OpenStreetMap. \n\nThe YouthMappers experience lends itself to explore interesting questions about the cultural and organizational aspects of data production and usage practices in OpenStreetMap, in order to improve them. In this case, we explore these aspects as they occur within and through the academic sector, particularly through the hands and eyes of student youth. As a consortium design, this networked set of local groups works on the one hand, to create and use data on their local campuses and home communities, and on the other, to remotely contribute data on imagery-visible features in response to humanitarian, development, and knowledge needs wherever they may occur around the globe. Furthermore, they act not only within the OSM “community of communities” framework (Solís 2016), but also within an existing global infrastructure of academic institutions with its own set of shared educational aims, knowledge generating practices, and cultural norms. Meanwhile, students are motivated both by learning and using new skills and workforce competencies as well as by the opportunity to participate in the world’s largest volunteered geographic information project and the activities that make common good use of the data. So how do YouthMappers navigate these different aims within these different spaces of action?\n\nTo address this question, two aspects of this experience are the focus of attention in this study. First, this study aims to identify what are some of the qualitative and quantitative characteristics distinguishing the performance of YouthMappers as an academic-based community within OSM. Second, this study aims to better understand how the design approach taken by and on behalf of YouthMappers reinforces an identity as unique contributors.\n\nThe presentation first will provide a description and justification for the purposeful design of the YouthMappers consortium (Solís et al. 2018) within the context of OpenStreetMap (Brovelli et al. 2019). The study will be contextualized with a review of literature on the current state of higher education, particularly with respect to a present tension around higher ed institutions’ purpose as sites for both workforce preparation and global citizenship. The latter point will be situated with reference to scholarship on the global targets of the Sustainable Development Goals (SDGs), as perhaps the predominant discourse for international action across humanitarian domains. This review sets up three interlocking hypotheses that the evidence is anticipated to reject:\nH1: (Action-of-Performance) Participating youth either map only locally or remotely, but not both; \nH2: (Hybrid-Roles) Participating youth cannot simultaneously pursue personal aims to prepare themselves for the workforce and to express their identities as global citizens; and\nH3: (Movement-Minded) Participating youth cannot articulate the impacts/benefits of actions undertaken for broader communities or society through their work with OSM, nor identify the roles/contributions of youth action in this work for the common good.\n\nData to test the first hypothesis relate to performance and include a range of metrics of participation (Andal et al. 2022; Boateng et al. 2022; Walachosky et al., 2022); statistics of known users (Anderson 2022), and a review of data from other studies of YouthMappers’ editing contributions (e.g., Mahmud et al. 2022). Data to test the second hypotheses relate to identity and come from the long-running student-authored blogs (Hite et al., 2018), as well as a global survey of YouthMappers collected in 2019 accompanied by a qualitative set of member queries to iterate interpretation of the survey results (Solís, Anderson \u0026 Rajagopalan 2020; Solís et al. 2022). Data to test the third hypotheses come from a set of case studies that are considered with respect to the SDGs (Solís \u0026 Zeballos, forthcoming). Collectively, these data are analyzed with respect to the above hypotheses. \n\nResults reveal a spectrum of interests balancing local and global mapping across the consortium, and across regions, and other axis of participation. They also indicate the extent to which youth reflect on local benefits, including personal skill development, versus global citizenship, including how they understand the meanings of their actions for SDGs, locally and globally. Detected differences by gender, world region, and duration of participation are interpreted and validated with additional qualitative data. The results are presented with respect to rejecting all three hypotheses, which validate the model.\n\nThese findings help to begin to build a case for understanding the potential of the YouthMappers design to advance the goals of OSM, of the academic community, and potentially the SDGs. In particular, we discuss the possible role of university systems as third space sites for enabling performance and identities for youth action (Bawakyillenuo et al., 2013; Soja, 1996). This gives consideration to the possibility for universities to serve as sites that Heaney and Rojas (2014) characterize as hybrid organizations that, when linked to global discourses on issues like sustainability (SDGs) and open data (OSM), can mobilize youth to create and participate in what we term digital humanitarian “hybrid movements” (Solís, et al. 2022). In turn, this hybrid movement puts into place both a framework of performance and a space of identity which can serve to advance OSM communities.\n\nThese findings offer insights for how other types of communities could leverage their existing milieu in ways that strengthens OSM broadly. Ultimately, the idea of hybrid movements encourages OSM to embrace a pluralistic, inclusive and diverse set of communities that not only bring individual contributions but leverages other systems like the landscape of academia was systematically enlisted via the YouthMappers design.","original_language":"eng","persons":["Patricia Solis"],"tags":["sotm2022","19305","2022","OSM","OpenStreetMap"],"view_count":45,"promoted":false,"date":"2022-08-21T14:30:00.000+02:00","release_date":"2022-10-16T00:00:00.000+02:00","updated_at":"2026-01-04T11:15:09.669+01:00","length":1527,"duration":1527,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19305-96eed09e-0832-5cde-9777-c54fa1f461e6.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19305-96eed09e-0832-5cde-9777-c54fa1f461e6_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19305-96eed09e-0832-5cde-9777-c54fa1f461e6.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19305-96eed09e-0832-5cde-9777-c54fa1f461e6.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19305-youthmappers-a-hybrid-movement-design-for-the-openstreetmap-community-of-communities","url":"https://api.media.ccc.de/public/events/96eed09e-0832-5cde-9777-c54fa1f461e6","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"a75fcaac-17fd-5015-b19f-bde6fe3e5d5c","title":"Integrating OpenStreetMap in the local governance of Nepal","subtitle":null,"slug":"sotm2022-18533-integrating-openstreetmap-in-the-local-governance-of-nepal","link":"https://2022.stateofthemap.org/sessions/FABJFJ/","description":"Through open mapping, we hope to inspire local youths and leaders to use technology to improve and create sustainable local governance system. We engaged 44 youths from 3 municipalities to participate in open mapping in first phase. In second phase, we gathered 10 IT officials from province 2 and highlighted OSM to connect geospatial elements for better local-level administration. \n\nHere, we will discuss our methodological framework, challenges, how we gained support from local units to coordinate with community youth, and impacts we successfully created. This talk will be relevant to those interested in laying a strong foundation not only for better governance but also for engaging youths.\n\nThe success of local governments in meeting development targets is crucial for the goals of federalism in Nepal. The local governments have a colossal opportunity to set the course of development based on people’s aspirations, as citizens have increased power and responsibility to choose and act on their agenda in the federal system. However, these governments face daunting challenges in human, capital, and other resource constraints. Innovative citizen-centric approaches are necessary to navigate these challenges to achieve the development goals. It is also imperative to note that properly achieving the development goals and measuring their progress is contingent on decision making and planning, driven by data. Geospatial data is valuable to the local economy and community in various aspects. The openness of the information, or more specifically the freedom to access and use such open data, is crucial to achieving such multiplier effects of taxpayer-funded data generation processes.\n\nSo hereby, our interest lies in training and encouraging local youths and leaders to leverage technology for good governance through open mapping in coordination with local administrative units. Our initiative looks beyond the myopic vision of training a handful of youth on digital mapping to create a limited number of outputs, i.e. maps. We implemented a mechanism in which we created a batch of OpenStreetMap (OSM) leaders, who in their regions, can inspire and enable such processes beyond the life of any particular project. As such, we wanted to set in motion a process that can beget far-reaching, longer-term benefits in the form of active citizenry (on the youths’ part) and data-driven development (on the local governments’ part). This will be in addition to the immediate, short-term outputs such as up-to-date maps and enhanced mapping skills of the mappers.\n\nIn the first phase, KLL trained 44 citizens from 3 local bodies in the use of OSM and mapping in coordination with the municipality. The trained youths were also involved field data collection. Simultaneously, we started remotely mapping the municipality. Based on the need, we collected field data to complete the map. On mutual interests and availability of resources, the mapping of the local governments included points of interest like: Roads, Settlements, Educational institutions, Health facilities, Government offices, Banking institutions and others as agreed upon by the municipalities. Using the data hence collected, we created softcopies and hard copies of ward (smaller administrative unit of government in Nepal) level and municipality level maps and handed them over to the municipalities for their use.\n\nIn the second phase, to ensure better understanding of OSM within the local governments, we trained 10 IT officers from Province 2 on OpenStreetMap, its uses and applications in local governance. We also led a focused group discussion on how open mapping can be utilised by municipalities, what features need to be mapped for the integration of OSM in local governance and challenges they foresee in this process.\n\nHence, we will be discussing our methodological framework and challenges in involving local youths and leaders in open mapping and in contributing to develop more promising local level governance. We will also share how we gained needed support from the local units to coordinate with the youth of the community and the impacts we successfully created. This talk will be relevant to those interested in laying a strong foundation not only for good governance with the core idea of open mapping and its application at its center but also for involving local youth in the process.","original_language":"eng","persons":["Aishworya Shrestha","Sushma Ghimire"],"tags":["sotm2022","18533","2022","User Experiences","OSM","OpenStreetMap"],"view_count":29,"promoted":false,"date":"2022-08-19T15:30:00.000+02:00","release_date":"2022-09-24T00:00:00.000+02:00","updated_at":"2026-03-01T11:45:04.645+01:00","length":1450,"duration":1450,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18533-a75fcaac-17fd-5015-b19f-bde6fe3e5d5c.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18533-a75fcaac-17fd-5015-b19f-bde6fe3e5d5c_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18533-a75fcaac-17fd-5015-b19f-bde6fe3e5d5c.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18533-a75fcaac-17fd-5015-b19f-bde6fe3e5d5c.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18533-integrating-openstreetmap-in-the-local-governance-of-nepal","url":"https://api.media.ccc.de/public/events/a75fcaac-17fd-5015-b19f-bde6fe3e5d5c","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"51ecea24-f61b-56e4-9a61-a71eb1692c73","title":"Lightning talks III","subtitle":null,"slug":"sotm2022-19712-lightning-talks-iii","link":"https://2022.stateofthemap.org/sessions/FGDY3F/","description":"Lighting talks registered during the State of the Map conference.\n\n## OSM Teams: History \u0026 Updates\n\n_by Lane Goodman_\n\n## Bike Data Project\n\n_by Ben Abelshausen_\n\n## 360° imagery everywhere\n\n_by Joost Schouppe_\n\n## Worldwide Administrative Boundaries Dataset Project\n\n_by Albert Bautista_","original_language":"eng","persons":["Various Speakers"],"tags":["sotm2022","19712","2022","OSM","OpenStreetMap"],"view_count":14,"promoted":false,"date":"2022-08-20T16:30:00.000+02:00","release_date":"2022-10-01T00:00:00.000+02:00","updated_at":"2024-08-21T21:45:02.199+02:00","length":1102,"duration":1102,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19712-51ecea24-f61b-56e4-9a61-a71eb1692c73.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19712-51ecea24-f61b-56e4-9a61-a71eb1692c73_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19712-51ecea24-f61b-56e4-9a61-a71eb1692c73.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19712-51ecea24-f61b-56e4-9a61-a71eb1692c73.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-19712-lightning-talks-iii","url":"https://api.media.ccc.de/public/events/51ecea24-f61b-56e4-9a61-a71eb1692c73","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"ddab791f-4722-52b3-9481-0e8f98b37a49","title":"Lightning talks IV","subtitle":null,"slug":"sotm2022-19713-lightning-talks-iv","link":"https://2022.stateofthemap.org/sessions/NNKX8K/","description":"Lighting talks registered during the State of the Map conference.\n\n## Offline Web Mapping Server UNVT Portable\n\nThe United Nations Vector Tile Toolkit.\n\n_by Shogo Hirasawa, Taichi Furuhashi_\n\n## Liaising OpenStreetMap (OSM) Community and Research Community with the Policy Makers: Reducing the Data Gap in Disaster Management \n\n_by Airin Akter, Shraddha Sharma_\n\n## Unique Mappers Network: The OpenStreetMap Community NGO in Nigeria\n\n_by Victor N. Sunday, Nwinkua Dumdibabari_\n\n## NOAH (Nationwide Operational Assessment of Hazards) Website, revamped!\n\n_by Feye Andal_","original_language":"eng","persons":["Various Speakers"],"tags":["sotm2022","19713","2022","OSM","OpenStreetMap"],"view_count":25,"promoted":false,"date":"2022-08-21T12:30:00.000+02:00","release_date":"2022-10-02T00:00:00.000+02:00","updated_at":"2025-01-02T07:30:04.513+01:00","length":1179,"duration":1179,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19713-ddab791f-4722-52b3-9481-0e8f98b37a49.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19713-ddab791f-4722-52b3-9481-0e8f98b37a49_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19713-ddab791f-4722-52b3-9481-0e8f98b37a49.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19713-ddab791f-4722-52b3-9481-0e8f98b37a49.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-19713-lightning-talks-iv","url":"https://api.media.ccc.de/public/events/ddab791f-4722-52b3-9481-0e8f98b37a49","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"7e625872-2910-59f0-aeda-f0391ad4ffff","title":"Every Door and the Future of POI in OpenStreetMap","subtitle":null,"slug":"sotm2022-18515-every-door-and-the-future-of-poi-in-openstreetmap","link":"https://2022.stateofthemap.org/sessions/ACCWJY/","description":"OpenStreetMap is a collector's dream. While there is a finite set of stamps or coins, there are millions of shops and beauty salons, and new ones are opened every day. Yay, collect them all for the map! (And make the map better in the process, of course.) Alas, this task was made tedious, virtually impossible by our current tools. Not as much for adding — but for updating the data we've already collected, and finding what's missing. I've talked many times of this problem, and this year I think I've fixed it. This year changes everything for how POI are handled in OpenStreetMap.\n\nLast year I presented the idea of making cartography apps without a map for the central UI element. And added a couple drawings of a better editor for POI. Well, it is finally out there, and hundreds of mappers have already surveyed... a lot. So one part of this talk is a typical OSM editor talk: I'll walk you through the design decisions, show some statistics, and elaborate on how writing a new OSM editor is akin to writing a new browser these days: an enormous task, but with a lot of open source code at hand.\n\nBut what is different, is that this editor is not a toy and not a general-purpose tool. It is very focused (well, on three things at once, but still). Meaning, you don't play with the map in it, and don't get lost in menus. You survey. Lots and lots and lots of shops and amenities are going to get added or confirmed. This editor does not just \"allow\" anything. It changes the landscape of OpenStreetMap. Before it shops were an afterthought — now people would seriously consider using OSM for searching. Here's where my experience in working with commercial places data comes in handy. Let's see what can we get out of this, and why \"fun\" is still as important in OSM mapping as always.","original_language":"eng","persons":["Ilya Zverev"],"tags":["sotm2022","18515","2022","Mapping","OSM","OpenStreetMap"],"view_count":173,"promoted":false,"date":"2022-08-20T12:30:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2026-02-23T15:45:08.168+01:00","length":1582,"duration":1582,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18515-7e625872-2910-59f0-aeda-f0391ad4ffff.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18515-7e625872-2910-59f0-aeda-f0391ad4ffff_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18515-7e625872-2910-59f0-aeda-f0391ad4ffff.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18515-7e625872-2910-59f0-aeda-f0391ad4ffff.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18515-every-door-and-the-future-of-poi-in-openstreetmap","url":"https://api.media.ccc.de/public/events/7e625872-2910-59f0-aeda-f0391ad4ffff","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"344128b9-e2df-5f62-8384-92f207a8150d","title":"UX for hyperlocal map in Southeast Asia","subtitle":null,"slug":"sotm2022-18458-ux-for-hyperlocal-map-in-southeast-asia","link":"https://2022.stateofthemap.org/sessions/YXMUVT/","description":"Designing hyperlocal maps starts with understanding the users in their day-to-day journey through some user research method and why the current digital maps experience does not provide a complete experience for them to navigate and explore the neighborhood. This talk will provide the audience the insights into the mobility lifestyle of the local people in tier 2 cities and how the GrabMaps design team translates into mobile app design to help improve the quality of life for our users. The designer will also share what are some of the key learnings when designing for Southeast Asia.","original_language":"eng","persons":["Low Ko Wee","Sriram Iyer"],"tags":["sotm2022","18458","2022","User Experiences","OSM","OpenStreetMap"],"view_count":46,"promoted":false,"date":"2022-08-19T17:00:00.000+02:00","release_date":"2022-09-24T00:00:00.000+02:00","updated_at":"2026-01-05T12:00:19.072+01:00","length":1375,"duration":1375,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18458-344128b9-e2df-5f62-8384-92f207a8150d.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18458-344128b9-e2df-5f62-8384-92f207a8150d_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18458-344128b9-e2df-5f62-8384-92f207a8150d.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18458-344128b9-e2df-5f62-8384-92f207a8150d.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18458-ux-for-hyperlocal-map-in-southeast-asia","url":"https://api.media.ccc.de/public/events/344128b9-e2df-5f62-8384-92f207a8150d","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"9a78babf-cedd-5b18-9762-6a2332f90655","title":"10 Years Of MapRoulette","subtitle":null,"slug":"sotm2022-18519-10-years-of-maproulette","link":"https://2022.stateofthemap.org/sessions/RRUH8S/","description":"MapRoulette was first announced at State of the Map US in 2012 as a tool to solve the many errors introduced by the import of TIGER road data in the United States. Since then, MapRoulette has been used for map improvements and guided data imports around the world. In this talk, MapRoulette creator Martijn van Exel will look at some of the achievements, lessons learned, and the evolution from a single purpose tool to a micro-tasking platform.\n\n[MapRoulette](https://maproulette.org), the open source web-based micro-tasking platform for OSM, was first announced at State of the Map US in 2012 as a tool to solve the many errors introduced by the [import of TIGER road data](https://wiki.openstreetmap.org/wiki/TIGER) in the United States. After a successful and quick cleanup of over 60.000 common problems found in the TIGER data, it was clear that the idea of a micro-tasking tool was worth developing further. \n\nOver the years, MapRoulette gained a lot of functionality while staying true to its original goal: supplying the OSM communuty with quick, easy to solve tasks that help fix or improve the map. Nowadays, anyone can create tasks [using Overpass](https://learn.maproulette.org/documentation/using-overpass-to-create-challenges/#content) or GeoJSON, there are [new task types](https://learn.maproulette.org/documentation/creating-cooperative-challenges/#content) that make fixing problems in OSM even easier, and there is support for working in teams.\n\nIn this talk, MapRoulette creator Martijn van Exel will look at some of the accomplishments and lessons learned in 10 years of MapRoulette, and highlight some interesting uses of MapRoulette over the years. If time allows, he will also show some of the newer functionality that may not be as well known even to experienced users.","original_language":"eng","persons":["Martijn van Exel"],"tags":["sotm2022","18519","2022","Mapping","OSM","OpenStreetMap"],"view_count":122,"promoted":false,"date":"2022-08-19T11:30:00.000+02:00","release_date":"2022-09-18T00:00:00.000+02:00","updated_at":"2025-12-15T13:30:04.919+01:00","length":1552,"duration":1552,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18519-9a78babf-cedd-5b18-9762-6a2332f90655.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18519-9a78babf-cedd-5b18-9762-6a2332f90655_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18519-9a78babf-cedd-5b18-9762-6a2332f90655.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18519-9a78babf-cedd-5b18-9762-6a2332f90655.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18519-10-years-of-maproulette","url":"https://api.media.ccc.de/public/events/9a78babf-cedd-5b18-9762-6a2332f90655","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"ebcfaa07-e456-5c2b-9409-47d5b49e829c","title":"Educational initiatives and platforms on OpenStreetMap: making open data more accessible","subtitle":null,"slug":"sotm2022-18518-educational-initiatives-and-platforms-on-openstreetmap-making-open-data-more-accessible","link":"https://2022.stateofthemap.org/sessions/SCBZ9Q/","description":"OpenStreetMap is a huge repository of geographic information – but how accessible is it? This panel aims at enlighting existing educational activities and platforms around OpenStreetMap topics, including editing, data usage and community governance, with the goal of exploring how new users and data consumers can approach OpenStreetMap in an easier way, and eventually widespread its adoption. During the panel we will discuss and share educational practices, experiences and tools and challenges.\n\nDuring the panel, we will discuss existing educational activities and platforms around OpenStreetMap topics, including editing, data usage and community governance. When first approaching OpenStreetMap, learning tools and activities may prove to be highly beneficial for new volunteers in several aspects as well as for expert mappers to improve their knowledge.\n Education around editing may lead to higher quality of data both during collaborative events or, in general, over all the mapper’s lifetime.\nMappers may have knowledge on OSM editing and complete lack of understanding on how the data can be used for software or cartographic applications. On the other hand, many data consumers may not know editing practices and how the tagging schema is refined and discussed in the OSM community. \nUnderstanding how the OSM community is interacting and governing the project, including exposure to the guidelines related to good editing practices, organised editing, imports and licensing, may have companies and professionals better understand how to behave in this environment.\nThe goal of this discussion is to enlist and explore all the tools and activities that organizations and communities are developing around education on OSM, with particular focus on the audience covered by those as well as the languages this material is offered through.","original_language":"eng","persons":["Michael Montani"],"tags":["sotm2022","18518","2022","Community and Foundation","OSM","OpenStreetMap"],"view_count":12,"promoted":false,"date":"2022-08-20T12:00:00.000+02:00","release_date":"2022-10-01T00:00:00.000+02:00","updated_at":"2024-05-03T01:30:02.917+02:00","length":3415,"duration":3415,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18518-ebcfaa07-e456-5c2b-9409-47d5b49e829c.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18518-ebcfaa07-e456-5c2b-9409-47d5b49e829c_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18518-ebcfaa07-e456-5c2b-9409-47d5b49e829c.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18518-ebcfaa07-e456-5c2b-9409-47d5b49e829c.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18518-educational-initiatives-and-platforms-on-openstreetmap-making-open-data-more-accessible","url":"https://api.media.ccc.de/public/events/ebcfaa07-e456-5c2b-9409-47d5b49e829c","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"30e2ce17-13ba-5307-ba7a-e9b0f5876f28","title":"None: a story of data that isn't there","subtitle":null,"slug":"sotm2022-18461-none-a-story-of-data-that-isn-t-there","link":"https://2022.stateofthemap.org/sessions/HZUFPQ/","description":"Understanding the limitations of data is hard.\n\nSome tags are missing, and some tend to be present only when others are. Is the missing tag saying something, is it just unknown? When tags take yes/no values, is a missing tag an implicit \"no\", maybe the tag \"does not apply\", or something else…?\n\nThis talk doesn’t have answers. It’s the journey we took through an investigation of road data in London. What we found, what we think about what we found, and ideas of things to compute and visualise, before performing an analysis - or to decide if the data is just not suitable for this analysis.","original_language":"eng","persons":["Gala","Simona Ciocoiu"],"tags":["sotm2022","18461","2022","Data Analysis \u0026 Data Model","OSM","OpenStreetMap"],"view_count":54,"promoted":false,"date":"2022-08-19T15:00:00.000+02:00","release_date":"2022-09-22T00:00:00.000+02:00","updated_at":"2025-07-04T10:45:03.351+02:00","length":1760,"duration":1760,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18461-30e2ce17-13ba-5307-ba7a-e9b0f5876f28.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18461-30e2ce17-13ba-5307-ba7a-e9b0f5876f28_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18461-30e2ce17-13ba-5307-ba7a-e9b0f5876f28.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18461-30e2ce17-13ba-5307-ba7a-e9b0f5876f28.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18461-none-a-story-of-data-that-isn-t-there","url":"https://api.media.ccc.de/public/events/30e2ce17-13ba-5307-ba7a-e9b0f5876f28","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"3ae650d8-8c3b-5db0-a3c0-6545518271c4","title":"Combining Volunteered Geographic Information and WPdx standards to Improve Mapping of Rural Water Infrastructure in Uganda.","subtitle":null,"slug":"state-of-the-map-2022-academic-track-19561-combining-volunteered-geographic-information-and-wpdx-standards-to-improve-mapping-of-rural-water-infrastructure-in-uganda-","link":"https://2022.stateofthemap.org/sessions/JNCVKY/","description":"The lack of data on the distribution of the water resources, possess a great challenge for the water resource investment and AI/ML-enabled advancements in the water sector compared to all other sectors like heath. This paper describes the methodology for combining different water mapping schemas to create comprehensive multi-platform water infrastructure data and enhance rapid updates to support a suite of water resource analytics and extended advanced technology explorations towards improved decision-making.\n\nAccess to clean and safe drinking water is critical to public health and socioeconomic prosperity, yet an estimated quarter of the world’s population lacks such. This was evidenced by the unprecedented outbreak of the COVID-19 pandemic, which left communities extremely vulnerable to fatal illnesses due to the limited access to water for handwashing or lack of knowledge of the existence of the utility. Subsequently, the lack of data on the distribution of the water resources poses a great challenge to the water resource investment and AI/ML-enabled advancements in the water sector compared to all other sectors like heath. Influencing the frequency of water point data collection through crowdsourcing and volunteered geographic information, would greatly improve the availability of water point data, and contribute to the extended roles of water resource distribution, monitoring, and management especially in rural communities.  Therefore, this paper describes the methodology for combining different water mapping schemas to create comprehensive multi-platform water infrastructure data and enhance rapid updates to support a suite of water resource analytics and extended advanced technology explorations towards improved decision-making. \nThe recent technological advances including the web 2.0, cameras, smartphones and sensor networks continue to empower the development of empirical methods as well as the generation of big data and analytical platforms that provide predictive performance on the various socioeconomic needs for sustainable development. OpenStreetMap (OSM) is a crowdsourcing platform which offers a collaborative experience through its database, community, and wiki platforms, to create and update data relevant to support or transform various data deficiencies whether humanitarian or planning. However, the project’s data quality shortcomings often hinder simultaneous data integration with other analytical platforms such as the Water Point Data Exchange (WPdx) that would explicitly maximize the usage and application of these crowdsourced data. Through a project dubbed ‘Water Infrastructure Mapping Uganda’, a data model based upon open mapping methods and survey tools was developed to facilitate the mapping of water infrastructure data points and simultaneous updates of both the WPdx and OSM databases. \nThe project engaged a comprehensive review of the OSM water tag, rural water infrastructure data standards and the WPdx database to generate a survey data form that supported one-time collection of a water point for both OSM and WPdx databases. Underlying the development of the data model/schema in the overall project, a design criterion was established which guided and justified the overall selection of the most relevant factors to include in the process that would eventually become detailed to communicate water infrastructure and functionality. The criteria were followed by an assessment of the; compliance [agreement of the tag], consistency [temporal and spatial representation of the tag], completeness [attribute description of the tag], and granularity [quality of the event information] of the OSM tag to support the development of the. osm language in the Kobo toolbox.\nGulu district, located in the North of Uganda, was identified as a potential pilot area for improving the approach created by the project based on its rich WPdx footprint as well as a well-established OSM community of YouthMappers. Up to date satellite imagery of up to 50cm spatial resolution was acquired through the USAID GeoCentre to facilitate any visual detection of water points, and the digitization of base map data including, buildings, roads and waterways, to be employed in the field mapping exercise. A field mapping workflow was designed to facilitate the field-data collection employing the developed water infrastructure data model and Kobo toolbox. An API link was developed that simultaneously tapped the open-source field collected data into the WPdx database. \nThrough the project, more than 15000 buildings, 1400square kilometres of roads and over 500 water data points were added to OSM as well as the WPdx database for the later data. Also, from the project, several observations were made regarding the improvement of such processes and the extension of the data model beyond one geographical area. The developed workflows characterized and provided a general improvement in the water infrastructure data quality especially for OSM   based on WASH indicators used to officially report on the sustainable development agenda. The workflow development waivered the interoperability gap in geospatial data sharing platforms which often results from unharmonized data structures. It was established that the designed methodology cannot be applied to water data updates but rather to freshwater point data collection. This would lead to exponential water point data increase, however, the workflow may be revised to include the framework for data updates without having to engage the full field mapping process. As well, the data model design was mainly based on the African water infrastructure and open mapping reviews, hence, the transfer of the data model from one continent to another may require a review of some data factors to create better insights of the water indicators in a place of that given continent.","original_language":"eng","persons":["Stellamaris Nakacwa"],"tags":["sotm2022","19561","2022","OSM","OpenStreetMap"],"view_count":6,"promoted":false,"date":"2022-08-21T14:00:00.000+02:00","release_date":"2022-10-14T00:00:00.000+02:00","updated_at":"2024-10-23T06:15:02.705+02:00","length":533,"duration":533,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/19561-3ae650d8-8c3b-5db0-a3c0-6545518271c4.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/19561-3ae650d8-8c3b-5db0-a3c0-6545518271c4_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/19561-3ae650d8-8c3b-5db0-a3c0-6545518271c4.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/19561-3ae650d8-8c3b-5db0-a3c0-6545518271c4.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/state-of-the-map-2022-academic-track-19561-combining-volunteered-geographic-information-and-wpdx-standards-to-improve-mapping-of-rural-water-infrastructure-in-uganda-","url":"https://api.media.ccc.de/public/events/3ae650d8-8c3b-5db0-a3c0-6545518271c4","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]},{"guid":"01cf5117-62ba-5cdf-bfe3-408f6f6e6316","title":"Localization as an inclusion and participatory enabler research","subtitle":null,"slug":"sotm2022-18288-localization-as-an-inclusion-and-participatory-enabler-research","link":"https://2022.stateofthemap.org/sessions/3MMV93/","description":"Language barrier and the default to English puts non-English speakers at a systemic disadvantage throughout open mapping communities and humanitarian open mapping activities resulting in significant missed participation and impact. We held experimentation on language translations of key resources identified by collaborators coming from local OSM communities and we hope to share the findings in this talk.\n\nWe believe that language localization will enable inclusion and participation of underrepresented groups in mapping, dialogues and other humanitarian open mapping activities. \n\nWe ran small experiments with local contributors to test how localisation of resources could work in the main languages of 3 priority countries (Vietnam, Madagascar, Mozambique) and we hoped its insights would inform a self-sustainable Localization Strategy for these communities and beyond. However, the documented findings are not sufficient due to challenges encountered (including communication and technical barriers, difficulty with monitoring and evaluation, among others) during the course of the research. Hence, opportunities and recommendations will be presented for future work to explore this theme.\n\nOSM Diary post on the launch: https://www.openstreetmap.org/user/arnalielsewhere/diary/397844","original_language":"eng","persons":["Arnalie Vicario"],"tags":["sotm2022","18288","2022","Remote Control","OSM","OpenStreetMap"],"view_count":18,"promoted":false,"date":"2022-08-20T17:30:00.000+02:00","release_date":"2022-09-19T00:00:00.000+02:00","updated_at":"2025-09-23T13:30:02.565+02:00","length":1301,"duration":1301,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2022/18288-01cf5117-62ba-5cdf-bfe3-408f6f6e6316.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2022/18288-01cf5117-62ba-5cdf-bfe3-408f6f6e6316_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2022/18288-01cf5117-62ba-5cdf-bfe3-408f6f6e6316.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2022/18288-01cf5117-62ba-5cdf-bfe3-408f6f6e6316.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2022-18288-localization-as-an-inclusion-and-participatory-enabler-research","url":"https://api.media.ccc.de/public/events/01cf5117-62ba-5cdf-bfe3-408f6f6e6316","conference_title":"State of the Map 2022","conference_url":"https://api.media.ccc.de/public/conferences/sotm2022","related":[]}]}