{"acronym":"sotm2025","aspect_ratio":"16:9","updated_at":"2026-04-03T18:15:04.519+02:00","title":"State of the Map 2025","schedule_url":"","slug":"conferences/geo/sotm2025","event_last_released_at":"2026-04-03T00:00:00.000+02:00","link":"https://2025.stateofthemap.org/","description":"State of the Map is the annual event for all mappers and OpenStreetMap users. In 2025 the State of the Map conference took place in Manila, Philippines and online. It was a three day conference packed with talks, workshops, discussion rounds and more.","webgen_location":"conferences/geo/sotm2025","logo_url":"https://static.media.ccc.de/media/events/sotm/2025/sotm2025.png","images_url":"https://static.media.ccc.de/media/events/sotm/2025","recordings_url":"https://cdn.media.ccc.de/events/sotm/2025","url":"https://api.media.ccc.de/public/conferences/sotm2025","events":[{"guid":"4e7c1b7d-3572-555f-ae1a-0d151d504e30","title":"Overpass Turbo goes PostGIS","subtitle":null,"slug":"sotm2025-81980-overpass-turbo-goes-postgis","link":"https://2025.stateofthemap.org/sessions/GQKLBA/","description":"This talks presents the \"Postpass\" service, a database that services OSM data for public querying much like Overpass, but uses PostGIS under the hood. With a few extra characters added to your query, you can have the power of PostGIS at your fingertips from within Overpass Turbo.\n\nOverpass and Overpass Turbo are indispensable tools for OSM contributors - everyone uses them whenever you need to glance at the data and go beyond what's visible on the map. Show me pizza places in Dundee, which playgrounds in Paris have age restrictions - stuff like that can be done quickly with Overpass and doesn't require any data downloading or software installation.\n\nBecause such \"rapid prototyping\" is so easy with Overpass, a lot of community projects small and large have come to depend on Overpass and its two domain-specific query languages as a backbone. Pages upon pages on the community forum and wiki deal with how to solve this or that query with Overpass - and there are cases where a PostGIS query could to the same thing more efficiently or easier than Overpass. \n\nThe missing link to allow the same \"rapid prototyping\" and experimenting with PostGIS that we already see with Overpass is a publicly accessible PostGIS instance. The author has been running exactly that for half a year (Github repos https://github.com/woodpeck/postpass and https://github.com/woodpeck/postpass-ops) and this talk explains how to use it, and what its strengths and weaknesses are.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Frederik Ramm"],"tags":["81980","2025","sotm2025","Data Analysis \u0026 Data Model","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 2"],"view_count":4,"promoted":false,"date":"2025-10-04T09:00:00.000+02:00","release_date":"2026-04-03T00:00:00.000+02:00","updated_at":"2026-04-03T17:00:04.419+02:00","length":1867,"duration":1867,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/81980-4e7c1b7d-3572-555f-ae1a-0d151d504e30.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/81980-4e7c1b7d-3572-555f-ae1a-0d151d504e30_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/81980-4e7c1b7d-3572-555f-ae1a-0d151d504e30.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/81980-4e7c1b7d-3572-555f-ae1a-0d151d504e30.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-81980-overpass-turbo-goes-postgis","url":"https://api.media.ccc.de/public/events/4e7c1b7d-3572-555f-ae1a-0d151d504e30","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"129b7b1b-113b-5c46-b986-b9247a30b26b","title":"Awesome (OSM) Games","subtitle":null,"slug":"sotm2025-71684-awesome-osm-games","link":"https://2025.stateofthemap.org/sessions/LPDJXY/","description":"OpenStreetMap and games feel like they go hand-in-hand and that's more than just coincidental. Both OSM and gaming have the power to bring people together, foster community engagement, and provide unique experiences. \n\nIn fact, the OSM wiki has a page for games built using OSM data (https://wiki.openstreetmap.org/wiki/Games) and in recent years, we've seen the increase in the use of tools such as MapRoulette and StreetComplete that gamify the experience of contributing to OSM. While the latter is a very interesting topic in itself, this talk will focus on the former—games that use, but are not necessarily intended to contribute, OSM data.\n\nIn this talk, we will explore the world of OSM-based/OSM-adjacent games to try and identify various game categories/genres and uses of OSM such as in location-based games (e.g. PokemonGO), serious and realistic simulation games, educational and trivia games, other niche/bespoke games, as well as both digital and tangible/tactile experiences.\n\nFurthermore, we will try to investigate the benefits and drawbacks of using OSM in games and look into other open source \"games/game resources/gaming communities\" (such as those in the Open Source Tabletop/RPG genre) to uncover possible intersections and opportunities.\n\nWhether you're a beginner or experienced OSM contributor, a game developer, or just a fellow gamer, this talk aims to spark new ideas and inspire further discussions, activities, and developments around the intersection of OpenStreetMap and games.\n\nBeyond the use of gamification for improving the OSM contributor experience, I feel that there is an opportunity to re-examine and revisit the broader topic of games using OSM data especially since OSM offers a rich and vibrant data source that can serve as the foundation for unique gaming experiences.\n\nHow is/was OSM data used in games? What works/worked? What doesn't/didn't? Where (or where else) can OSM and games intersect?  A lot of games use maps, can we use OSM there too? What other games can we create with OSM (e.g. Tactics? TTRPG? Board Games?).\n\nThese are just some questions I'd like to ask and hopefully answer in the presentation.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Ben Hur Pintor"],"tags":["71684","2025","sotm2025","User Experiences","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":505,"promoted":false,"date":"2025-10-03T06:00:00.000+02:00","release_date":"2026-03-05T00:00:00.000+01:00","updated_at":"2026-04-03T17:30:05.447+02:00","length":1466,"duration":1466,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71684-129b7b1b-113b-5c46-b986-b9247a30b26b.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71684-129b7b1b-113b-5c46-b986-b9247a30b26b_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71684-129b7b1b-113b-5c46-b986-b9247a30b26b.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71684-129b7b1b-113b-5c46-b986-b9247a30b26b.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-71684-awesome-osm-games","url":"https://api.media.ccc.de/public/events/129b7b1b-113b-5c46-b986-b9247a30b26b","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"365467e5-2ac4-51db-ac27-7338b7ce5b2c","title":"When Metadata Isn’t Enough: Extrinsic Quality Assessment of OSM Using Custom Reference Data","subtitle":null,"slug":"sotm2025-osm-science-71063-when-metadata-isn-t-enough-extrinsic-quality-assessment-of-osm-using-custom-reference-data","link":"https://2025.stateofthemap.org/sessions/EH3ZWQ/","description":"This talk explores the extrinsic quality of OpenStreetMap data in Brno by comparing the city’s most frequently mapped amenities against a custom, field-collected reference dataset. The findings highlight relatively high attribute accuracy in OSM but reveal gaps in feature completeness, with only about 34.94% of features matched with the reference dataset.\n\nOpenStreetMap is a notable example of a database created by volunteers. Due to its open approach to data collection, establishing trust in the data is essential. Three key factors must be considered to evaluate this trust: completeness, correctness, and positional accuracy.  The most common method in recent years for assessing large datasets like OpenStreetMap is to examine intrinsic data quality [1-3], which relies on metadata. However, this approach does not allow for a thorough analysis of the mapped features, resulting in only a rough estimate of the data's trustworthiness. To provide a more detailed understanding of the data, extrinsic data quality is evaluated by comparing OpenStreetMap with a reference dataset. This method is effective for evaluating feature completeness. However, when assessing attribute accuracy, a similarly detailed dataset for comparison is often unavailable. In these instances, the evaluator must gather their own reference dataset, which can be expensive and time-consuming. Because of that, previous studies mainly focused on assessing attribute accuracy by utilizing intrinsic data quality.\nIn our study, we focused on assessing the extrinsic data quality of the city of Brno in the Czech Republic. We wanted to know how much we can trust OpenStreetMap in our city and if there is some correlation between attribute accuracy and metadata of the features. A secondary objective was to determine how well ISO 19157 can be used to assess the attribute quality of the OpenStreetMap. We chose the city’s ten most mapped amenity features and gathered a reference dataset for these features. Since we knew what we would be evaluating, we have acquired a dataset perfect for evaluating OpenStreetMap. Thus, there was no need for major compromises in data evaluation.\nOver the course of several months, we traveled over 1,000 km on foot and gathered a few thousand reference features using the Locus GIS app. Each feature contained a list of evaluated attributes together with a photo of the object for further evaluation. Evaluated amenities were bench, waste_basket, recycling, restaurant, bicycle_parking, cafe, vending_machine, post_box, pub and fast_food – the most mapped node features in Brno.\nOur assessment of OpenStreetMap's attribute accuracy revealed generally positive results. Several attributes in our sample achieved 100% accuracy, particularly those with boolean values. However, the most significant issues arose with string attributes that lack defined value lists, such as opening_hours. Ultimately, the primary concern identified was the completeness of the data.\nWe found that the completeness of feature occurrence is inadequate; we were only able to match 34.94% of all reference features with those in OpenStreetMap. This completeness varied significantly across evaluated amenities, with waste_baskets and benches being notably underrepresented.\nWe also assessed the positional accuracy of the data. Each amenity was evaluated separately, revealing average positional errors ranging from 2.63 meters to 4.02 meters. The median error was found to be between 1.83 meters and 3.13 meters. Overall, OpenStreetMap appears to be a relatively accurate positional database for the city of Brno, despite some isolated deviations (outliers).\nWhen examining the relationship between attribute accuracy and feature metadata, we assumed that more users editing a feature would lead to more accurate data. This concept is known as the “many eyes principle” [4, 5].  However, the correlations between metadata (such as the number of contributors, versions, and days since the last edit) and attribute correctness are typically not statistically significant. As a result, no explicit dependency can be determined, and no clear patterns emerge from the statistically significant values.\nOur work also shows that evaluating OpenStreetMap using ISO 19157 can be problematic because this standard does not consider multiple correct values or the varying degrees of attribute correctness (“level of detail” of attributes). Additionally, automating the evaluation of specific attributes, such as opening_hours, is challenging since these attributes can contain different yet correct values. Furthermore, missing or incomplete documentation significantly impacts evaluation, as there should be clear rules indicating which values are correct or not. However, achieving this is difficult for projects that rely on the folksonomy principle, which encourages users to create new values that often lack documentation.\nOpenStreetMap offers a comprehensive database, but its data quality varies significantly depending on the main tag and geometry type. In our sample data, both attribute and positional accuracy are reasonably good. However, a more significant issue is incompleteness, which includes missing and poorly mapped features. It is important to note that only a small subset of the database has been evaluated. The overall accuracy and completeness can vary significantly around the world, as demonstrated by numerous studies [6, 2, 7, 8]. This research aimed to assess the area of Brno while also testing how ISO 19157 can be applied to evaluate OpenStreetMap. Comparing Brno with a rural area or a city of a similar size in another country would provide valuable insights. However, this evaluation is very time-consuming due to the demands of actual data collection and subsequent processing. The uniqueness of this study is emphasized by the fact that a similar research effort, which uses collected data specifically to evaluate OpenStreetMap, has not yet been conducted.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Daniel Kašík"],"tags":["71063","2025","sotm2025","Pulag","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":2,"promoted":false,"date":"2025-10-03T08:30:00.000+02:00","release_date":"2026-03-24T00:00:00.000+01:00","updated_at":"2026-03-30T22:38:42.846+02:00","length":1484,"duration":1484,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71063-365467e5-2ac4-51db-ac27-7338b7ce5b2c.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71063-365467e5-2ac4-51db-ac27-7338b7ce5b2c_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71063-365467e5-2ac4-51db-ac27-7338b7ce5b2c.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71063-365467e5-2ac4-51db-ac27-7338b7ce5b2c.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-osm-science-71063-when-metadata-isn-t-enough-extrinsic-quality-assessment-of-osm-using-custom-reference-data","url":"https://api.media.ccc.de/public/events/365467e5-2ac4-51db-ac27-7338b7ce5b2c","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"8c20d25f-6586-5ae6-9ea2-c66dc9295f16","title":"Platinum Sponsor Session: OSM Data for Two-Wheelers and Safety","subtitle":null,"slug":"sotm2025-81615-platinum-sponsor-session-osm-data-for-two-wheelers-and-safety","link":"https://2025.stateofthemap.org/sessions/8KQRCY/","description":"This talk delves into the unique challenges and opportunities of enhancing OpenStreetMap (OSM) data for two-wheeler routing and safety. Two-wheelers are a vital mode of transportation within SEA, yet their safety needs are often overlooked. \n\nDrawing from our experience in building and contributing enriched safety data back to OSM, we explore how attributes like lighting conditions, speed bumps, and pothole assessments can make navigation safer and more efficient for two-wheelers.\n\nThrough this talk, we aim to inspire the OSM community to prioritize safety attributes for improving map data quality for two-wheelers.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Soh Leng"],"tags":["81615","2025","sotm2025","Platinum Sponsor Sessions","Pulag","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":4,"promoted":false,"date":"2025-10-03T11:00:00.000+02:00","release_date":"2026-03-30T00:00:00.000+02:00","updated_at":"2026-03-31T14:00:06.544+02:00","length":913,"duration":913,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/81615-8c20d25f-6586-5ae6-9ea2-c66dc9295f16.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/81615-8c20d25f-6586-5ae6-9ea2-c66dc9295f16_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/81615-8c20d25f-6586-5ae6-9ea2-c66dc9295f16.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/81615-8c20d25f-6586-5ae6-9ea2-c66dc9295f16.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-81615-platinum-sponsor-session-osm-data-for-two-wheelers-and-safety","url":"https://api.media.ccc.de/public/events/8c20d25f-6586-5ae6-9ea2-c66dc9295f16","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"93a5b3bf-0c6d-5c80-93e2-86d8317571d8","title":"Intelligent Enough? Evaluating Collective Action in HOT Tasking Manager Mapping Projects","subtitle":null,"slug":"sotm2025-osm-science-71336-intelligent-enough-evaluating-collective-action-in-hot-tasking-manager-mapping-projects","link":"https://2025.stateofthemap.org/sessions/CWLZMQ/","description":"This talk examines the dynamics of collective intelligence in humanitarian mapping projects coordinated through the HOT Tasking Manager, using a dataset of 746 projects and 312,289 tasks to evaluate participation, collaboration, and evidence of intelligent group behavior.\n\nHumanitarian mapping projects coordinated through the Humanitarian OpenStreetMap Team Tasking Manager (HOT-TM) represent a paradigmatic case of large-scale digital collaboration. Yet, while their practical utility in disaster response and preparedness is increasingly evident, the underlying collective dynamics that allow these efforts to function effectively remain under-explored. This talk builds on our recent article published in ACM Transactions on Computer-Human Interaction (TOCHI) [1] that presents a comprehensive analysis of HOT-TM projects through the lens of collective intelligence.\n\nCollective Intelligence is defined as “groups of individuals acting collectively in ways that seem intelligent [2].” Following this definition, we structured our analysis around three guiding research questions: (RQ1) What characterizes the group of individuals collaborating in HOT-TM mapping projects?, (RQ2) How is collective action organized within these projects?, and (RQ3) What evidence of intelligent action can be identified in this setting?\n\nTo answer these questions, we constructed and analyzed a dataset encompassing 746 HOT-TM projects executed between December 2021 and November 2023. The dataset includes 312,289 mapping tasks performed by 38,893 contributors, as well as detailed records of over 1.8 million task states. Additionally, we incorporated spatial information on the area of mapped buildings using data extracted from the OpenStreetMap database.\n\nOur analysis proceeds in three stages. First, we profile the mapping community. Results show that the vast majority of contributors are beginners, who typically participate in a single project. However, a small group of highly experienced mappers—classified by HOT-TM as \"advanced\"—contribute to dozens of projects and assume more complex tasks. Notably, only 29% of contributors declare their country, but among those who do, the majority are based outside the regions affected by the mapping projects (see Figure 1). This reinforces existing concerns about the limited presence of local knowledge in humanitarian Volunteered Geographic Information (VGI) initiatives [3].\n\nSecond, we investigate the organization of collective action using process mining techniques applied to task state logs. Most mapping tasks follow a simple trajectory: a task is mapped and then validated without being split or invalidated (see Figure 2). However, tasks that involve higher complexity or suffer errors require more contributors and longer processing times. Roles within the mapping system are clearly stratified: while beginners dominate mapping in simpler projects, advanced mappers take the lead in complex cases and are responsible for nearly all validations. Despite the potential for collaboration in the mapping phase—through the sequential editing of tasks—true interdependence among contributors is limited. Most tasks are executed by a single mapper, and where collaboration occurs, it is often sequential and uncoordinated. This suggests that HOT-TM's microtasking design promotes a form of \"collection\" rather than \"collaboration\" [4].\n\nThird, we assess the presence of intelligent group behavior by analyzing task validation outcomes through logistic regression. We find that advanced contributors are significantly more likely to produce validated outputs, especially when working alone. However, involving multiple contributors in a task—especially when they are less experienced—decreases the probability of successful validation. Furthermore, tasks with larger building areas or those requiring extensive validation times are less likely to be validated, suggesting that complexity and ambiguity remain major challenges.\n\nThese findings highlight a paradox at the heart of humanitarian mapping: although the system succeeds in rapidly mobilizing volunteers to produce useful geographic data, its collective intelligence is unevenly distributed and relies heavily on a small core of experienced contributors. The wisdom of the crowd is therefore not uniformly distributed; rather, it is the wisdom of a few that sustains the productivity and reliability of the system. Moreover, the absence of strong collaborative mechanisms and the limited engagement of local mappers constrain the potential for adaptive and context-aware mapping.\n\nWe conclude by reflecting on possible design improvements for platforms like HOT-TM. These include: (1) enhancing onboarding and mentorship to accelerate the transition from beginner to advanced contributor; (2) incentivizing meaningful collaboration beyond sequential task handovers; and (3) integrating local knowledge more effectively by prioritizing and rewarding contributions from mappers with relevant geographic proximity or contextual expertise. These directions not only aim to improve the efficiency of mapping but also address the deeper goal of fostering sustainable, inclusive, and context-aware humanitarian VGI ecosystems.\n\nIn this era of rapid and widespread adoption of artificial intelligence, our study offers valuable guidance for the thoughtful integration of these technologies in ways that strengthen—rather than undermine—productive collaboration. By examining how collective intelligence emerges and operates in humanitarian mapping, we identify strategies for deploying AI that support human contributors, enhance coordination, and sustain engagement across diverse experience levels.\n\nThis work contributes to the growing body of research at the intersection of Human-Computer Interaction, Collective Intelligence, and Geographic Information Science, and provides empirical grounding for future interventions in humanitarian mapping systems. \n\n\nAcknowledgement:\nThis work is supported by the ODECO project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 955569.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Héctor Ochoa Ortiz"],"tags":["71336","2025","sotm2025","Pulag","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":0,"promoted":false,"date":"2025-10-03T09:30:00.000+02:00","release_date":"2026-03-29T00:00:00.000+01:00","updated_at":"2026-03-30T22:48:34.834+02:00","length":1789,"duration":1789,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71336-93a5b3bf-0c6d-5c80-93e2-86d8317571d8.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71336-93a5b3bf-0c6d-5c80-93e2-86d8317571d8_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71336-93a5b3bf-0c6d-5c80-93e2-86d8317571d8.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71336-93a5b3bf-0c6d-5c80-93e2-86d8317571d8.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-osm-science-71336-intelligent-enough-evaluating-collective-action-in-hot-tasking-manager-mapping-projects","url":"https://api.media.ccc.de/public/events/93a5b3bf-0c6d-5c80-93e2-86d8317571d8","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"4389d627-a9a5-52b3-9b12-228871853f32","title":"Lightning Talks I","subtitle":null,"slug":"sotm2025-74515-lightning-talks-i","link":"https://2025.stateofthemap.org/sessions/P3FWCW/","description":"## OpenStreetMap Sound Demo\n_by Taro Matsuzawa (@smellman)_\n\n## ArcGIS Living Atlas of the World: Open Data Content\n_by Deniz Karagulle_\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["SotM Working Group"],"tags":["74515","2025","sotm2025","Lightning Talks","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":10,"promoted":false,"date":"2025-10-03T10:30:00.000+02:00","release_date":"2026-03-22T00:00:00.000+01:00","updated_at":"2026-03-30T22:45:44.799+02:00","length":1278,"duration":1278,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/74515-4389d627-a9a5-52b3-9b12-228871853f32.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/74515-4389d627-a9a5-52b3-9b12-228871853f32_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/74515-4389d627-a9a5-52b3-9b12-228871853f32.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/74515-4389d627-a9a5-52b3-9b12-228871853f32.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-74515-lightning-talks-i","url":"https://api.media.ccc.de/public/events/4389d627-a9a5-52b3-9b12-228871853f32","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"1343ca04-349f-5693-b878-4f0a2810ba47","title":"Walking Milano: Unveiling the City’s Character Through 360° Street-Level Panorama Imagery.","subtitle":null,"slug":"sotm2025-71132-walking-milano-unveiling-the-city-s-character-through-360-street-level-panorama-imagery","link":"https://2025.stateofthemap.org/sessions/NRJEVM/","description":"Between September 2024 and August 2025, we conducted a comprehensive street-level survey of Milano, Italy, capturing approximately one million 360° panoramic images using a monopod-mounted camera setup. These images were uploaded to Mapillary, contributing to open-access urban geospatial data. This presentation shares practical insights into continuous data collection methods and analyzes urban characteristics discernible from the imagery, such as graffiti prevalence, urban greenery distribution, and the potential of these visuals as foundational data for 3D digital twin models. I will discuss the current capabilities and limitations of using crowdsourced street-level imagery for urban analysis and planning.\n\nBetween September 2024 and August 2025, we undertook a comprehensive street-level survey of Milano, Italy, capturing approximately one million 360° panoramic images using a monopod-mounted camera setup. These images were uploaded to Mapillary, contributing to open-access urban geospatial data.\n\nThis presentation shares practical insights into continuous data collection methods and analyzes urban characteristics discernible from the imagery, such as graffiti prevalence, urban greenery distribution, and the potential of these visuals as foundational data for 3D digital twin models. I will discuss the current capabilities and limitations of using crowdsourced street-level imagery for urban analysis and planning.\n\nAdvancements in consumer-grade 360° cameras have significantly enhanced image resolution, with modern devices achieving up to 11K. These improvements, coupled with enhanced low-light performance, have expanded the temporal window for effective data collection beyond daylight hours. \n\nMapillary’s object detection capabilities can identify over 150 object classes, including traffic signs, poles, and vegetation. However, challenges remain in detecting certain features like graffiti and nuanced vertical urban greenery, highlighting areas for future development.\n\nBy analyzing the Milano dataset, we can assess the efficacy of current detection algorithms and identify gaps where manual annotation or algorithmic refinement is necessary. This analysis informs strategies for leveraging 360° imagery in urban planning, such as monitoring infrastructure conditions and informing greening initiatives.\n\nThe session will conclude with a discussion on the future of crowdsourced 360° street-level imagery, exploring how community-driven data collection can support comprehensive urban analysis and planning efforts.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Taichi Furuhashi"],"tags":["71132","2025","sotm2025","Mapping","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":27,"promoted":false,"date":"2025-10-03T06:30:00.000+02:00","release_date":"2026-03-21T00:00:00.000+01:00","updated_at":"2026-03-30T22:38:36.455+02:00","length":1793,"duration":1793,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71132-1343ca04-349f-5693-b878-4f0a2810ba47.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71132-1343ca04-349f-5693-b878-4f0a2810ba47_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71132-1343ca04-349f-5693-b878-4f0a2810ba47.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71132-1343ca04-349f-5693-b878-4f0a2810ba47.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-71132-walking-milano-unveiling-the-city-s-character-through-360-street-level-panorama-imagery","url":"https://api.media.ccc.de/public/events/1343ca04-349f-5693-b878-4f0a2810ba47","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"5c0a583e-bb7c-5ccd-b243-576187de651c","title":"Open Mapping for Urban Resilience: A Routing Model to Nearest Safe Spaces in Earthquake-Vulnerable Dhaka","subtitle":null,"slug":"sotm2025-osm-science-70364-open-mapping-for-urban-resilience-a-routing-model-to-nearest-safe-spaces-in-earthquake-vulnerable-dhaka","link":"https://2025.stateofthemap.org/sessions/8UZC9Y/","description":"Dhaka, one of the most densely populated cities in the world, faces a high risk of catastrophic damage in the event of a major earthquake. The lack of accessible open spaces poses a serious challenge for emergency evacuation and survival. This project utilizes OpenStreetMap (OSM) road and building layers, combined with satellite imagery, to identify existing open spaces across the city. Using ArcGIS, a routing model was developed to guide individuals to their nearest safe zone during an earthquake. This approach demonstrates how open-source geospatial data and GIS tools can be leveraged for disaster preparedness in high-risk areas.\n\n1.\tTitle: Open Mapping for Urban Resilience: A Routing Model to Nearest Safe Spaces in Earthquake-Vulnerable Dhaka\n\n2.\tSubmission Type: 20-minute talk\n\n3.\tAbstract: \n\nThis presentation introduces a GIS-based routing model developed for earthquake preparedness in Dhaka, Bangladesh. By integrating OpenStreetMap data with satellite imagery, the project identifies open spaces and directs urban populations to the nearest safe zones during seismic emergencies.\n\n4.\tDescription:\n\nDhaka is one of the most densely populated cities in the world, with over 20 million residents living in an area of approximately 306 square kilometers. Despite its rapid urbanization, the city faces a severe shortage of open spaces, such as parks, playgrounds, and public squares, which are critical in times of disaster. The urban landscape is dominated by high-rise buildings and commercial complexes, with little consideration for creating large, accessible public spaces. This makes it increasingly difficult to accommodate the population in emergency situations, particularly during a major earthquake. According to the World Bank, Dhaka’s vulnerability to earthquakes is exacerbated by the lack of proper urban planning and outdated infrastructure [6]. A study by Rahman et al. (2015) highlights that most of Dhaka’s infrastructure, including residential buildings and roads, was not designed with seismic safety in mind, making the city highly susceptible to catastrophic damage in the event of an earthquake [2].\n\nIn addition to the lack of open spaces, the city's rapid urbanization has led to insufficient planning for evacuation routes and safety measures. Public awareness regarding earthquake preparedness is low, and emergency response systems are not adequately equipped to handle large-scale disasters. Without well-documented open spaces and efficient evacuation routes, the challenge of evacuating residents to safety during an earthquake becomes significantly more complex.\n\nThe objective of this research is to develop a spatial framework for earthquake emergency evacuation planning by integrating multiple data sources: OpenStreetMap (OSM) road and building layers, satellite-derived open space data, and spatial analysis tools in ArcGIS. The methodology involves three major steps:\n\n1.\tOpen Space Identification: Open spaces were delineated by classifying high-resolution satellite imagery, verified and cross-referenced with existing land use data and field knowledge. Parks, playgrounds, schoolyards, open fields, and other publicly accessible open areas were identified as potential evacuation zones [2].\n\n2.\tUrban Feature Mapping with OSM: OSM was used as the primary source for road networks and building footprints. The completeness and accessibility of OSM data enabled detailed modelling of the urban structure [3], and the data was further cleaned and enriched using local knowledge and field validation.\n\n3.\tRouting Model Development: Using ArcGIS Network Analyst, a routing model was developed to calculate the shortest and most accessible path from any given building or cluster of buildings to the nearest open space [4]. This model considers actual street networks and real-world constraints. Future versions of the model could also include blocked roads or damaged areas during earthquakes to make the routes more realistic.\n\nThe significance of this work lies in its practical application: in a high-density city like Dhaka, where open spaces are scarce and poorly documented, a pre-disaster preparedness model like this can be integrated into municipal planning, community training, and mobile-based alert systems [1]. By leveraging open-source geospatial data such as OSM and combining it with remote sensing and network analysis, this approach offers a low-cost, scalable, and replicable solution for other vulnerable cities in the Global South [3].\n\nMoreover, this project highlights the power of community mapping and open data in disaster risk reduction. YouthMappers and local GIS volunteers can play a role in validating OSM features and verifying open space locations [3]. Community participation in data validation enhances the credibility of the model and promotes local ownership of the disaster preparedness process.\n\nThe proposed presentation will:\n•\tDemonstrate the technical workflow from satellite image classification to OSM integration and route modelling;\n•\tShare maps and visualizations showing high-risk areas and evacuation routes in Dhaka;\n•\tDiscuss challenges such as data gaps, building density, and limited public access to open spaces [2];\n•\tSuggest future enhancements including dynamic routing, mobile application integration, and expansion to other hazard types such as fire or flood response [5].\n\nFuture Prospects of the Project: \n\nLooking ahead, this project has the potential to evolve into a user-friendly mobile application or website that could serve as an accessible tool for the residents of Dhaka, helping them navigate to the nearest open space during an earthquake or other disaster. The app could integrate real-time data, such as building damage reports, blocked roads, or infrastructure collapse, to provide dynamic evacuation routes based on the current situation. As part of future iterations, the app could include features like push notifications for earthquake warnings, location tracking, and an interactive map with various evacuation routes displayed in real time. The mobile app could also support crowd-sourced data, where locals can report new open spaces or hazards, continuously improving the accuracy of the evacuation model.\n\nThis work not only contributes to urban resilience but also aligns with the mission of the OpenStreetMap community to use freely accessible geospatial data for humanitarian and civic purposes [3]. It underscores how locally driven mapping efforts, when supported by global tools and platforms, can lead to impactful and life-saving innovations.\n\nBibliography\n1.\tIslam, M. S., \u0026 Adri, N. (2023). Earthquake preparedness in an urban area: the case of Dhaka city. Geoscience Letters, 10(1), 1-12. https://doi.org/10.1186/s40562-023-00281-y\n2.\tRahman, M. M., \u0026 Saha, S. K. (2015). GIS based mapping of vulnerability to earthquake and fire hazard in Dhaka city, Bangladesh. International Journal of Disaster Risk Reduction, 13, 291-300. https://doi.org/10.1016/j.ijdrr.2015.06.002\n3.\tOpenStreetMap Bangladesh Community. (2014). OpenStreetMap for disaster management in Bangladesh. ResearchGate. https://www.researchgate.net/publication/261240182_OpenStreetMap_for_the_disaster_management_in_Bangladesh\n4.\tEsri. (2012). Out of Harm's Way: A custom Network Analyst tool for evacuation routing. ArcUser. https://www.esri.com/news/arcuser/0612/out-of-harms-way.html\n5.\tRed Cross EU. (n.d.). Dhaka earthquake and emergency preparedness – Activities. https://redcross.eu/projects/dhaka-earthquake-and-emergency-preparedness\n6.\tWorld Bank. (2022). Urban resilience in Dhaka: Addressing seismic vulnerability and disaster risks. Retrieved from https://www.worldbank.org\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Ramisa Maliha"],"tags":["70364","2025","sotm2025","Pulag","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":6,"promoted":false,"date":"2025-10-03T10:40:00.000+02:00","release_date":"2026-03-30T00:00:00.000+02:00","updated_at":"2026-03-31T15:15:06.574+02:00","length":375,"duration":375,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/70364-5c0a583e-bb7c-5ccd-b243-576187de651c.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/70364-5c0a583e-bb7c-5ccd-b243-576187de651c_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/70364-5c0a583e-bb7c-5ccd-b243-576187de651c.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/70364-5c0a583e-bb7c-5ccd-b243-576187de651c.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-osm-science-70364-open-mapping-for-urban-resilience-a-routing-model-to-nearest-safe-spaces-in-earthquake-vulnerable-dhaka","url":"https://api.media.ccc.de/public/events/5c0a583e-bb7c-5ccd-b243-576187de651c","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"91e44240-656d-5420-8299-fb7b843f7706","title":"Mapping workflows in iD for new, intermediate and advanced mappers","subtitle":null,"slug":"sotm2025-71166-mapping-workflows-in-id-for-new-intermediate-and-advanced-mappers","link":"https://2025.stateofthemap.org/sessions/H3NM7X/","description":"Mapping in iD is designed to be a welcoming experience that should require little to no required knowledge to get started. Additionally, the editor does also have some additional features up its sleave for more advanced mapping tasks. Regardless if you are a new, intermediate or advanced mapper: it can never hurt to know a trick or two that make your mapping more efficient!\n\nThis talk will go through common mapping workflows in iD and how they can elevate your mapping experience. There will be sections dedicated for beginners, intermediate and advanced mappers. For starters, it will be shown how one can get up to speed most efficiently with iD through its built-in walkthough and other help functionality. For intermediate mappers, the talk will cover topics such as photo-mapping, efficient use of keyboard shortcuts, integrated quality assurance tools, useful browser extensions, etc. The talk concludes with advanced techniques such as adding custom background imagery or map data.\n\nThe showcased mapping workflows shall give a good overview of the spectrum of different mapping tasks one might encounter as a mapper on an regular basis, and will also highlight tools other than iD that allow to dive even further into the respective topics.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Martin Raifer"],"tags":["71166","2025","sotm2025","OSM Basics","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":12,"promoted":false,"date":"2025-10-03T09:00:00.000+02:00","release_date":"2026-03-22T00:00:00.000+01:00","updated_at":"2026-03-30T22:52:40.995+02:00","length":3178,"duration":3178,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71166-91e44240-656d-5420-8299-fb7b843f7706.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71166-91e44240-656d-5420-8299-fb7b843f7706_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71166-91e44240-656d-5420-8299-fb7b843f7706.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71166-91e44240-656d-5420-8299-fb7b843f7706.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-71166-mapping-workflows-in-id-for-new-intermediate-and-advanced-mappers","url":"https://api.media.ccc.de/public/events/91e44240-656d-5420-8299-fb7b843f7706","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"bf70ec40-58d6-5b6a-a6c9-af8b369452cb","title":"OpenStreetMap x Wikidata Collaboration: Taiwan Case","subtitle":null,"slug":"sotm2025-68707-openstreetmap-x-wikidata-collaboration-taiwan-case","link":"https://2025.stateofthemap.org/sessions/JUSRXF/","description":"Not only NSI, but Wikidata can be integrated with OpenStreetMap, and vice versa! Many years ago, with the help of Wikidata Taiwan, OpenStreetMap Taiwan has mapped all 7,000 villages, and also cross-linked to Wikidata. We also have a similar river project of mapping all rivers in Taiwan and crosslinking to Wikidata with the help of the river code published by the Taiwan government. In this talk, we want to further talk about the school, mountain, church, temple, and hiking trail mapping projects, which also have the corresponding external 3rd-party Wikidata property. We will also describe the process like documenting and tools involved\n\nNSI is quite an important project to cross-link OpenStreetMap and Wikidata, but the story can go further. With the help of the Wikidata Taiwan community, there are some achievements, like the rivers crosslinked (P9170), and the villages mapping and cross-linked (P5020). We also use Wikidata information to double check if the school's basic information, like address or website is correct or not, and adding its coordinates from OpenStreetMap geolocation.\n\nThe OpenStreetMap Taiwan continues to integrated more Wikidata and external identifier like religious related Chinese Church and Organization Dictionary ID(P13272) and Platform for Taiwan Religion and Folk Culture ID(P13349); hiking related identifier: Hiking Note Trail identifier(P13406), Hiking Note mountain identifier(P13407). \n\nWe also use automatic approach to make sure both OpenStreetMap and Wikidata records all those entries and information. By the help of Listeria powered by OpenStreetMap, and the datamining  tools Overpass Turobo, to doing some documents and data check.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Dennis Raylin Chen"],"tags":["68707","2025","sotm2025","Mapping","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 2"],"view_count":2,"promoted":false,"date":"2025-10-04T03:30:00.000+02:00","release_date":"2026-03-31T00:00:00.000+02:00","updated_at":"2026-04-02T13:15:04.674+02:00","length":1626,"duration":1626,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/68707-bf70ec40-58d6-5b6a-a6c9-af8b369452cb.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/68707-bf70ec40-58d6-5b6a-a6c9-af8b369452cb_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/68707-bf70ec40-58d6-5b6a-a6c9-af8b369452cb.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/68707-bf70ec40-58d6-5b6a-a6c9-af8b369452cb.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-68707-openstreetmap-x-wikidata-collaboration-taiwan-case","url":"https://api.media.ccc.de/public/events/bf70ec40-58d6-5b6a-a6c9-af8b369452cb","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"3e0b5823-eab0-5e4e-824c-b25e3b7ef0da","title":"Behaviour-Based Quality Assessment of OpenStreetMap Data in Data Scarce Area Using Unsupervised Machine Learning","subtitle":null,"slug":"sotm2025-osm-science-71105-behaviour-based-quality-assessment-of-openstreetmap-data-in-data-scarce-area-using-unsupervised-machine-learning","link":"https://2025.stateofthemap.org/sessions/GCAXF9/","description":"This study introduces a behavior-dependent, unsupervised machine learning approach to assess the intrinsic quality of OpenStreetMap (OSM) data in Dhaka, which is both data-starved and urbanizing rapidly urbanizing area. Leveraging enriched contributor metadata and Principal Component Analysis (PCA), latent behavioral patterns and segmented contributors identified using KMeans and HDBSCAN. The silhouette score for PCA-based clustering was 0.951. The results show superior interpretability of KMeans over HDBSCAN. This repeatable methodology provides a scalable and reference-free solution to take quality assurance of VGI datasets to the front-line, in cases of limited or no authoritative data.\n\nOpenStreetMap (OSM) is an important source of geospatial information in data-starved urban areas, where official geospatial data are scarce, outdated, or are not readily available. Increasing need for current and accurate geospatial data in fast urbanizing and under surveyed regions makes the use of OpenStreetMap (OSM) an essential resource. As one of the most representative Volunteered Geographic Information (VGI), OSM offers a free world map that is editable and can be contributed by millions of people [1]. The tool is an essential component for urban analytics, transport planning, disaster risk reduction, and spatial modeling in the world [2], [3], [4]. Although widely used, the quality of OSM data varies greatly across regions and contributor skill level, and there is no unified, system level quality assurance mechanism [5]. This heterogeneity can be risk inducing for users making use of this data for precision tasks (e.g., routing, land use modeling and infrastructure design) [2], [6].\nTraditional OSM quality assessments rely on extrinsic comparisons with satellite imagery or authoritative datasets, which are often unavailable in the very regions that need the data the most [7], [8]. To overcome this challenge, a reproducible, unsupervised machine learning framework propose to assess OSM data quality intrinsically, based on contributor behavior metadata alone. Specifically, Dhaka —a data-scarce and fast-growing megacity in Bangladesh select as a study area—using the hypothesis that distinct contributor behavioral patterns correlate with different levels of data reliability. This behavior-centric perspective leverages the insight that contributor frequency, recency, thematic focus, and spatial editing behavior can serve as meaningful proxies for feature quality [5], [9].\nRoads and buildings for Dhaka extracts by using by a.osm.pbf with the Pyrosm library. Then enriched feature vector creates for each unique contributor, composed of (total_edits, edit_rate, active_days, spatial_extent, pct_road, pct_building, weekday_activity, days_since_last_edit). Principal Component Analysis (PCA) applies for dimensionality reduction and shows that PC1 roughly represents global mapping activity, while PC2 corresponds to thematic attention (road versus building), and PC3 represents the geographical coverage of contributions. These observations are supported by a feature contribution heatmap (Figure 1.(a)), which indicates that it is reasonable to consider the behavioral features to be interpretable and highly separable in the component-reduced space. PCA has also the purpose of reducing noise and gets the data ready for clustering [10].\nNext, KMeans clustering (with k = 4) and HDBSCAN, a density-based clustering is performed on the PCA-transformed feature set. The silhouette score of the KMeans model was 0.951, suggesting high cohesion within the clusters and good separation between the clusters of behaviors. The PCA cluster scatterplot (Figure 1.(c)) indicates four separated clusters: (1) most participants (Figure 1. (b)) fall in cluster 0, which mainly encompasses casual or one-hit contributors who probably participate in sporadic mapathons, or make large scale imports, (2) cluster 1 and 2 consist of moderate to heavy contributors, who are relatively more or less stable, with richer semantic tagging, and whose edits are spatially distributed, (3) cluster 3 is composed of a small group of “power users,” who are characterized by high activity volume and a large geographical distribution.\nHDBSCAN also use on the same dataset in order to analyze its capability of separating varies densities in clusters and noise. HDBSCAN found small, dense clusters, and labeled a large percentage of contributors as noise. Although helpful for identifying anomalousness and potential vandalism, HDBSCAN was unable to produce as clear clusters for the main contributors as KMeans, likely because the extreme imbalance in contributor engagement. This benchmarking demonstrated that KMeans comes with a better interpretability and cluster stability, and is therefore preferred for behavioral segmentation at the high volumes of OSM dataset.\nTo further verify the clustering, the changes in edit volume over time per cluster investigated, and calculated feature distributions per cluster. The contributor distribution bar chart (Figure 1. (b)) shows that the participation structure in OSM is highly skewed, which is also in line with previous VGI studies [11], [12]. Feature analysis showed that clusters associated with more recent, frequent, and thematically rich editing were also responsible for higher-quality contributions—consistent with prior work linking contributor experience to data quality [5], [9], [13].\nA key contribution of this work is its extensible and repeatable approach. All data processing, feature engineering, PCA and clustering have been performed in Python (Colab) with open-source packages (scikit-learn, geopandas, pyrosm, matplotlib). This method doesn't need any external validation databases, so it is particularly adapted for developing countries and isolated locations, where reference data are limited or unavailable [8].\nThis study contributes methodologically to three areas in the sciences, more precisely to the area of geospatial data science, unsupervised machine learning, and VGI quality assurance in showing how user behavior can be harnessed for deriving inherent data quality. It complements the literature about behavior-based contributor profiling, incorporates dimensionality reduction to facilitate the interpretation of results, and is an argument against central quality assessment as well as one for local quality assessment, which seems feasible even in urban settings with complex mobility patterns.\nPragmatically, this work can help NGOs, local authorities and the OSM community to support the allocation of resources toward data validation and enrichment where coverage is primarily in lower-quality contribution clusters. It also allows hybrid-quality models with behavior signals are augmented with selective extrinsic checks (such as anomaly detection or community verification). For example, contributors from Cluster 3 (power users) may be assigned higher trust weights in quality models, while edits from Cluster 0 may be flagged for further review or enrichment.\nIn conclusion, a new behaviour-based quality assessment of OSM report based on the specific usage of unsupervised machine learning. This cluster- and PCA-driven design is transparent, and interpretable, and completely reproducible. It is a model that addresses the challenges of working in data scarce urban areas and it paves the way for a behavior driven VGI quality models in the framework of urban resilience, infrastructure planning and humanitarian mapping. Future studies will incorporate spatial error measures and use this methodology with longitudinal OSM data for quality evolution monitoring.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Maruf Ahmed"],"tags":["71105","2025","sotm2025","Pulag","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":2,"promoted":false,"date":"2025-10-03T10:35:00.000+02:00","release_date":"2026-03-29T00:00:00.000+01:00","updated_at":"2026-04-01T15:15:09.956+02:00","length":319,"duration":319,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71105-3e0b5823-eab0-5e4e-824c-b25e3b7ef0da.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71105-3e0b5823-eab0-5e4e-824c-b25e3b7ef0da_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71105-3e0b5823-eab0-5e4e-824c-b25e3b7ef0da.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71105-3e0b5823-eab0-5e4e-824c-b25e3b7ef0da.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-osm-science-71105-behaviour-based-quality-assessment-of-openstreetmap-data-in-data-scarce-area-using-unsupervised-machine-learning","url":"https://api.media.ccc.de/public/events/3e0b5823-eab0-5e4e-824c-b25e3b7ef0da","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"20394d69-2a21-542b-87e9-247f3e903810","title":"Leveraging OpenStreetMap for hyperlocal geocoding of Twitter data: A spatiotemporal analysis of the 2016 Haifa (Israel) wildfire","subtitle":null,"slug":"sotm2025-osm-science-81446-leveraging-openstreetmap-for-hyperlocal-geocoding-of-twitter-data-a-spatiotemporal-analysis-of-the-2016-haifa-israel-wildfire","link":"https://2025.stateofthemap.org/sessions/YMCEX7/","description":"This study presents a geospatial framework that combines NLP, machine learning, and GIScience to extract and georeference tweets related to the November 2016 Haifa wildfire, enabling near real-time insights into urban fire dynamics. Using OpenStreetMap and GeoNames to geocode over 16,000 tweets, the researchers demonstrated strong spatial and temporal alignment with official fire incident reports, highlighting social media’s potential as a supplementary data source for disaster response. The approach offers a scalable model for leveraging crowdsourced and user-generated data in emergency informatics, especially in data-scarce regions.\n\nThe increasing frequency and severity of urban wildfires demand new sources of near real-time information to support emergency response and disaster risk reduction [1-4]. In this study, we present an application for extracting and georeferencing the spatiotemporal distribution of tweets associated with the November 2016 wildfire in Haifa, Israel. The unprecedented urban fire influenced densely populated neighbourhoods, caused extensive infrastructural damage, and led to the evacuation of thousands of residents [5]. However, because of the nature and extent of the fire that lasted nearly 3 days, complete and reliable information concerning the emergence and development of new fire locations at the sub-city scale was only partial. Accordingly, management and decision-making procedures were complicated and some of the cascading events along the occurrence of the catastrophe were hard to be detected and addressed. \nThe purpose of the study was to analyse tweets as a potential source of near real-time information and examine to what degree Twitter can be used to assist decision-making during occurrences on urban catastrophes. The implemented research combined Natural Language Processing (NLP), Machine Learning (ML), and Geographic Information Science (GIScience) to filter, classify, and precisely geolocate tweets at the city, neighbourhood or street-level resolution. One of the main components of the established geospatial framework was OpenStreetMap (OSM, https://www.openstreetmap.org, accessed July 2019), used in conjunction with the GeoNames gazetteer (http://www.geonames.org/, accessed July 2019) to construct a comprehensive spatial reference corpus. This enabled the geocoding of both explicitly and implicitly localized tweets that lack GPS metadata—an essential challenge given that only 1–3% of the tweets are geotagged with reliable geographic coordinates [6, 7].\nWe have collected approximately 2.4 million tweets using keywords related to the wildfire (in the Hebrew, Arabic, and English languages) between November 24th –27th, 2016. After classification using topic modelling and RCNN (Recurrent Convolutional Neural Networks) [8], around 114,000 tweets were labelled as relevant to the event. Of these, only 31 tweets were geotagged with geographic coordinates which is obviously an insufficient number of observations to perform spatial analysis. To overcome this shortcoming, we implemented a text-based georeferencing approach leveraging gazetteer data extracted from the OpenStreetMap and GeoNames databases. Accordingly, we converted 18 OSM shapefiles into a unified point dataset containing a wide variety of geographic features—ranging from neighbourhoods and roads to public buildings and natural landmarks. This dataset was merged with a version of the GeoNames corpus to create a point-based localized gazetteer representing the Haifa metropolitan area and its environs. For the purpose of geocoding, each point was associated with its place name in Hebrew, Arabic, or English. Following, the geocoding pipeline consisted of the following key steps: (1) NLP techniques including tokenization, stemming, and stop-word removal to extract named entities and spatial references from the tweet’s metadata [9]; (2) FuzzyWuzzy-based string matching algorithm that computes the Levenshtein distances between strings extracted from the tweet tokens and the place names in our gazetteer [10]; and (3) matchings were assigned a confidence score, which allowed us to filter or weight the credibility and accuracy of the spatial data. For example, georeferenced tweets with scores above 90% were deemed highly reliable for spatial trend detection.\nThe process yielded 16,672 georeferenced tweets distributed across 130 unique localities within Haifa and its close vicinity. Following, we conducted a spatiotemporal inspection by aggregating tweets into 5×5 km grid cells and 8-hour intervals—bins that were informed by the density of localities extracted from the OSM/GeoNames hybrid gazetteer. We used Esri ArcGIS Pro to generate Kernel Density Estimation (KDE) maps [11] and 3D visualizations of the tweets’ distribution. The results presented strong temporal (Figure 1) and spatial (Figure 2) correspondence between georeferenced tweets and the officially reported fire incidents by the Israel Fire and Rescue Services (IFRS). In most of the bins where actual fires were documented, relevant georeferenced tweets were also present. Additionally, tweets often captured the cascading nature of the fire, spreading across Haifa and adjacent towns. Importantly, the spatial granularity enabled us to detect not only urban hotspots but also peripheral areas such as the cities of Daliyat el-Carmel and Akko, which were underrepresented in the media reporting.\nOpenStreetMap played a pivotal role in our ability to extract high-resolution geospatial insights from unstructured social media content. Its richness and multilingual tagging schema allowed effective token matching across Hebrew, Arabic, and English tweets. While GeoNames provided broad administrative and populated place names, OSM uniquely offered sub-city level detail—such as parks, fire stations, neighbourhoods, and points of interest—that dramatically enhanced geocoding precision. The findings of the study demonstrate that OSM and GeoNames might function as an open, extensible backbone for disaster informatics, particularly in regions where official geospatial datasets are sparse or restricted. Additionally, this research showcases a replicable model for fusing crowdsourced geographic data (OSM and GeoNames) with user-generated content (Twitter) to inform emergency response at the hyperlocal scale.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Motti Zohar"],"tags":["81446","2025","sotm2025","Pulag","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":12,"promoted":false,"date":"2025-10-03T06:35:00.000+02:00","release_date":"2026-03-23T00:00:00.000+01:00","updated_at":"2026-03-30T22:31:51.101+02:00","length":386,"duration":386,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/81446-20394d69-2a21-542b-87e9-247f3e903810.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/81446-20394d69-2a21-542b-87e9-247f3e903810_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/81446-20394d69-2a21-542b-87e9-247f3e903810.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/81446-20394d69-2a21-542b-87e9-247f3e903810.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-osm-science-81446-leveraging-openstreetmap-for-hyperlocal-geocoding-of-twitter-data-a-spatiotemporal-analysis-of-the-2016-haifa-israel-wildfire","url":"https://api.media.ccc.de/public/events/20394d69-2a21-542b-87e9-247f3e903810","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"b4421e7d-b01f-5da9-902d-9bc3b85a22ed","title":"What opinions do LLMs/chatbots have about OpenStreetMap?","subtitle":null,"slug":"sotm2025-osm-science-72161-what-opinions-do-llms-chatbots-have-about-openstreetmap","link":"https://2025.stateofthemap.org/sessions/BNYFHF/","description":"In this work we develop a reproducible pipeline for querying multiple LLMs/chatbots in order to access and analyse their opinion on OpenStreetMap by prompting these systems to answer a series of questions on OSM.  People are turning to chatbots and LLMs for opinions and advice on practically every topic. We believe it is important that we begin to assess how chatbots and the LLMs provide information and opinion about OSM. Among other outputs, this work can providing evidence to the OSM community that can be used to shape future public engagement strategies about the project.\n\nThe work described in this abstract is motivated by the rising interest in studying the human-like traits of Large Language Models (LLM). Since LLMs are pretrained on vast amounts of human data, it is reasonable to assume that LLMs can reflect the AOVs (Attitudes Opinions and Values) embedded in the data [1]. LLMs are increasingly being used in open-ended contexts, where the opinions they reflect in response to subjective queries can have a profound impact, both on user satisfaction, and shaping the views of society at large [2]. While LLMs can never have opinions in the same way a person does these systems can be prompted to generate opinion-like text. For example, in Malleson et. al [3] LLMs are used to give opinions on neighbourhood change. Chatbots can output opinionated language, but service providers usually purposely describe these outputs as \"generated perspectives\" rather than actual beliefs [4]. There is usually topic-specific or subject-specific safeguards implemented—especially around political discourse and advice involving risk to human life, health, and so on. Despite this, the huge popularity of chatbots mean that opinionated text produced by chatbots and LLMs can have the potential to be very influential on the reader or user[4,5]. The attraction of these outputs emerge from the fact that these opinions arrive instantly, they are written in very confident, certain, and reassuring language, and often include citations and evidence to support claims. When LLMs are integrated into search engines, social platforms, and other interactive systems, their outputs when the user is seeking opinions or viewpoints can become a user’s first—or only—exposure to a topic[5]. There are positives and negatives to these situations. On the one hand, the systems can provide well-sourced perspectives to a wide audience but they also risks amplifying hidden biases in training data or reflecting other ideological priorities embedded in deployment of these models. Ultimately, their persuasive power arises less from any individual (correct,incorrect,biased) answer and more from their persistent and seamless presence in everyday workflows where over time they can shape public discourse [1].\n\nWith this we decided to investigate the opinions or views expressed by some of the major LLMs when prompted or questioned about their opinions on OpenStreetMap (OSM). Given that LLMs will have been trained on data and information related to OSM - papers, blogs, social media, presentations, etc., can we understand the Attitudes Opinions and Values (AOVs) of LLMs to OSM? To the best of out knowledge, there is no research currently reported about this topic. As all major LLMs have been trained on huge volumes of data from the Internet spanning many years, even decades, we make the assumption that it is very likely that LLMs will have consumed papers, articles, presentations, social media, and so on regarding all aspects of OSM and in particular legacy topics such as: data quality, comparison with official or authoritative sources, accuracy, bias in crowdsourced data, and so on. In related work, Santurkar et. al. [2] developed an extensive framework to study the opinions reflected by LLMs and their alignment with different existing human population opinion-based surveys. These surveys were structured multiple choice-type surveys.  \n\nEveryone involved in the OSM community has their own AOVs about OSM, the OSM ecosystem, OSM strategic direction, and so on. For example, there are often differences in OSM mapper behavior \"explained by clashing values and opinions within and across different mapper subgroups\" [6]. As mentioned above, we are not aware of any similar work having been carried out so far. The majority of work published about LLMs and OSM revolves around using the LLMs as mapping assistants [7,8] or using LLMs to enrich existing data or allow more accessible approaches to mapping [9]. Other works have considered the ability of chatbots such as ChatGPT to take a GIS exam [10] or exploit the deep contextual understandings in LLMs for building function classification in OSM [11]. \n\nCurrently, at the time of writing, we are beginning the implementation of this work. However, a high-level description of our methodology is outlined as follows: \n1. Selection of the target group of LLMs/chatbots: ChatGPT (GPT-4o / GPT-4-turbo), Microsoft Copilo, Anthropic Claude 3/4, Google Gemini Advanced, and Perplexity AI \n2. We have developed a list of questions to ask each LLM with\nsome example prompts: “What do you think about the data quality of OpenStreetMap compared to official government maps?”, “Is OpenStreetMap biased?”, “How reliable is OSM for disaster response?”, “How does the OpenStreetMap community compare to other volunteer-based platforms?”\n3. Assessment and analysis of answers. Here we will assess the answers by applying NLP techniques, using sentiment analysis, and so on. Within step 2 we will ask each LLM/chatbot to rate its own answer. There are some powerful transformers-based sentiment analysis models available for these purposes, such as Barbieri et. al. [12]. Self-rating by the LLMs/chatbots may reveal internal consistency and help us assess their perceived confidence against the actual factual correctness of their answers.\n4. Assess whether each answer (or set of answers for each LLM) is positive or negative in regards to opinions of OSM. Answers will also be rated or assessed by the human authors of this paper. \n5. Use statistical approaches such as Fleiss Kappa[13] for measurement of agreement between the different LLMs/chatbots.\n\nPeople are turning to chatbots and LLMs for opinions and advice, the capacity of these systems for \"nuanced human interactions remains an area of pivotal interest\" [14]. Indeed, some recent studies indicate that individuals with less technical or scientific training may be more likely to follow and find advice useful from chatbots [15] with other studies indicating that responses from ChatGPT were \"perceived as more balanced, complete, empathetic, helpful, and better\" compared to social advice columnist responses [14]. We believe it is important that we begin to assess how chatbots and the LLMs provide information and opinion about OSM. Our work has a number of potential scientific contributions and  practical benefits/implications. These include:\n- Development of a reproducible evaluation framework to provide the means to analyze opinions from LLMs about OSM and crowdsourced spatial data, as well as when prompted with different contexts\n- Providing evidence to the OSM community that can be used in future public engagement strategies \n- Providing the potential to develop transparency into the values reflected by these AI systems and potentially contribute to development of models that are more inclusive of diverse viewpoints around crowdsourced geospatial data\n- Acting as starting point for other researchers to analyze previous surveys carried out on OSM with LLMs/chatbots and subsequently help in the design of future surveys. \n\nAll source code and configuration files support the analyses presented in this work shall be made available as open-source software at the time of publication in a publicly accessible repository. The repository will contain the necessary documentation and pipelines to ensure that this work can be reproduced in full on any compatible systems.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Peter Mooney"],"tags":["72161","2025","sotm2025","Pulag","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":10,"promoted":false,"date":"2025-10-03T10:45:00.000+02:00","release_date":"2026-03-30T00:00:00.000+02:00","updated_at":"2026-04-02T23:15:07.144+02:00","length":332,"duration":332,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/72161-b4421e7d-b01f-5da9-902d-9bc3b85a22ed.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/72161-b4421e7d-b01f-5da9-902d-9bc3b85a22ed_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/72161-b4421e7d-b01f-5da9-902d-9bc3b85a22ed.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/72161-b4421e7d-b01f-5da9-902d-9bc3b85a22ed.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-osm-science-72161-what-opinions-do-llms-chatbots-have-about-openstreetmap","url":"https://api.media.ccc.de/public/events/b4421e7d-b01f-5da9-902d-9bc3b85a22ed","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"b4e478de-8fd3-52b1-988c-f4171e238642","title":"EUthMappers - learning by teaching mapping","subtitle":null,"slug":"sotm2025-osm-science-71029-euthmappers-learning-by-teaching-mapping","link":"https://2025.stateofthemap.org/sessions/EK9UYY/","description":"EUthMappers is an ERASMUS+ initiative that promotes STEAM education in secondary schools throughout the European Union, enhancing students' digital skills and fostering environmental civic engagement. The project includes three universities, students and teachers from five European schools, working for two years. The project has three main phases: development of training materials, local mapping projects and humanitarian mapping collaboration. This presentation outlines the project's implementation steps and showcases the remarkable results achieved by not only participating students but also organizers.\n\nEUthMappers is an ERASMUS+ initiative that promotes STEAM education in secondary schools throughout the European Union, enhancing students' digital skills and fostering environmental civic engagement. The project bridges theoretical learning with practical applications using open-source geospatial tools and collaborative mapping on the OpenStreetMap (OSM) platform. The idea is to establish an European mapping network similar to YouthMappers. This presentation outlines the project's implementation steps and showcases the remarkable results achieved by participating students.\n\nThe project involves three universities (Politecnico di Milano, Universidad Politécnica de Madrid, and Presovska Univerzita V Presov) and five secondary schools located in Italy, Spain, Slovakia, Romania, and Portugal, engaging approximately 160 students in total. The management of the project is done by Euronike, which is an association expert in capacity building activities and EU policies. Until now, the project has been running for two years, and was implemented in three phases.\n1. Development of training materials: a comprehensive training package on open geospatial tools and data analysis was written and made available in six languages (English, Italian, Spanish, Portuguese, Slovakian and Romanian). This training was first delivered to teachers, who then transferred the knowledge to their students.\n2. Local mapping projects: under the guidance of their teachers and with university collaboration, students developed local mapping initiatives from ideation to implementation, focusing on data acquisition and visualization techniques. Across five European cities, pupils carried out mapping-focused projects that combined local engagement with practical outcomes. In Rovereto (Italy), students identified drainage channels on forest roads in the “Bosco della Città” as a central issue and mapped them using GPS and drone imagery. Since such features had never been mapped on OSM, they engaged with the OSM community to propose and share a new mapping methodology. The resulting maps and database now support both routine and extraordinary maintenance by the Rovereto Forestry Service, helping mitigate hydrogeological risks. In Madrid (Spain), pupils mapped key elements related to mobility, leisure spaces, and climate shelters in the Arganzuela neighborhood. Working in groups, they identified and analyzed public space features, and then proposed concrete improvements to enhance young people’s quality of life, which they presented at local events and forums. In Prešov (Slovakia), students focused on tree mapping in central city parks. After ecological training, they collected detailed data—such as species, trunk width, and height—using practical tools and mobile apps. Their results contributed to urban ecological databases and were showcased to the public during their school’s open-door day. In Lisbon (Portugal), pupils explored how graffiti and street art reflect local identity by mapping urban artworks around their school. Through documentation and critical analysis, they created a record of artistic interventions with social and historical value, culminating in a community event promoting dialogue on youth expression in public space. In Pitești (Romania), students diagnosed low public awareness of recycling facilities and mapped the location, accessibility, and types of waste accepted at collection points. Their interactive map aimed to make recycling easier and more visible, encouraging environmental responsibility across the city. These projects show how pupil-led mapping activities can generate innovative tools, influence local planning, and foster active citizenship.\n3. Humanitarian mapping collaboration: as the final activity, students participated in a humanitarian mapping project issued by the United Nations Global Service Centre (UNGSC). There were two workshops about humanitarian mapping and Sustainable Development Goals (SDGs) in order to provide students basic knowledge about humanitarian mapping and their contribution to common goods. Students are then trained on humanitarian mapping simulation projects before actually participating in a real-world scenario proposed by UN Maps, a programme in UNGSC to enhance UN peacekeeping missions operational capabilities through open geospatial information. The training project on OSM Sandbox took place in Idlib refugee camps in Syria, while during actual project students map building data to contribute to generate a three-dimensional digital replica of the city Kandahar, Afghanistan. The generated graphical replica can be used by the UNAMA mission engaging in Virtual Reality (VR) applications to train peacekeeping officers and as a collaborative tool for security and operational briefings. This final phase expanded students' collaborative abilities beyond their classrooms to engage in a meaningful humanitarian project with international impact.\n\nThroughout these activities, students developed teamwork capabilities, creative thinking skills, and innovative data collection methodologies. Not to mention that, the project’s partners also had to think outside the box so as to deliver complicated insights particularly geospatial technology, OSM, collaborative and humanitarian mapping, to the students in the most intuitive way possible. Didactic materials have been researched and developed within the process : (A) interactive engagement tools, (B) custom OSM editors (iDSandbox4ALL and iD4ALL) designed specifically for educational contexts, (C) data visualization of student mapping results.\n\nMore specifically, the interactive engagement tools have been developed to solve the problem of lack of physical interaction among the students, during the online workshop. A set of quizzes were developed, initially with simple questions, then upgraded to a discussion forum, and finally the students were driven  to put their opinion on the world map.  All the quizzes were programmed using Python distributed on the Streamlit platform, therefore they are easily modifiable and reusable for other educational projects.\n\nThe custom OSM editors were designed to solve the digital accessibility gap and to allow an easy practice of mapping activity. Pupils are asked to work on OSM Sandbox servers in simulating projects to acquire necessary skills for the final actual project and not all the schools can provide one computer for each student. Then the application iDSandbox4ALL with a smartphone-friendly interface was developed. Subsequently the new application iD4All, with similar technical specifications for mapping in OSM has been released.\n\nTha last teaching material is related to data statistics and visualization. A set of python notebooks for acquiring OSM Sandbox  and OSM data, making statistics and deploying three dimension maps has been developed. Furthermore the work of the students with precise information of the contribution for each pupil is reported weekly.    \n\nIn conclusion the EUthMappers project is a mapping playground not only for students but also for educators. Through dynamic mapping activities, students collaborate to identify local and global challenges and address them using geospatial technology. Humanitarian mapping encourages students to develop empathy and participate meaningfully in global emergency initiatives.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Quang Huy NGUYEN"],"tags":["71029","2025","sotm2025","Pulag","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":2,"promoted":false,"date":"2025-10-03T10:30:00.000+02:00","release_date":"2026-03-29T00:00:00.000+01:00","updated_at":"2026-04-02T23:15:05.854+02:00","length":347,"duration":347,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71029-b4e478de-8fd3-52b1-988c-f4171e238642.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71029-b4e478de-8fd3-52b1-988c-f4171e238642_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71029-b4e478de-8fd3-52b1-988c-f4171e238642.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71029-b4e478de-8fd3-52b1-988c-f4171e238642.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-osm-science-71029-euthmappers-learning-by-teaching-mapping","url":"https://api.media.ccc.de/public/events/b4e478de-8fd3-52b1-988c-f4171e238642","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"4b0b8b57-7680-5715-850f-e07231900407","title":"World-Map-Explorer – Explore the world with ease","subtitle":null,"slug":"sotm2025-71174-world-map-explorer-explore-the-world-with-ease","link":"https://2025.stateofthemap.org/sessions/ABLRCG/","description":"World-Map-Explorer is a web application that helps the visually impaired learn about the world and understand maps. It is a great pleasure to note that the application was developed by a group of students from the Computer Science Department of Sri Krishnapuram Government Engineering College in collaboration with Zendalona with support of OpenStreetMap Kerala Community.\n\nThis app, which can be used by both the visually impaired and the sighted, is based on the free software OpenStreetMap. This app describes the world based on audio cues with the help of a screen reader. Existing applications like Google Maps cannot be used with a keyboard. Therefore, the visually impaired cannot use map applications. They understand the structure of areas by swiping their hands through the lines and dots that stand out on the tactile map. They understand where a place is by waving their hands over the place name written in Braille.\n\nWorld-Map-Explorer is a web application that helps the visually impaired learn about the world and understand maps. It is a great pleasure to note that the application was developed by a group of students from the Computer Science Department of Sri Krishnapuram Government Engineering College in collaboration with Zendalona with support of OpenStreetMap Kerala Community.\n\nThis app, which can be used by both the visually impaired and the sighted, is based on the free software OpenStreetMap. This app describes the world based on audio cues with the help of a screen reader. Existing applications like Google Maps cannot be used with a keyboard. Therefore, the visually impaired cannot use map applications. They understand the structure of areas by swiping their hands through the lines and dots that stand out on the tactile map. They understand where a place is by waving their hands over the place name written in Braille.\n\nBut this has many limitations. It only provides a few details about the place. And it is difficult to carry it anywhere. That is why the Map World-Map-Explorer, which can be easily used from anywhere, is opening up new possibilities for visually impaired peoples.\n\nhttps://map.zendalona.com/\nThis Project Launched by Dr. R Bindu, incumbent Minister for Higher Education and Social Justice, Government of Kerala. https://www.facebook.com/drrbindhu/posts/pfbid02vPrXmPFuG6UE2p8yymnqxVKWwUTh2nA5FkDfw37qCNgrntCqiczPNoc8hJK81H7kl\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Manoj Karingamadathil"],"tags":["71174","2025","sotm2025","Software Development","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 2"],"view_count":0,"promoted":false,"date":"2025-10-04T06:30:00.000+02:00","release_date":"2026-04-03T00:00:00.000+02:00","updated_at":"2026-04-03T14:10:08.731+02:00","length":1150,"duration":1150,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71174-4b0b8b57-7680-5715-850f-e07231900407.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71174-4b0b8b57-7680-5715-850f-e07231900407_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71174-4b0b8b57-7680-5715-850f-e07231900407.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71174-4b0b8b57-7680-5715-850f-e07231900407.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-71174-world-map-explorer-explore-the-world-with-ease","url":"https://api.media.ccc.de/public/events/4b0b8b57-7680-5715-850f-e07231900407","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"56996cf2-4ae2-5823-96b3-129fe97a566b","title":"Platinum Sponsor Session: From Edits to Impact - TomTom’s Journey with OpenStreetMap Communities","subtitle":null,"slug":"sotm2025-81612-platinum-sponsor-session-from-edits-to-impact-tomtom-s-journey-with-openstreetmap-communities","link":"https://2025.stateofthemap.org/sessions/TZNMJG/","description":"TomTom partners with OSM communities worldwide to build richer maps through collective action. In this SotM Global event 2025, we share how we support university mapathons, YouthMappers, and humanitarian mapping in 189 countries, reflecting on learnings and challenges in scaling community engagement while advancing OSM’s mission through meaningful, local partnerships.\n\nTomTom and the OpenStreetMap Community share a common vision: building a map that reflects and serves the world, powered by local knowledge and collective action. In this session, We will be taking you behind the scenes of TomTom’s partnerships with OSM communities worldwide, demonstrating how collaborative efforts are strengthening the quality, freshness, and inclusiveness of OpenStreetMap data.\nFrom hosting mapathons with universities and YouthMappers chapters to supporting humanitarian mapping during crises, TomTom’s initiatives have connected 3,397+ OSM contributors across 189 countries. We will share real examples of how these collaborations are transforming local mapping ecosystems while providing technical resources, training, and data that support contributors in making impactful edits.\nWe will also discuss scaling corporate-community collaborations responsibly, challenges faced in diverse regions, and opportunities for co-creating projects that benefit both OSM and local communities. Participants will leave with ideas and inspiration on how to replicate or adapt these approaches to engage more mappers, foster skill-building, and deepen OSM’s reach.\nJoin us to discover how mapping together can empower communities and create a richer, more inclusive map of the world.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Hajar ElOuafi","Kiran Ahire"],"tags":["81612","2025","sotm2025","Platinum Sponsor Sessions","Pulag","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":4,"promoted":false,"date":"2025-10-03T11:20:00.000+02:00","release_date":"2026-03-31T00:00:00.000+02:00","updated_at":"2026-04-03T10:15:05.450+02:00","length":1450,"duration":1450,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/81612-56996cf2-4ae2-5823-96b3-129fe97a566b.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/81612-56996cf2-4ae2-5823-96b3-129fe97a566b_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/81612-56996cf2-4ae2-5823-96b3-129fe97a566b.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/81612-56996cf2-4ae2-5823-96b3-129fe97a566b.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-81612-platinum-sponsor-session-from-edits-to-impact-tomtom-s-journey-with-openstreetmap-communities","url":"https://api.media.ccc.de/public/events/56996cf2-4ae2-5823-96b3-129fe97a566b","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"935a8500-a4f5-5cee-b52e-20bc403cbd62","title":"PlaceCrafter: Curating Urban Functional Regions through Platial Clustering of OpenStreetMap Points of Interest","subtitle":null,"slug":"sotm2025-osm-science-72397-placecrafter-curating-urban-functional-regions-through-platial-clustering-of-openstreetmap-points-of-interest","link":"https://2025.stateofthemap.org/sessions/LJGVP9/","description":"The world is not just made of streets, buildings, and zones; it is shaped by how people engage and interact with places in their everyday lives. This abstract presents a web-based geospatial tool that supports the mapping of these lived places and locales named PlaceCrafter. PlaceCrafter supports researchers in identifying platial regions: functional, human-centred areas that cross administrative and formal boundaries. The framework is built on OpenStreetMap, combining (near) real-time clustering, analysis, and statistical validation of these platial regions. PlaceCrafter supports researchers in exploring the subjective experiences of place through existing datasets and city structures.\n\nContemporary urban analysis requires tools and analytical software that can not only capture the physical structure of the city, but also the dynamic and human-centred places that can emerge from everyday interactions. While these technologies, such as existing OpenStreetMap (OSM) [1] views and Geographic Information Systems (GIS) [2] are effective for spatial tasks, these abstractions fail to understand the notions of place when compared to space [3]. Recent work [4, 5] has sought to shift the focus towards platial information systems, tools which situate the human experience, subjective knowledge, and fuzzy representation as a comparison to existing spatial systems. These fuzzy, subjective, and personal representations attempt to model ‘place’ as a contrast to space, which may not align with traditional geometries or formal administrative zones. \n\nPlaceCrafter responds to this challenge by integrating a spatial-platial [6] approach to identifying regions that are functionally cohesive, representing dense and meaningful concentrations of specific points of interest (POI). The POIs are traditional representations of space within GIS [3], such as cafes, restaurants, museums, pathways, and places of worship. Rather than relying on the top-down designation of locations, PlaceCrafter supports users, analysts, and researchers curating clusters that represent how space is used as opposed to administratively divided. The approach aligns with recent calls in GIScience to shift from ‘space’ to ‘place’ in smart city analysis [7] and to continue building on work which has operationalised the sense of place in urban contexts [8].\n \nPlaceCrafter is designed not just to analyse space, but to make its platial structure visible, explorable, and comprehensible to analysts, researchers, and planners. Figure 1 presents the web-based application, developed in TypeScript using React, Vite, Leaflet, Turf.js, and D3.js. The software uses the Overpass API [9] to retrieve (near, depending on number of POIs loaded) real-time user-filtered POI data. The datasets are organised into semantic categories based upon the existing OSM semantic structures [10]; these filters can be customised dependent on the analytical task. This functionality supports the broad purpose of the web-based application, which is to enable researchers and practitioners to understand city form and structure through a platial lens. \n\nPlaceCrafter is structured around four phases guided by the OSM filtering approach: the initial phase (1) focuses on filtering and selection of relevant OSM categories, building upon existing work, such as POI Pulse [11] which classifies regions using semantic signatures, and user behaviour to generate profiles of locations in the Los Angeles area and ClusterRadar [12] which supports comparative spatial clustering and parameter tuning through interactive visualisation to examine how clusters change temporally; the second phase (2) is where the fuzzy clustering approaches are applied interactively and include K-Means [13] for compact cluster formation, DBSCAN [14] for spatial structures, and hierarchical clustering for multi-level spatial structures. These clustering methods are applied to the filtered POI data to reveal platial regions.  \n  \nThe penultimate phase (3) focuses on statistical validation, where each clustered region is evaluated using established spatial metrics. These evaluations include the nearest neighbour index to assess spatial clustering, silhouette scores [15] for understanding cluster coherence, and spatial autocorrelation is measured using a simplified Moran’s I statistic for insights into category-based dependency [16]; The final phase is (4) visualisation, which explores the concept of platial readability, where each region is presented not just spatially but semantically, with data supported by POI type, diversity score, and density metrics. Additionally, the platial visualisation techniques used support the emerging approaches to conveying ambiguity, overlap, and functional gradients [3, 17]. The visualisation subsystem is modular for a wide array of end-user requirements, supporting fuzzy spray can visualisation and region influence grids as presented in Figure 2, in addition to convex hulls, kernel density heatmaps, and region quality indicators.\n \nFigure 3 presents a case study using PlaceCrafter to analyse Nottingham, United Kingdom and the surrounding areas. The POI filtering focused on tourism, historical, leisure, and natural categories. A total of 534 POIs were clustered into 18 functional regions using K-Means. There were 344 historical POIs and 111 leisure POIs as the largest categories from the filtering. The spatial pattern reflects the diverse landscape of Nottingham, with dense clusters in the city centre based around historical POIs, with suburban and rural areas having a more diffuse pattern of historic and leisure clustering. The statistical validation showed strong autocorrelation using Moran’s I (0.68) and a high internal cohesion Silhouette score (0.83), confirming the utility of platial clustering in capturing real-world functional structures. \n\nPlaceCrafter offers a powerful and emerging way to engage with spatial data, but its outputs are shaped by the characteristics of OSM. The crowd-sourced nature of this data means that certain POI types particularly those tied to commerce or tourism, are more visible than the informal or everyday categories more relevant to place-based ambiguity or subjective personal experiences. However, previous research has shown that OSM data is generally reliable for urban-area analysis, despite some noise [18]. Additionally, the clustering algorithms respond to spatial density and POI tag semantics, which do not explicitly capture human-perceived understandings of place boundaries, indicating a need for inclusion of social media data such as those used in POI Pulse [11] or the subjective experiences captured from linked walking narratives in WalkGIS [19].\n\nPlaceCrafter is being developed to be an open-source software which enables broader use, adaptation, and academic contributions in platial information systems. Planned expansions include supporting historical analysis to track the evolution of platial regions and their changes over time. We intend to further expand the platform to incorporate multi-comparative views of platial functional regions, enabling cities, timeframes, and thematic domains to be compared in real-time. We also intend to conduct a qualitative study investigating how users, analysts, and researchers interpret and engage with PlaceCrafters outputs in practice. These improvements will help the validation of the tool’s interpretability and role as a flexible analytical environment for platial analysis. \n\nAs cities continue to change, understanding how people engage with their environments becomes increasingly layered and complex. Recognising that cities are more than their administrative boundaries, while valuing the role of spatial data, PlaceCrafter helps uncover the functional geography of place by clustering OSM POIs into meaningful regions that reflect how place is used, shared, and shaped through the spatial logic embedded in mapped data.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["James Williams"],"tags":["72397","2025","sotm2025","Pulag","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":6,"promoted":false,"date":"2025-10-03T06:30:00.000+02:00","release_date":"2026-03-23T00:00:00.000+01:00","updated_at":"2026-03-30T22:30:55.799+02:00","length":524,"duration":524,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/72397-935a8500-a4f5-5cee-b52e-20bc403cbd62.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/72397-935a8500-a4f5-5cee-b52e-20bc403cbd62_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/72397-935a8500-a4f5-5cee-b52e-20bc403cbd62.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/72397-935a8500-a4f5-5cee-b52e-20bc403cbd62.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-osm-science-72397-placecrafter-curating-urban-functional-regions-through-platial-clustering-of-openstreetmap-points-of-interest","url":"https://api.media.ccc.de/public/events/935a8500-a4f5-5cee-b52e-20bc403cbd62","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"32486f1f-7de4-592d-ba71-6b83f43d8d19","title":"Lightning Talks II","subtitle":null,"slug":"sotm2025-73301-lightning-talks-ii","link":"https://2025.stateofthemap.org/sessions/NVHF3U/","description":"## Utilizing OSM with QGIS\n_by Ariel Dome_\n\n## Open Mapping potential in Jakarta Metropolitan: Case Study of Mapillary Capture  in Depok City\n_by Yabes Butar Butar_\n\n## People data tracing movement with GPS\n_by CJ Capuli_\n\n## All of five km\n_by Ewen Hill_\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["SotM Working Group"],"tags":["73301","2025","sotm2025","Lightning Talks","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 2"],"view_count":4,"promoted":false,"date":"2025-10-04T04:30:00.000+02:00","release_date":"2026-04-01T00:00:00.000+02:00","updated_at":"2026-04-02T23:00:05.326+02:00","length":1546,"duration":1546,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/73301-32486f1f-7de4-592d-ba71-6b83f43d8d19.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/73301-32486f1f-7de4-592d-ba71-6b83f43d8d19_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/73301-32486f1f-7de4-592d-ba71-6b83f43d8d19.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/73301-32486f1f-7de4-592d-ba71-6b83f43d8d19.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-73301-lightning-talks-ii","url":"https://api.media.ccc.de/public/events/32486f1f-7de4-592d-ba71-6b83f43d8d19","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"b3a87bf8-09b0-504a-8a17-58eef18ea8d7","title":"User testing of AI-assisted mapping tool fAIr","subtitle":null,"slug":"sotm2025-osm-science-72051-user-testing-of-ai-assisted-mapping-tool-fair","link":"https://2025.stateofthemap.org/sessions/WJDTBX/","description":"Humanitarian OpenStreetMap Team proposed the fAIr project (https://fair.hotosm.org/) - an open-source AI-assisted mapping tool. This study describes our user testing organized to compare AI-assisted mapping of buildings in the fAIr tool and classic manual mapping of buildings in the JOSM editor without AI assistance. 26 participants took part in the experiment. Efficiency (number of buildings mapped per minute), effectiveness (proportion of buildings mapped correctly), and satisfaction (feedback from participants) were analyzed.\n\nIntroduction \nHumanitarian organizations need maps for their activities. However, there is a lack of quality maps in many countries. The Humanitarian OpenStreetMap Team (HOT) is coordinating a global effort for humanitarian mapping, where volunteers create maps of remote locations. They use satellite imagery to search for roads and buildings and draw them into the OpenStreetMap. Despite all the efforts of volunteers, many regions remain insufficiently mapped (Herfort et al., 2021). Artificial intelligence (AI) is already used to analyze satellite imagery. There is thus an opportunity to use AI for humanitarian mapping as well. HOT proposed the fAIr project (https://fair.hotosm.org/) – an open-source AI-assisted mapping tool (HOT, 2025). In a presentation from SOTM 2024, Anna Zanchetta (Zanchetta, 2024) analyzed the performance of fAIr in detecting buildings in different conditions. \n\nOur group, located in the Department of Geography, Faculty of Science, Masaryk University, Czechia, is part of the humanitarian mapping community Missing Maps Czechia and Slovakia. We were in contact with the fAIr developers from HOT – Omran Najjar, Kshitij Sharma, and Anna Zanchetta. They asked us if we could organize user testing of the first version of fAIr. The goal was to compare AI-assisted mapping buildings with the fAIr tool and classic manual mapping buildings without AI assistance. The popular JOSM editor, which offers experienced tools for manual building mapping, was used for comparison.\n\nMethodology \nThe experiment took place in an online environment during November 2024. The experiment took place in four different sessions, so each participant could choose the day and time that suited them best. Training datasets were prepared for 24 different localities around the globe. 26 participants took part in the experiment – 14 beginners and 12 experienced contributors. The participants were divided into groups A and B, so there were similar numbers of experienced contributors and beginners in both groups. Each participant was assigned one location for mapping in fAIr and one location for mapping in JOSM. Each session was structured as follows: \n-An explanation of the rules for correctly and incorrectly mapped buildings. \n-45 minutes: group A mapped in fAIr, group B mapped in JOSM \n-5 minutes: break\n-45 minutes: group B mapped in fAIr, group A mapped in JOSM \n-Participants filled in the feedback questionnaire. \nValidators evaluated the mapped buildings, and the results were analyzed. Efficiency (number of buildings mapped per minute), effectiveness (proportion of buildings mapped correctly), and satisfaction (feedback from participants) were analyzed.\n\nResults\nMapping in JOSM (86.08 %) was significantly more accurate than the fAIr tool (78.22 %).  Mapping in JOSM (4.23 buildings/min) was significantly faster compared to the fAIr tool (1.97 buildings/min).\nResults of experienced users and beginners were also compared. Beginners mapped faster in JOSM (2.89 buildings/min) than in fAIr (2.08 buildings/min), but the mapping quality was almost the same in JOSM (79.28 %) and fAIr (80.41 %). Experienced contributors mapped much faster (5.69 buildings/min) and with higher quality (93.45 %) in JOSM than in fAIr (1.84 buildings/min and 75.38 %).\n\nInterestingly, it means that beginners have better results of mapping quality (80.41%) and mapping speed in fAIr (2.08 buildings/min) than experienced contributors (75.38 % and 1.84 buildings/min). On the contrary, experienced contributors have much better results in mapping quality (93.45 %) and mapping speed (5.69 buildings/min) in JOSM than beginners (79.28 % and 2.89 buildings/min).\n\nWe also analyzed the influence of the complexity of mapping locality, e.g., the density of buildings in the mapped site and the quality of the imagery. For easy localities, experienced contributors generally have higher mapped building counts, indicating better fAIr performance on simpler tasks. For complex localities, the number of buildings mapped by experienced contributors drops significantly, suggesting that complex localities pose a challenge even for experienced contributors. A higher number of errors was found in complex localities, where AI likely generated lower-quality building outline designs. Interestingly, for complex localities, beginners mapped more buildings than experienced contributors, which may suggest that they focus on quantity. This could indicate potential quality issues.\n\n23 participants answered a feedback questionnaire. They indicated that fAIr was not their preferred tool for humanitarian mapping, but more than half of them said they would return to it. In their opinion, fAIr is suitable for beginners. On the other hand, most participants stated that the buildings generated by AI need to be further modified. Most participants claimed that mapping in the JOSM editor was faster.\n\nDiscussion \nAfter evaluating the results and feedback from participants, recommendations were proposed for the further development of the fAIr tool:\n-fAIr is a beginner-friendly tool. It is suitable for quick mapping of simple features. Users appreciated the simple interface and AI support. Beginners have similar results in fAIr as experienced contributors, sometimes even better. Users suggested combining both tools – starting with quick mapping in fAIr and refining the quality in JOSM. \n-Participants had issues with AI-generated shapes, as fAIr did not allow proper mapping of more complex buildings. Often, separate buildings were mistakenly combined into a single outline. These problems were greater in the complex areas (e.g., density of the mapped site and the quality of the imagery), where the AI seemed unable to design good building outlines. \n-The results were influenced by the fact that JOSM includes tools for mapping buildings, which gives it an advantage over fAIr. However, JOSM does not have specialized tools for mapping other features (e.g., roads). It is possible that mapping these features would yield more favorable results for fAIr. \n-Users believe that fAIr has the potential to improve the mapping process and could be helpful if key problems are addressed. \n\nSome problems will likely be solved using innovations in the new version of fAIr – e.g., using a YOLO-type ML model and increasing the speed of prediction of building outlines (HOT, 2025). A continuation of our study could be to repeat user testing with the new version of fAIr – e.g., compare the results of the RAMP model from the original version of fAIr with the YOLO model from the latest version.\n\nThe results of the experiment (HOT \u0026 MUNI, 2024) were published as a scientific report: \nhttps://drive.google.com/file/d/10I1wz1BHsBXmBjXMx99KzqBwaVpj5MJN/view?usp=sharing \nRaw data from the experiment can be found here: \nhttps://docs.google.com/spreadsheets/d/10LUGkN10wUt-VBRJoNFdCxIEOpvQkyWg/edit?usp=sharing\u0026ouid=116154085074085968041\u0026rtpof=true\u0026sd=true\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Radim Štampach"],"tags":["72051","2025","sotm2025","Pulag","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":24,"promoted":false,"date":"2025-10-03T09:00:00.000+02:00","release_date":"2026-03-24T00:00:00.000+01:00","updated_at":"2026-04-01T23:45:05.708+02:00","length":1812,"duration":1812,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/72051-b3a87bf8-09b0-504a-8a17-58eef18ea8d7.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/72051-b3a87bf8-09b0-504a-8a17-58eef18ea8d7_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/72051-b3a87bf8-09b0-504a-8a17-58eef18ea8d7.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/72051-b3a87bf8-09b0-504a-8a17-58eef18ea8d7.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-osm-science-72051-user-testing-of-ai-assisted-mapping-tool-fair","url":"https://api.media.ccc.de/public/events/b3a87bf8-09b0-504a-8a17-58eef18ea8d7","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"343d8876-eb73-5416-8252-8553e64510f3","title":"OSMlanduse: A dataset of European Union land use at 10 m resolution derived from OpenStreetMap and Sentinel-2","subtitle":null,"slug":"sotm2025-osm-science-81445-osmlanduse-a-dataset-of-european-union-land-use-at-10-m-resolution-derived-from-openstreetmap-and-sentinel-2","link":"https://2025.stateofthemap.org/sessions/BDTYZ9/","description":"OSMlanduse is the first EU-wide 10 m land-use map integrating 3.2 million OpenStreetMap geometries with Sentinel-2 imagery through an open deep-learning workflow. Delivering CORINE-level thematic detail with finer spatial resolution, it achieves 89% accuracy, providinng wall-to-wall coverage while retaining OSM’s sub-metre detail where available. Released under open licences with reproducible scripts, it supports applications from climate modelling and biodiversity surveys to urban planning and policy monitoring. By uniting crowdsourced mapping and Earth observation, OSMlanduse demonstrates a scalable, transparent approach to producing reliable, high-resolution land-use information at continental scale.\n\nSpatially resolved information on present-day land use (LU) is fundamental for climate-mitigation tracking, food-system monitoring and spatial planning, yet Europe still relies on\ninventories such as CORINE Land Cover (CLC) that are updated quinquennially at 100 m and under-represent the urban micro-mosaic. Meanwhile, new 10 m remote-sensing maps excel at spectral land-cover separation but lack the thematic richness contributed by citizens through OpenStreetMap (OSM).\nWe introduce OSMlanduse, the first European Union-wide LU map at 10 m resolution that fuses 3.2 million OSM geometries with Sentinel-2 multispectral composites by means of a completely open workflow. The product supplies CLC-level thematic detail while matching the spatial grain of Copernicus imagery.\nOur objective was to demonstrate that globally recurrent, freely licensed data streams can be combined through deep learning to overcome the spatial incompleteness of volunteered geographic information. We converted the March 2020 OSM planet snapshot into 13 CLC classes, directly labelling 61.8 % of EU28 territory. For the unlabeled remainder we trained country-specific residual convolutional neural networks (ResNets) on medoid composites of cloud-masked Sentinel-2 top-of-atmosphere reflectance, then mosaicked predictions and original labels into a seamless raster–vector hybrid.\nAccuracy was assessed with 4 616 stratified reference points interpreted independently on sub-metre Bing and Google imagery. The map attains 89 % overall accuracy (95 % CI ± 2 %); producer’s accuracies range from 77 % (shrub/herbaceous vegetation) to 99 % (water bodies), while user’s accuracies exceed 93 % for agricultural strata. Most confusion arises between spectrally similar urban greens and semi-natural grasslands, or between early construction sites and bare soil, reflecting both spectral ambiguity and occasional tag noise.\nOSMlanduse inherits sub-metre geometric detail wherever OSM mapping is dense—Dutch canal parcels, German allotment gardens, Romanian farmyards—yet guarantees wall-to-wall coverage through 10 m raster infill. GeoTIFF tiles, training rasters and the OSM-to-CLC translation table are released under CC-BY 4.0 (DOI: 10.11588/data/IUTCDN) and visualised at https://osmlanduse.org; GPL-3.0 scripts ensure full reproducibility.\nThree methodological insights emerge. First, the current density and thematic granularity of OSM LU tags suffice to train deep networks that generalise across divergent biogeographic regions without external annotation campaigns. Second, multi-temporal medoid compositing plus per-country modelling dampens atmospheric noise and phenological divergence, enabling continental consistency from uncalibrated top-of-atmosphere data.\nThird, open-science principles—public code, permissive licences, cloud execution—place high-resolution mapping within reach of resource-constrained institutions.\n\nThe dataset opens new avenues for investigating LU dynamics, from crop-rotation detection and peri-urban sprawl quantification (SDG 11.3.1) to habitat-specific sampling frames for biodiversity surveys and downscaling of economic statistics. Moreover, its billions of labelled pixels address the chronic scarcity of public training corpora highlighted by recent computer-vision studies.\nLimitations persist. Crowdsourced tags remain temporally asynchronous; the March 2020 snapshot necessarily precedes pandemic-era peri-urban expansion. Spectral ambiguity endures in arid shrublands and gravel pits, and the 10 m grid misses features narrower than one pixel such as hedgerows. Nevertheless, by releasing not only the final map but all processing scripts under GPL-3.0 we invite replication, auditing and regional adaptation.\nTraining was performed on the FAO SEPAL cloud using Nesterov-accelerated Adam, batch size 64 and early stopping on 20 % held-out OSM tiles per country. Managing the 1.2 TB Sentinel-2 archive through orbit-wise partitioning and raster caching allowed execution of the full continental workflow within 96 GPU-hours on commodity instances. Sampling 5 000 patches per class and tile balanced the abundant yet noisy training labels while preserving rare categories such as mines and wetlands.\nWhile earlier studies either harvested OSM tags to create fragmented local LU layers or applied deep networks to remote-sensing mosaics devoid of human semantics, our approach unifies both paradigms at continental scale. This synergy circumvents the classic cartographic trade-off between spatial detail, thematic depth and geographic extent, whilst adhering strictly to open-data licences.\nPolicy makers can exploit OSMlanduse for rapid appraisal of agricultural subsidy eligibility, Natura 2000 management and disaster-risk exposure without waiting for the next CLC release. Sub-municipal authorities may integrate the vector segments directly into cadastre systems, whereas climate modellers gain higher-resolution surface descriptors that improve albedo and evapotranspiration parameterisation in regional Earth-system simulations. The open architecture ensures that local corrections contributed through everyday OSM editing propagate into forthcoming versions, creating a virtuous cycle of citizen engagement and scientific refinement.\nBy making the entire workflow transparent we aspire to seed analogous initiatives on other continents, particularly in data-scarce regions where OSM growth is accelerating. Replication requires only Sentinel-2 coverage and a minimally tagged OSM backbone—resources already available in most inhabited areas. We therefore invite collaboration from both researchers and local mapping communities to test transferability, co-produce validation data and push the frontier of open land-use science.\nWe conclude that crowdsourced mapping and Earth observation form a complementary, fully open pipeline capable of producing reliable 10 m LU information at continental scale, bridging the thematic gap between authoritative 100 m inventories and purely spectral cover maps.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Michael Schultz"],"tags":["81445","2025","sotm2025","Pulag","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":22,"promoted":false,"date":"2025-10-03T06:40:00.000+02:00","release_date":"2026-03-24T00:00:00.000+01:00","updated_at":"2026-03-30T22:32:52.468+02:00","length":404,"duration":404,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/81445-343d8876-eb73-5416-8252-8553e64510f3.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/81445-343d8876-eb73-5416-8252-8553e64510f3_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/81445-343d8876-eb73-5416-8252-8553e64510f3.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/81445-343d8876-eb73-5416-8252-8553e64510f3.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-osm-science-81445-osmlanduse-a-dataset-of-european-union-land-use-at-10-m-resolution-derived-from-openstreetmap-and-sentinel-2","url":"https://api.media.ccc.de/public/events/343d8876-eb73-5416-8252-8553e64510f3","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"07ddfbd6-1d7b-594f-b6b2-66fc22544368","title":"The Ugandan Geo Quests: Mapping Libraries and Museums into the Knowledge Commons","subtitle":null,"slug":"sotm2025-71082-the-ugandan-geo-quests-mapping-libraries-and-museums-into-the-knowledge-commons","link":"https://2025.stateofthemap.org/sessions/MTS9TS/","description":"Across Uganda, countless community libraries and local museums hold untold stories, cultural memory, and untapped knowledge—but remain invisible on the digital map. The Ugandan Geo Quests project aims to change that.\n\nThis session introduces two complementary national mapping initiatives: The Ugandan Libraries Geo Quest and The Ugandan Museums Geo Quest, which leverage OpenStreetMap, Wikidata, and Wikipedia to bring local knowledge institutions into the global digital commons.\n\nWe’ll explore how we:\n\nMobilized local communities, Wikimedians, and mappers for participatory documentation\nUsed tools like uMap, ODK Collect and KoboToolbox to ensure data quality and visibility\nLinked OSM data with Wikidata and Wikipedia for multi-platform discoverability\nAddressed challenges around verification, tagging, and sustainability\n\nAttendees will gain insights into designing thematic mapping campaigns that go beyond infrastructure—to elevate culture, education, and heritage in open data ecosystems. Whether you're a mapper, librarian, or advocate for digital equity, this session offers inspiration on how maps can preserve identity and empower communities.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Micheal Kaluba"],"tags":["71082","2025","sotm2025","Mapping","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 2"],"view_count":2,"promoted":false,"date":"2025-10-04T04:00:00.000+02:00","release_date":"2026-03-31T00:00:00.000+02:00","updated_at":"2026-04-02T22:30:05.869+02:00","length":1638,"duration":1638,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71082-07ddfbd6-1d7b-594f-b6b2-66fc22544368.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71082-07ddfbd6-1d7b-594f-b6b2-66fc22544368_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71082-07ddfbd6-1d7b-594f-b6b2-66fc22544368.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71082-07ddfbd6-1d7b-594f-b6b2-66fc22544368.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-71082-the-ugandan-geo-quests-mapping-libraries-and-museums-into-the-knowledge-commons","url":"https://api.media.ccc.de/public/events/07ddfbd6-1d7b-594f-b6b2-66fc22544368","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"3cd61760-7e81-5dad-a9ae-47f8c268e985","title":"Addressing Participation Gaps in Nepal’s OSM Ecosystem: Strategies for Long-Term Community Retention","subtitle":null,"slug":"sotm2025-71159-addressing-participation-gaps-in-nepal-s-osm-ecosystem-strategies-for-long-term-community-retention","link":"https://2025.stateofthemap.org/sessions/RUCRPS/","description":"Nepal’s OpenStreetMap (OSM) community is currently experiencing a noticeable decline in active participation and long-term engagement. While the ecosystem once thrived on regionally supported events and student-led enthusiasm, it now faces a rise in passive contributors and a lack of continuity in mapping efforts. This trend is particularly concerning in a context like Nepal, where access to reliable and up-to-date geospatial data remains limited, and OSM presents a valuable, open-source alternative for inclusive mapping and data democratisation. A key factor contributing to this decline is the narrow framing of OSM as a repetitive editing platform, often reinforced by conventional training sessions that overlook the broader creative and applied potential of the tool. Innovative and accessible platforms such as Mapillary, MapSwipe, and OSM-based design tools—which can make mapping more engaging and relevant to everyday challenges—are rarely introduced or integrated into learning experiences. Drawing on my own experience organising initiatives such as the OSM Hackfest and thematic map design based on OSM dataset competitions, I have witnessed how student engagement can be restored when OSM is presented not just as an editing tool but as a gateway to problem-solving in urban planning, disaster resilience, and many more potentials of the OSM datasets. More importantly, there is a need for advocacy for a shift in narrative: from simply contributing data to understanding the importance of OSM and utilising it. By embedding OSM awareness from the student level to policymaking stakeholders, Nepal can foster a resilient and self-sustaining mapping ecosystem.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Dibikshya Shrestha"],"tags":["71159","2025","sotm2025","Community and Foundation","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":6,"promoted":false,"date":"2025-10-03T11:20:00.000+02:00","release_date":"2026-03-23T00:00:00.000+01:00","updated_at":"2026-03-30T22:52:31.028+02:00","length":448,"duration":448,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71159-3cd61760-7e81-5dad-a9ae-47f8c268e985.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71159-3cd61760-7e81-5dad-a9ae-47f8c268e985_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71159-3cd61760-7e81-5dad-a9ae-47f8c268e985.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71159-3cd61760-7e81-5dad-a9ae-47f8c268e985.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-71159-addressing-participation-gaps-in-nepal-s-osm-ecosystem-strategies-for-long-term-community-retention","url":"https://api.media.ccc.de/public/events/3cd61760-7e81-5dad-a9ae-47f8c268e985","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"c35eab1e-5201-50b4-8676-497cec3ce328","title":"Resilience Starts with a Map: Community-Led OSM Action in Dhaka’s Climate-Vulnerable Settlements","subtitle":null,"slug":"sotm2025-71128-resilience-starts-with-a-map-community-led-osm-action-in-dhaka-s-climate-vulnerable-settlements","link":"https://2025.stateofthemap.org/sessions/8KKLCS/","description":"Dhaka, one of the most climate-affected cities in South Asia, is home to millions of migrants living in informal urban settlements. These densely populated areas are increasingly exposed to environmental and health hazards, particularly seasonal waterlogging and recurring dengue outbreaks. Despite the severity of these issues, many of these settlements remain underrepresented in official datasets, limiting the ability to implement targeted and effective resilience measures.\n\nAs part of our Capstone Project under the Climate Resilience Fellowship (CRF), this initiative leveraged OpenStreetMap (OSM) to conduct a community-led, GIS-based multi-hazard assessment in some of Dhaka’s most climate-vulnerable settlements. Our work followed a two-phase approach:\n\nField-based data collection and mapping, where youth and women from local communities actively identified water accumulation zones and dengue breeding hotspots using OSM tools.\n\nAwareness building and local preparedness activities, using the mapped data to facilitate community dialogues and promote action for health and disaster resilience.\n\nThrough this participatory mapping effort, we not only generated critical geospatial data but also strengthened local capacity to respond to climate and health risks. OSM served as both a data platform and a tool for empowerment, enabling residents to visualize their vulnerabilities and advocate for solutions.\n\nThis talk will showcase how open mapping, when integrated with community engagement and local knowledge, can effectively support disaster risk reduction, public health planning, and climate adaptation in marginalized urban areas. We will also reflect on the potential for replicating this model in other cities facing similar climate and health challenges.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Faiza Waziha"],"tags":["71128","2025","sotm2025","User Experiences","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":4,"promoted":false,"date":"2025-10-03T11:00:00.000+02:00","release_date":"2026-03-22T00:00:00.000+01:00","updated_at":"2026-03-30T22:51:07.213+02:00","length":538,"duration":538,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71128-c35eab1e-5201-50b4-8676-497cec3ce328.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71128-c35eab1e-5201-50b4-8676-497cec3ce328_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71128-c35eab1e-5201-50b4-8676-497cec3ce328.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71128-c35eab1e-5201-50b4-8676-497cec3ce328.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-71128-resilience-starts-with-a-map-community-led-osm-action-in-dhaka-s-climate-vulnerable-settlements","url":"https://api.media.ccc.de/public/events/c35eab1e-5201-50b4-8676-497cec3ce328","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"0da688fd-fe21-5d8d-a2de-067123705a13","title":"fAIrSwipe","subtitle":null,"slug":"sotm2025-67122-fairswipe","link":"https://2025.stateofthemap.org/sessions/3HRTVP/","description":"This project aims to combine AI‐generated building predictions from fAIr with crowdsourced validation and conflation workflows in MapSwipe, ultimately pushing high‐confidence conflated map data into OpenStreetMap (OSM).\nfAIr is an AI‐powered mapping assistant by HOT that helps users map smarter, faster, and more accurately.\nMapSwipe is a crowdsourcing app that lets volunteers validate or identify features (e.g. buildings) quickly on satellite imagery.\n\nObjectives\n- Automate the creation of MapSwipe projects from fAIr (with building predictions, TMS layers, etc.).\n- Validate AI‐predicted features via MapSwipe’s volunteer workflow via redundancy.\n- Conflate validated features with existing OSM data.\n- Upload conflated data back to OSM in a controlled, conflict‐aware manner.\n- Provide feedback to improve fAIr's AI models based on volunteer validation results and possibly task the tasking manager to manually map the parts that are difficult to validate via MapSwipe.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Omran NAJJAR"],"tags":["67122","2025","sotm2025","Software Development","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 2"],"view_count":4,"promoted":false,"date":"2025-10-04T06:00:00.000+02:00","release_date":"2026-04-01T00:00:00.000+02:00","updated_at":"2026-04-02T21:45:06.108+02:00","length":1827,"duration":1827,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/67122-0da688fd-fe21-5d8d-a2de-067123705a13.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/67122-0da688fd-fe21-5d8d-a2de-067123705a13_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/67122-0da688fd-fe21-5d8d-a2de-067123705a13.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/67122-0da688fd-fe21-5d8d-a2de-067123705a13.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-67122-fairswipe","url":"https://api.media.ccc.de/public/events/0da688fd-fe21-5d8d-a2de-067123705a13","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"3c965d34-1b66-5046-91f6-eb10cfbf3cbd","title":"Keeping alive OSMTracker","subtitle":null,"slug":"sotm2025-71023-keeping-alive-osmtracker","link":"https://2025.stateofthemap.org/sessions/9HN9SX/","description":"This is a story about the last 7 years of OSMTracker and how, from an academic space in a Costa Rican public university, we managed to keep alive the app with computer engineering undergrad students.  We want to share what we have learned: the challenges and contributions during this process, and how we plan to continue. \n\nOSMTracker is one of the oldies free software data capture tools in the OSM ecosystem.  Its simplicity, low technical requirements, easy customization, and ability to use the exported data in various applications make it a valuable resource for mappers. The app became part of the methodological resources used by the Laboratorio Experimental de Computación y Comunidades (LabComún) for development of university extension projects together with communities like Erizo Juan Santamaría Informal settlements and Alajuela en Cleta urban cycling collective. Naturally, the usage of OSMTracker in this context showed us improvements needed in the app, and consequently, we contributed with code that were incorporated in the tool. \n\nIn 2018, nguillaumin, the original developer of the app, transferred the maintenance OSMTracker to LabComún. Since then, LabComún has been managing the challenge of developing free software from a (global south) public university: scarcity of resources, bureaucratic difficulties, and how to align the process of developing software while offering a sustainable educational experience for students.\n\nThe ongoing focus of development of OSMTracker highlights the importance of engagement of undergrad computer science students in projects that are related to territories and people. This offers a real perspective on how their contributions to software could improve the quality of life. From a technical and academic perspective, the opportunity to be part of a bigger free software and open data community is unique.\n\nFinally, we want to open discussion on how to enhance the sustainability of OSMTracker, involving other actors in collaborations and keeping the app useful for thematic mapping projects.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Jaime Gutiérrez Alfaro","Diego Munguía Molina"],"tags":["71023","2025","sotm2025","User Experiences","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 1"],"view_count":20,"promoted":false,"date":"2025-10-03T08:30:00.000+02:00","release_date":"2026-03-21T00:00:00.000+01:00","updated_at":"2026-03-30T22:38:51.027+02:00","length":1799,"duration":1799,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71023-3c965d34-1b66-5046-91f6-eb10cfbf3cbd.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71023-3c965d34-1b66-5046-91f6-eb10cfbf3cbd_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71023-3c965d34-1b66-5046-91f6-eb10cfbf3cbd.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71023-3c965d34-1b66-5046-91f6-eb10cfbf3cbd.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-71023-keeping-alive-osmtracker","url":"https://api.media.ccc.de/public/events/3c965d34-1b66-5046-91f6-eb10cfbf3cbd","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"b51c714b-0846-5a01-a563-9249256d4355","title":"Journey to the Center of the Planet","subtitle":null,"slug":"sotm2025-71643-journey-to-the-center-of-the-planet","link":"https://2025.stateofthemap.org/sessions/CBMXL3/","description":"Whether you’re a simple mapper or a sophisticated data consumer, you depend on a lot of machinery at the heart of OpenStreetMap that you probably never think about. After many years mapping and tinkering on OSM-based frontend applications, Minh is venturing into the software core of OSM’s world. Join him as he recounts his travels, unearthing the many moving parts beneath the surface and piecing together how they fit into the larger constellation of OSM software. Get a glimpse of the future of this software stack as we invest in development resources and make progress on longstanding priorities – and how you can help.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Minh Nguyễn"],"tags":["71643","2025","sotm2025","Software Development","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 2"],"view_count":2,"promoted":false,"date":"2025-10-04T08:30:00.000+02:00","release_date":"2026-04-03T00:00:00.000+02:00","updated_at":"2026-04-03T18:15:04.516+02:00","length":1414,"duration":1414,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71643-b51c714b-0846-5a01-a563-9249256d4355.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71643-b51c714b-0846-5a01-a563-9249256d4355_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71643-b51c714b-0846-5a01-a563-9249256d4355.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71643-b51c714b-0846-5a01-a563-9249256d4355.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-71643-journey-to-the-center-of-the-planet","url":"https://api.media.ccc.de/public/events/b51c714b-0846-5a01-a563-9249256d4355","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]},{"guid":"c64bf755-f233-5a80-ace4-9439f7c62f26","title":"How to Complete Japan’s Building Mapping in One Year? Strategies for Integrating PLATEAU Data into OSM","subtitle":null,"slug":"sotm2025-71106-how-to-complete-japan-s-building-mapping-in-one-year-strategies-for-integrating-plateau-data-into-osm","link":"https://2025.stateofthemap.org/sessions/N89YSU/","description":"Since 2020, Japan’s Ministry of Land, Infrastructure, Transport and Tourism (MLIT) has led Project PLATEAU, releasing 3D city models in CityGML format. By 2025, over 236 municipalities have published building datasets compatible with the Open Database License (ODbL), suitable for integration into OpenStreetMap (OSM).\n\nSince 2022, OpenStreetMap Japan volunteers have imported PLATEAU’s Level of Detail 1 (LOD1) building data into OSM, completing imports for 13 cities as of May 2025. Despite these efforts, only about 62% of Japan’s estimated 38 million building polygons are mapped in OSM.\n\nThis presentation proposes a roadmap to complete Japan’s building mapping within one year, focusing on optimizing PLATEAU data imports, ensuring data consistency, and strengthening community collaboration.\n\n— Strategies for Integrating PLATEAU Data into OpenStreetMap\n\nSince 2020, Japan’s Ministry of Land, Infrastructure, Transport and Tourism (MLIT) has spearheaded Project PLATEAU, a national initiative to develop and openly release 3D city models in CityGML format. As of 2025, over 236 municipalities have published detailed building datasets, which are compatible with the Open Database License (ODbL) and suitable for integration into OpenStreetMap (OSM). These semantically rich and high-precision datasets hold significant potential for applications in urban planning, academic research, and civic technology.\n\nBuilding upon previous presentations at State of the Map conferences, this talk outlines the progress made since 2022 by volunteers from OpenStreetMap Japan in importing PLATEAU’s Level of Detail 1 (LOD1) building data into OSM. As of May 2025, imports have been completed for 13 cities. In addition to these imports, numerous volunteer mappers continue to enhance OSM’s building data through aerial imagery tracing and other methods.\n\nDespite these efforts, only approximately 62% of Japan’s estimated 38 million building polygons are currently mapped in OSM, leaving about 14 million polygons—38%—yet to be integrated. Addressing this gap necessitates a clear numerical target and a strategic approach.\n\nThis presentation sets forth an ambitious yet achievable goal: to complete the mapping of Japan’s building data within one year. We will provide a quantitative analysis of current progress and delineate a roadmap to achieve this objective. Key strategies include optimizing the importation of PLATEAU data, ensuring data consistency between PLATEAU and OSM, and strengthening collaboration within the mapping community.\n\nBy sharing these insights and methodologies, we aim to accelerate the integration of PLATEAU data into OSM and to serve as a model for other countries seeking to enhance their building mapping efforts.\n\nCreative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/","original_language":"eng","persons":["Taichi Furuhashi"],"tags":["71106","2025","sotm2025","Mapping","Mayon","sotm2025-eng","OSM","OpenStreetMap","Day 2"],"view_count":10,"promoted":false,"date":"2025-10-04T05:30:00.000+02:00","release_date":"2026-04-01T00:00:00.000+02:00","updated_at":"2026-04-03T13:45:08.826+02:00","length":1704,"duration":1704,"thumb_url":"https://static.media.ccc.de/media/events/sotm/2025/71106-c64bf755-f233-5a80-ace4-9439f7c62f26.jpg","poster_url":"https://static.media.ccc.de/media/events/sotm/2025/71106-c64bf755-f233-5a80-ace4-9439f7c62f26_preview.jpg","timeline_url":"https://static.media.ccc.de/media/events/sotm/2025/71106-c64bf755-f233-5a80-ace4-9439f7c62f26.timeline.jpg","thumbnails_url":"https://static.media.ccc.de/media/events/sotm/2025/71106-c64bf755-f233-5a80-ace4-9439f7c62f26.thumbnails.vtt","frontend_link":"https://media.ccc.de/v/sotm2025-71106-how-to-complete-japan-s-building-mapping-in-one-year-strategies-for-integrating-plateau-data-into-osm","url":"https://api.media.ccc.de/public/events/c64bf755-f233-5a80-ace4-9439f7c62f26","conference_title":"State of the Map 2025","conference_url":"https://api.media.ccc.de/public/conferences/sotm2025","related":[]}]}