Chaos Computer Club - archive feed

The research evaluates the accuracy and completeness of wind and solar energy infrastructure data in OpenStreetMap (OSM) for Belgium and Ireland, identifying common mapping errors and proposing techniques to enrich available data. By combining OSM data with CORINE Land Cover inventory, we study land use patterns around renewable energy infrastructures, facilitating environmental planning. We are witnessing the rise of a collective awareness of the importance of building a more environmentally sustainable future. Within this context, the relevance of renewable energy sources is widely acknowledged; however, to support the energy transition, the availability of reliable data about energy supply, infrastructures, and their environmental impacts is essential [1]. OpenStreetMap (OSM) emerges as a valuable data source to meet these requirements. In this work, we describe our research on evaluating the OSM database in a study of wind and solar energy infrastructures. As a case-study, we analyse two countries: Belgium and Ireland. Data within OSM is well-known to have a diverse level of completeness and granularity [2]. Considering OSM's vibrant mapping communities coupled with both (a) the environmental visibility of wind and solar energy infrastructures and (b) their relatively limited number compared to other built infrastructures, it is reasonable to assume that the majority of these installations are well mapped within OSM. OSM energy-related objects are mapped under the _“power”_ tag, with available key-value combinations to identify wind and solar sources. Wind turbines are commonly tagged as _“power=generator”_ and _“generator:source=wind”_, while solar farms are identified as _“power=plant”_ and _“plant:source=solar”_. By combining OSM data with the CORINE Land Cover (CLC) inventory we considers two research questions. Firstly, we seek to identify common mapping errors and tagging issues associated with wind and solar energy infrastructure representation within OSM. This involves examining geometries and tagging mistakes while evaluating the accuracy and completeness of these infrastructures. Secondly, we perform a geographical analysis to consider the distribution of infrastructures across various CLC land covers. We seek to detect patterns around land cover and renewable energy infrastructure. Our methodology is summarised as follows: OSM data, in PBF format, is downloaded from _GeoFabrik_. Initially here we just consider Ireland and Belgium due to local knowledge and their manageable data sizes. Analysis is performed using _Python_ and the _osmium_ library. Pending acceptance, source code will be made openly available on GitHub in documented Jupyter notebooks. We answer our research questions using three key steps: 1. Assess available wind and solar infrastructures in OSM for the correctness of OSM datatypes and potential geometric errors. 2. Differentiate between individual installations and larger "farms", to evaluate the quality and completeness of OSM data. 3. Investigate commonalities around land use surrounding renewable energy infrastructures using CLC. Initially, we extract all OSM objects with _“power”_ tag =_“plant”_ or _“generator”_, yielding a full listing of all available power sources. We filter solar and wind sources based on the _"generator:source"_ or _"plant:source"_ tags. The accuracy of the OSM datatypes and geometries is checked before moving to "farms" identification. To accurately differentiate single installations from farms, we analyze solar and wind sources separately due to their different geometric nature. We employ a point-in-polygon search with spatial indexes to assign nodes – of either wind or solar type – to intersecting areas of the same energy type. This process raises some geometric questions concerning the meaning of areas without nodes and nodes not linked to any area. We observed several issues: (a) not mapping individual panels within solar farms or defining turbines as circular areas, (b) nodes not associated with any area, (c) unlinked solar nodes representing private installations, (d) the absence of OSM areas or relations for turbines frequently indicated incomplete mapping. This final issue is significant: 91.4% of wind nodes in Belgium and around 87.4% in Ireland are not located within any mapped area. Considering the limited number of turbines included in OSM areas or relations, we employed spatial clustering on all turbines to identify wind farms and validate our findings by automatically comparing clusters with existing OSM areas and relations. DBSCAN (Density-based spatial clustering of applications with noise) is particularly suited for our purpose. One of the most important DBSCAN parameters is the maximum distance between two samples belonging to the same cluster which we set as 5 times the rotor diameter [3]. We assume a diameter value of 130 meters for all turbines [3] considering the frequent absence of t

Whether they are man-made or natural, disasters pose serious risks to communities all over the world. A comprehensive evaluation of gasoline station infrastructure is necessary to ensure public safety and reduce fire dangers. This research, which focuses on Harare, Zimbabwe, uses Geographic Information Systems (GIS) to examine building patterns and access building footprints, road networks, and public facilities using the OpenStreetMap Database. Fuel station mapping and dataset overlaying allow to define fire risk levels that are susceptible to fire hazards and identify danger zones. This study supports risk management, public safety, disaster preparedness, and mitigation initiatives in Harare, Zimbabwe. Natural or man-made disasters pose serious risks to communities all over the world and frequently have dire repercussions, including the loss of life, destruction of property, and disruption of social order [1]. Fire occurrences are particularly dangerous among these calamities, especially when they include infrastructure such as gasoline stations [2]. The potential for large-scale fire catastrophes underscores the need of ensuring the safety of gasoline station infrastructure, as evidenced by occurrences documented in Zimbabwe [3]. Petroleum derivatives, such as gasoline, diesel, kerosene, and LPG, have the potential to ignite fires when handled improperly [4]. It is clear that a comprehensive fire danger assessment is necessary, which highlights the need for proactive fire management planning [4]. Between 1993 and 2004, there were around 243 fire-related incidents at fuel service stations worldwide that were recorded. it's evident that these sites present significant risks Geospatial technology has become an invaluable instrument for disaster preparedness, response, and mitigation as a result of these issues [5]. By offering open data necessary for disaster response and mitigation, initiatives such as Humanitarian OpenStreetMap and the utilization of platforms similar to OpenStreetMap have made substantial progress in these efforts [6]. These systems ensure the availability of high-quality data for efficient mitigation measures and provide quick and open access to geospatial data, facilitating prompt disaster response activities [7]. The OpenStreetMap data consists of many datasets that use points, lines, polygons, and area attributes to represent real-world features. These databases include characteristics that are useful for study, mitigation, recovery, and preparedness for disasters. Through the use of Geographic Information Systems (GIS) techniques, mapping into and querying the OpenStreetMap Database, this project seeks to overcome the difficulties presented by fire dangers, namely in gasoline station infrastructure. In order to identify danger zones and potential vulnerabilities, the research specifically aims to perform a thorough examination of building footprints, conventional construction standards, and public spaces such as marketplaces, parks, schools, hospitals, houses of worship, and road networks in Harare, Zimbabwe. Additionally, it seeks to map gas stations, obtain exact locations, and extract pertinent data from OpenStreetMap and other sources by utilizing Geographic Information System (GIS) approaches. The study also aims to categorize areas at risk of fire danger according to a number of factors, such as proximity to fuel stations and the position of LPG filling stations according to Zimbabwean government rules and retail premises, as well as other factors identified through spatial analysis. Additionally, it seeks to offer practical suggestions and solutions for improving public safety and lessening the effects of fire disasters in Harare, Zimbabwe, offering insightful information for initiatives related to disaster preparation, mitigation, and recovery. to advance resilience and sustainable development in the area while also adding to the scientific understanding of the dangers of fire hazards related to gasoline stations. This study's methodology combines spatial analytic methodologies, data extraction from OpenStreetMap and other sources, and Geographic Information System (GIS) analysis. To fully comprehend the gaps that now exist, we will first start by gaining access to the data that is already available on the OpenStreetMap Database. The gasoline stations will then be mapped into the OpenStreetMap database, where pertinent data such as locations and infrastructure aspects will be recorded. This information will then be taken out of OpenStreetMap. The procedure involves extracting comprehensive information on the locations of buildings, roads, and public spaces including playgrounds, marketplaces, hospitals, and schools from the massive OpenStreetMap database. A thorough examination of the geographic distribution of filling stations, public buildings, and fire service stations will be done using a geographic information system. The objective of this research is to categor

In recent years, advancements in 3D city models have been instrumental in urban planning and public engagement. In Japan, over 200 cities have adopted open digital twin data in CityGML format as promoted by Project PLATEAU of the Ministry of Land, Infrastructure, Transport and Tourism. This initiative, detailed in Binyu et al.'s research and the 3D City Index report, involves collaboration with the global OpenStreetMap community. Utilizing open database license ODbL, the integration of digital twin data with OpenStreetMap has been explored since 2022, aiming to enhance the global adoption of 3D city models and showcase the benefits of collaborative urban development. # References Lei, B., Stouffs, R., & Biljecki, F. (2023). Assessing and benchmarking 3D city models. International Journal of Geographical Information Science, 37(4), 788–809. https://doi.org/10.1080/13658816.2022.2140808 Toshikazu Seto. (2023). Role of 3D City Model Data as Open Digital Commons: A Case Study of Openness in Japan's Digital Twin "Project PLATEAU", Academic Track, FOSS4G 2023 Prizren. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/PRQKRX/

OpenStreetMap (OSM) provides detailed street networks essential for analyzing active transportation (AT) infrastructure. However, the granularity and inconsistencies in OSM data pose challenges in modeling AT users' movements at the street level. This study proposes a novel methodology to generate axial networks for AT users using OSM data, simplifying the network while preserving topology. Applied to cities like Turin, Tel Aviv, and San Francisco, this approach effectively streamlined pedestrian and bicycle networks. The proposed methodology can enhance AT infrastructure, contributing to safer and more efficient urban mobility. Introduction OpenStreetMap (OSM) offers comprehensive street networks that span nearly every city worldwide. The networks contain essential details like road type and name, and individual streets are often represented through multiple concurrent segments. The segments include separate lanes for motor vehicles, dedicated bike paths, and pedestrian walkways. OSM's comprehensive dataset allows the creation of specialized networks customized for pedestrian and cyclist analysis, providing users with a powerful tool for understanding and improving active transportation (AT) infrastructure. These specialized networks play a pivotal role in monitoring and understanding walking and bicycling patterns, contributing to infrastructure enhancement aligned with cities' goals for fostering AT to combat traffic congestion, obesity, and air pollution (Nelson et al., 2021) Nevertheless, the granularity of these networks may pose challenges in modeling AT users' movements on a street level. For example, when assessing AT safety or suggesting a walkability index at the street level, the current data representativeness on OSM necessitates significant manipulation to do so. Furthermore, owing to OSM's open-editing model, the standards for mapping elements are not consistently defined, leading to contentions and variations in data quality (Haklay, 2010). Individuals often map elements based on personal needs and knowledge, introducing inconsistencies in the dataset. Consequently, in some locales, all designated lanes for various users are meticulously mapped, while in others, only a single lane representing the presence of a street is depicted. Additionally, there are instances where only lanes for motor vehicles are detailed, with scant attention paid to lanes catering to other road user groups. Previous research has endeavored to address this issue. For example, a study suggested a topology-preserving simplification of OSM network data for large-scale simulation in sumo (Meng et al., 2022). However, they primarily focus on less complex areas and prioritizing vehicular considerations when generating the new network over those of AT users. Aim In this study, we propose an innovative solution to generate an axial network specifically designed for monitoring and analyzing AT users, using exclusively OSM data. Our approach simplifies the network while preserving its topology. We apply this solution across diverse spatial contexts, from straightforward geographic regions to complex urban environments. Furthermore, we implement our methodology in several cities worldwide—Tel Aviv (Israel), Turin (Italy), and San Francisco (United State)—each characterized by unique urban structures and varying levels of economic development. Methodology The preliminary tasks use OSMnx (Boeing, 2017) to acquire OSM street network data, converting it into a graph while correcting topological errors. Then, the data is stored in a geodata table, including polyline geometry, names, and road types. Our algorithm filters out unsuitable roads, like motorways and trunk roads, and replaces roundabouts with their central points. The network is then ready for the multilane detection algorithm. Polylines identified in a multilane scenario are aggregated into a centerline and added to the Simplification OSM Data (SOD) network. Other polylines are added to the SOD network, retaining their original geometry. A multilane scenario is identified when polylines share the same street name, have similar angles, and are close together. Polylines are grouped by street name, and azimuth (0°-180°) narrows the angle range to ensure that parallel lines are considered parallel, regardless of orientation. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clusters similarly angled polylines using a 10° radius and a minimum of two samples. Outliers with significantly different angles are excluded. The remaining clusters apply a right-shifted buffer to each polyline. If two or more polylines in the same class overlap by at least 10% in the shifted buffer, they're classified as multilane, and the entire class is replaced with one or more centerlines. The core idea behind creating a new centerline is to identify its start and end vertices and then add intermediate vertices to preserve the overall shape of multilane

Lightning talks are short presentations (maximum 5 minutes) about a topic related to OpenStreetMap. ## Campus Guide: Enhancing campus navigation through accurate and updated OSM maps _by Victor Ademoyero_ ## Mapswipe for web _by Geoffrey Kateregga_ ## Mapping activities and impact in Uganda by Geo-YouthMappers _by Umar Katongole_ ## OSM and OpenAerialMap data for machine learning _by Remígio Chilaule_ ## Essentials for a geospatial entrepreneur _by Antidius Kawamala_ ## Perspective of women participation in OSM (brief insights of survey conducted in March 2023) _by Benedicta Ohene_ Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/ZZTZRB/

This presentation introduces how to develop your own style of OpenMapTiles for MapLibre GL from scratch with *your favorite editor*. - Introduce of OpenMapTiles schema. - Introduce of Charites to use your favorite editor. - Introduce of other tools to compile JSON format for MapLibre GL. This presentation describes with how to use command line tools to create MapLibre GL style. I recommend to use UNIX based operation system or Raspberry Pi. The editors supports VS Code, Emacs, or Vim. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/NHZVNW/

Our work aims to assess OSM road data quality with a focus on car driving. Current work-in-progress proposed indicators to assess attribute accuracy, with a focus on speed limits and methods for estimating the logical consistency of road data. Due to the crowdsourced nature of OpenStreetMap (OSM) and the lack of quality control during the contribution, the data quality issue has become a research focus [1]. Understanding and addressing these data quality issues can facilitate unlocking OSM's full potential for diverse applications. OSM data quality assessment methods can be divided into two broad categories: extrinsic and intrinsic. Extrinsic quality assessment methods compare OSM data with a reference dataset (e.g., authoritative data sources). This is where most initial research on OSM data quality started from [2-4]. Yet, a reference dataset may not always be available. On this ground, researchers called for attention to the intrinsic indicators of OSM data quality [5] and proposed intrinsic data quality measures based on the data, data history, and metadata [5-9]. It is crucial to acknowledge that the quality of OSM data is not a one-size-fits-all metric. Rather, it heavily depends on the purpose of the application domain, known as "fitness-for-use" [5,10]. The diverse application potentials introduce dynamic data requirements. Navigation is one of the primary application domains in which OSM plays a pivotal role. Among them, vehicular traffic constitutes a significant portion of road usage and has a substantial impact on urban mobility and infrastructure planning. Additionally, the complexities involved in automotive navigation and traffic systems require a higher level of data accuracy and reliability, making it a compelling starting point for investigating intrinsic road data quality. In the context of car driving, the attribute accuracy and logical consistency of road data are particularly important [11]. Attribute accuracy refers to the correctness and logical coherence of attributes associated with road features, such as speed limits, road classifications, and turn restrictions [12]. Logical consistency ensures that the road network data follows the correct topological rules, such as proper connectivity of different road classes. The attribute accuracy and logical consistency of OSM road data are essential for traffic planning and supporting navigation applications. To address the challenges of the evaluation of OSM data quality for navigation, when a reference dataset is unavailable, this research narrows its focus on assessing the attribute accuracy and logical consistency of OSM road data. To assess the attribute accuracy, we first identified important attributes related to car driving, such as road name, road class, surface, speed limit, capacity, height limit, total weight limit, and vehicle type. Then our method assesses the attribute completeness by looking into the tags related to the aforementioned attributes, and whether the relevant information is available. The attribute completeness provides us with a first overview of the attribute data. In the next step, a set of rules based on country-specific traffic laws is defined, and the relevant attributes are checked against these rules. So far, we have looked into the traffic law in Germany and defined rules for speed limits according to the road classes. Our proposed method looks into the “maxspeed” tag of each road segment, and verifies their value against the rule defined for the corresponding “highway” value. When a mismatch occurs, these road segments are identified and considered as inaccurate. The inaccuracy of the speed limit is calculated as the number of road segments with an invalid “maxspeed” value divided by the total road segments with “maxspeed”. At the current stage of our work-in-progress, we verified our method in the Heidelberg region, where the inaccuracy rate is very low. Regarding logical consistency, a set of rules is defined based on country-specific conventions. We acknowledge that different regions in the world have different road construction and road mapping conventions. So far, we have based on our case study in Germany, and defined rules for the values of the “highway” tag. These rules include: the value of the “highway” tag should be consistent along a path; a link road should be connected to its corresponding highway; connection of different classes of roads should be logically consistent (a way with a high level of importance in the road network should not connect directly to a way with a much lower level of importance). Our work aims to assess OSM road data quality with a focus on car driving. Current work-in-progress proposed indicators to assess attribute accuracy, with a focus on speed limits and methods for estimating the logical consistency of road data. In the next step, we plan to build more rules for speed limits, considering the geometry of the road and their neighbouring zone to infe

fAIr is an open AI-assisted mapping service developed by the Humanitarian OpenStreetMap Team (HOT), with the aim of improving and assisting mapping for humanitarian aid and disaster relief. The proposal illustrates the research undertaken to assess the performance of fAIR underlining ML model training, from the training datasets selection process, the choice of the metrics used to measure accuracy, and finally the analysis of the results obtained testing for different metrics. The research falls within the broader spectrum of research on understanding the fine tuning process for geographic domain adaptation in image analysis validation, particularly for building footprints detection. **Introduction** Building footprints features are useful in a wide range of applications such as disaster assessment, urban planning, and environmental monitoring (You et al., 2018; Owusu et al., 2021; Yang, Matsushita, & Zhang, 2023), and their identification has been gaining increasing interest and attention from the ML research for Earth Observation (Hoeser, Bachofer, & Kuenzer, 2020). Particularly in the disaster response context, accurate and prompt availability of such information is crucial (Boccardo & Giulio Tonolo, 2015; Deng, 2022; Sun et al., 2022). fAIr, developed by the Humanitarian OpenStreetMap Team (HOT), a fully open AI-assisted mapping service to generate semi-automated building footprints features, addresses this need (fAIr website, 2022). fAIr stands for “Free and open source AI that is resilient for local contexts, and represents the Responsibility of HOT for local communities and humanitarian mapping", reflecting the objective of HOT to improve and assist mapping for humanitarian aid and disaster relief. Useful open-source data sets of AI-generated building footprints exist (e.g. Microsoft’s global buildings dataset, available through Rapid, and Google’s Open Buildings for Africa and the Global South at large), however, the Machine Learning (ML) models are not currently open-sourced. fAIr, on the other hand, addresses the lack AI models openness by being a fully open source project (HOT Tech Blog, 2022). While in OSM building footprints mapping is currently supported in most countries through Rapid, "users should take care to ensure adjustments and corrections are made as needed" (Rapid - OSM wiki, 2024); fAIr goes over this issue by reintroducing _the human in the loop_. In fact, at its current state, fAIr allows OSM mappers to create their own local training dataset, train/fine-tune a pre-trained Eff-UNet model, and then map into OSM with the assistance of their own local model. In its initial release, the performance of the model following training was not assessed, thus the objective of this research is to address this gap. This proposal describes the research developed in recent months to assess how the ML fine-tuning process performs, investigating the currently used accuracy metric, and comparing against different sets of evaluation metrics. The final aim of this research is to advise on the optimal metric for this building footprints segmentation task. The research falls within the broader spectrum of research on understanding the fine-tuning process for geographic domain adaptation in image analysis validation (Rainio, Teuho, & Klén, 2024; Maier-Hein et al., 2024). **Data and Methodology** fAIr is a software service that performs semantic segmentation to detect building footprints from openly available local satellite and UAV imagery at high resolution (cm) (OpenAerialMap website, 2017). In computer vision (CV), semantic segmentation is the task of segmenting an image into semantic meaningful classes, which is performed with convolutional neural networks (CNNs) architectures (Hoeser & Kuenzer, 2020). The deep learning CNN model used in fAIr is called RAMP (Replicable AI for MicroPlanning), and its architecture originates from an Eff-UNet model (RAMP model card, 2020; Baheti, Innani, Gajre, & Talbar, 2020). With the aim to analyse the current validation accuracy performance and compare against other metrics, an initial literature review on fine-tuning processes for geographic domain adaptation in image analysis validation was performed, which led to outlining a list of candidates for validation metrics (Reinke et al., 2024; Maier-Hein et al., 2024). In parallel, manual labelling on selected areas of interest (AoI) was carried out on a data set of sixteen urban regions, chosen as the most representative of different grades of urbanity, density, regional characteristics, roof cover types, etc. The pre-processing of the AoI images was performed for each urban region through fAIr-dev website (fAIr website, 2022), and produced 256x256 georeferenced tiles for both the original RGB images and labelled masks (see example in Figure 3). Then, the ML training was run on all sixteen training datasets using an Nvidia Tesla T4 GPU, for different batch sizes, epochs, and zo

Starting as a small research project at the University of Bonn and later the University of Heidelberg at around 2008, Openrouteservice has been steadily growing since. This ensures that now almost 120,000 users get free and equal access to basic mobility solutions every day. With the release of version 8, this conference talk will give you an exclusive behind-the-scenes look at almost a decade of OpenStreetMap-driven open-source software development and delivery. Our successes and challenges, and how we stayed true to our values, providing free and open-source software as a non-profit organization. With over ten years of history and over 120,000 users, openrouteservice has established itself as a cornerstone in the area of open-source and OSM-based routing engines. Driven by an increasing availability of road network data through the OpenStreetMap project, openrouteservice could steadily grow and evolve and find its unique spot as a good addition among other routing engines such as OSRM and Valhalla. The current portfolio of freely available services now ranges from standard routing, isochrones and vehicle optimization to custom routing algorithms designed especially for use cases such as relief operations in flooded or earthquake shaken areas. Openrouteservice was created as part of a research project at the University of Bonn around 2008 and was moved to the University of Heidelberg in the subsequent years. It has since been steadily developed, maintained, and supported. In 2017, we launched our public and complimentary API, offering fair quotas for a diverse range of our mobility tools, including routing, isochrones, matrix, vehicle optimization, geocoding, and numerous other features. This was so well received that openrouteservice partnered up with the German “Federal Agency for Cartography and Geodesy” in 2018 to provide routing services to the German Federal Government. In early 2019 the project was transferred to the Heidelberg Institute for Geoinformation Technology (HeiGIT), a non-profit and non-governmental research organization associated with the University of Heidelberg. The combination of non-profit, non-governmental, research, and free and open-source software allows us to make decisions based on social, ethical and environmental reasons without being influenced by commercial interests. The resulting stability and continued support already motivated over 600 other prominent non-profit organizations to install and use openrouteservice and build on top of our service APIs to run their missions. These missions encompass scenarios such as disaster relief efforts, supply chain management, last-mile logistics for health items, accessibility analysis to ensure equal access to health facilities, or planning the support areas for fire brigades. With our latest release, version 8, we would like to take this opportunity to provide the OSM community, which we so heavily build on, with an insight into nearly a decade of OSM-driven open-source software development and delivery. What it takes to run and provide a global public API, and how we are navigating the current professionalisation push for open-source software to shift from formerly hacky self-hosted solutions to data centre ready deployments. We will also share details on how we deal and operate with the ever lasting questions about osm data quality, accuracy and explainability of our routing results. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/XUJWAM/

Lightning talks are short presentations (maximum 5 minutes) about a topic related to OpenStreetMap. ## EcoMappers activities and growth in Rwanda _by Liliane Nishimirwe_ ## Hot CWG mentorship _by Benedicta Ohene_ ## YouthMappers activities and growth in Sri Lanka (2021-2024) _by Sajeevini Sivajothy_ ## #DEI in OSM - Ladies in Maps Zimbabwe _by Letwin Pondo_ ## Microgrants program in Latin America _by Maya Lovo_ ## My journey in OSM: YouthMappers Tanzania _by Iman Seleman_ Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/SYRTRW/

This report outlines a project to improve the workflow for integrating external open datasets into OpenStreetMap (OSM), ensuring compliance with import guidelines. The process includes tools for conflating data with OSM, supported by roles and tasks specific to this workflow. The AllThePlaces project supports this by extracting and mapping open (government) data to the OSM schema. A new tool, "DiffedPlaces", is proposed to automate diffs, perform data matching with an improved machine learning algorithm, and host the results. In addition, the existing OSM Conflator tool set, enhanced with a web-based data validation application, will be connected to DiffedPlaces. This is a work-in-progress report on an applied university research project to improve the workflow for integrating external "third party" datasets into OSM to complete OSM while adhering to the the import guidelines. First, the workflow for conflating (merging, fusing) external data into OSM is outlined with the main roles and tasks involved. Then some existing and missing software tools and challenges are presented. At the beginning there's data compliant to the ODbL license - especially selected open government data (OGD) - in raw format, or open data already mapped to the OSM schema from the existing AllThePlaces (ATP) project. ATP is a set of spiders and scrapers for extracting external open data. ATP implements periodic extraction, schema mapping, and hosting. A missing piece is the planned open source tool with the working title "DiffedPlaces", which will periodically process "diffs" from ATP and external data, i.e. OSM elements to be created, updated or deleted. DiffedPlaces will provide matching (incl. deduplication, record linkage, named entity resolution) and "diffing" external data to OSM as well as hosting. Another existing tool set is the OSM Conflator web application originally implemented by Ilya Zverev. It implements extraction and schema mapping (like ATP) as well as diffing, validating and hosting. Part of OSM Conflator is the cf_audit GUI web application, which presents the diffed data to be validated by the mappers. It already gives an idea of how diffing reduces tedious manual validation. Only after several validations and communication with the community, the data is finally updated to OSM. This tool will be extended to replace Overpass and it's limited matching capabilities with a connection to DiffedPlaces. This will allow OSM Conflator to focus on validation and hosting, while the matching and diffing would be done in one place, in DiffedPlaces, where other OSM tools and data editors can also connect. One of the challenges is data matching, which is being addressed with an extended random forest based algorithm from Piech et al. 2020. Another challenge is the management of the OSM elements to be deleted. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/N7TGMA/

If you have ever wondered what OSMF and OSMF board are doing and why they exist it is a good presentation for you. Will include a brief overview (as promised in the title) of what they are doing, reason for their existence and why you may want to care about them. And why you may want join the board or one of working groups. Presented by OSMF board member. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/8XCQJB/

The Strava Global Heatmap visualizes aggregated public GPS traces, guiding athletes worldwide in planning their routes by highlighting popular paths. This presentation demonstrates how the Heatmap, in conjunction with our routing engine, can be utilized to identify and fill in missing ways in OpenStreetMap (OSM). We will discuss an algorithmic strategy for creating generated pathways and introduce a prototype of an interface that facilitates the easy integration of these pathways into OSM. We will showcase how Strava's Heatmap can promote OSM's goal of developing a more comprehensive global map, highlighting a mutually beneficial relationship between the two platforms. At Strava, we encourage and support over one hundred million athletes to be active. Athletes memorialize their physical activity via activity uploads that they can share with friends around the world. The vast majority of activities uploaded to Strava include time-series GPS traces. This wealth of information underpins our geospatial products, such as advanced routing capabilities and the visually engaging Global Heatmap. Through Strava Metro, we extend this aggregated and de-identified data at no cost to entities like governments, urban planners, and NGOs, aiding in the creation of more efficient human-powered transportation infrastructures. The backbone of Strava's routing algorithm lies in attributing popularity to roads and trails in our edge graph. We align raw GPS lines from Strava activities to these edges, enabling a popularity-driven routing experience that highlights preferred routes within the collection of OSM ways. With over a billion uploads, Strava athletes cover over 55% of the ways in OpenStreetMap with human-powered activity. In contrast, the Global Heatmap presents an unaligned aggregation of GPS lines for over one billion public activities, offering an intuitive visual representation to aid athletes in exploring their surroundings. It's this dataset that Strava Metro partners leverage to extract insights on the safety, directness, and overall appeal of routes in cities, parks, and recreation areas. We found that Metro partners will often overlay the Heatmap onto physical maps to identify and fill gaps in local pedestrian and cycling infrastructures. In response, we built a prototype of a tool that automates the identification and labeling of unaligned geometries, allowing our partners to streamline the process of integrating these new geometries into OSM. This innovative approach utilizes GPS data points unattributable to existing OSM edges to generate potential new pathways, enabling mappers to enrich the map with previously uncharted ways. Leveraging Strava's global scale, we have batch-processed a collection of these potential pathways worldwide. This data can help fill in unmapped areas and highlight regions needing more mapping attention. It can also highlight commonly used unauthorized paths and potential areas for infrastructure development. This presentation will delve into the algorithms that facilitate the generation of these new edges and explain how mappers can harness this data to enrich the OSM database, fostering a more comprehensive map for human-powered transport. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/HVVYM7/

The botanical collection Arboretum and Palmetum Leon Morales Soto houses 412 species from 64 botanical families, totaling 4892 individuals. The mapping carried out by the SAGEMA chapter of YouthMappers allowed sharing valuable information about Colombian flora with the university community, facilitating its conservation and recognition through the integration of collection data into OSM. The project aims to promote the conservation of threatened tropical ecosystems through open mapping. It involved the participation of 12 students and a training strategy through open workshops to replicate the project in other Latin American regions. The Universidad Nacional de Colombia is one of the most important ecological nuclei and connection points in Medellín and the Aburrá Valley. In its approximately 37 hectares, the university harbors a high diversity of fauna, especially birds, insects, small mammals, and reptiles, which find shelter, food, nesting areas, and a passage point in their biological corridor in the tree cover. Therefore, the conservation of this space is of special importance to the city. The collection stands out for housing various species representative of different Colombian tropical ecosystems, such as the dry forests of the Cauca and Magdalena canyons, the humid forests of Chocó, and the flooded forests of the Amazon, among others, offering students and visitors the opportunity to immerse themselves in the biodiversity of the extraordinary Colombian tropical forests and ensuring the maintenance of native species. The "Open Data, Vibrant Ecosystems" project aims to bring the university community closer to the care and appreciation of the flora and fauna present on the Universidad Nacional de Colombia, Medellín campus, through the recognition and appropriation of the living collection Arboretum and Palmetum Leon Morales Soto. It is based on open and collaborative mapping to encourage students to interact with the different species of palms, trees, shrubs, and herbaceous plants that the collection houses, and aims to go beyond visualization and consultation by inviting them to be active participants in the collection's tree inventory data collection, creation, use, and download through open data and the OpenStreetMap platform. As a result of the first phase of mapping the project, the collection's inventory was imported into OpenStreetMap, adding around 3000 new data points to the map. The project is developed in 3 phases: Training, Data validation in office and field, Data import to OSM. For the first phase, a work route was established for data import. First, information gaps and technical knowledge needed for the import were identified. Based on this analysis, collaboration was sought with volunteers from the OSM Colombia and OSM Latin America communities who had experience in tree mapping in OSM. A schedule of five training sessions was established for project members to acquire the knowledge and technical skills necessary for mapping the collection. As part of the project's dissemination and replicability strategy, it was decided to open the talks to the general public and especially invite the OSM Colombia and YouthMappers Latin America community to the training cycle. Five (5) training sessions were held in February and March 2024. The attendance range per talk was between 14 and 30 people, including students, educators, researchers, and institutions from 5 countries in Latin America (Colombia, Brazil, Mexico, Peru, Nicaragua, Chile). The training process covered topics such as data cleaning, mapping project documentation strategies, data import models with QGIS, urban trees and green areas in the OSM ecosystem, and open data licensing. To ensure the project's replicability, a YouTube channel was created with recordings of all the training sessions. A wiki was created documenting the data cleaning process, selected tags, the validation phase, and the import model. For fieldwork preparation, the QGIS program and the QField application were used. Through QGIS, tree data obtained from the university's Environmental Management System were imported, and then each campus of the Universidad Nacional de Colombia - Medellín Campus was divided into quadrants/polygons, with the aim that each member verified the tree data/points in the assigned quadrant. Field data verification was done through the QField application. The qfiel form identifies the mapper with their OSM username, allows them to add new individuals, map dead trees, and delete absent individuals. Information on species, growth habit, condition, and a photo was collected during the recognition. Python code was used to complement the information collected in the field, using the GBIF open repository API to add information about the botanical family for each of the mapped species. After socializing the import in the OSM Colombia community, the data import was carried out by quadrants under the user of each of the

Importing a government-release open data dataset is an important data source for OpenStreetMap. I will talk about the experience of the OpenStreetMap Taiwan community importing tasks. I will describe the methods and the challenges we face during the whole import process. We have already dealt with 9 counties and cities' address datasets, and looking forward to dealing with more datasets released by other local government agencies in the future. Importing a government-release open data dataset is an important data source for OpenStreetMap. Most of the time you have to judge if the data quality is good enough to do so, and once you decide to import, you have to convert the dataset's field into the correspondent tags of OpenStreetMap. I will talk about the experience of the OpenStreetMap Taiwan community importing the open data datasets of addresses from the Taiwan government. I will describe the methods and the challenges we face during the whole import process. We have already dealt with 9 counties and cities' address datasets, and looking forward to dealing with more datasets released by other local government agencies in the future. I will report 6 cities and 3 counties importing address tasks. And talk about the experience of previous import tasks, like importing ETC tolls, public bicycle stations, bus stops, etc. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/ZVVQTA/

Thousands of Linked [Open] Data sources and knowledge graphs allow to access an enormous amount of structured interconnected data with built in interoperability. This talk explains the basics of Linked Data and offers an overview about the potential its combination with OSM has and the most popular methods available for linking, extracting, combining and querying data from OSM and Linked Data sources. Linked Data defines data structured with design principles that make it easily interoperable. Thousands of Linked [Open] Data sources and knowledge graphs around the world allow to access an enormous amount of structured interconnected data. The combination of this data with OpenStreetMap has considerable potential for valuable insights, quality control and new methods of interaction. This talk explores the basics of Linked Data and offers an overview of the state of the art in methods for mapping links to Linked Data sources and for extracting, combining and querying their data. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/A3Y3BC/

The OSM Foundation is kept running with the help of many volunteers. They look after our servers, the data, the community, the conference, membership and much more. In this session, some of our working groups will introduce themselves. Hear about what they do and how you can help. There will be also plenty of time to ask questions. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/HS8SVU/

This presentation will introduce a download service of OpenStreetMap GIS layers in English, but also in French for French-speaking areas. In addition to the translation aspect, this service is intended as a proof of concept for an approach that is interoperable, flexible and replicable: it uses open source software components, some of which are supported by OSGeo, and interoperable WMS and WFS protocols of the OGC (Open Geospatial Consortium), while providing detailed OSM data for the countries in question. While English dominates the OSM ecosystem and remains the reference language for the project, several initiatives allow non-English speakers to participate in and benefit from the project: a multilingual forum, translation of the wiki and certain self-learning platforms, translated user interfaces for applications and editors, including OSM label presets. But regardless of the technology or service used, the raw OSM data, once downloaded, is still exclusively in English, and any searching or filtering of OSM data in free GIS software such as QGIS can only be done in this language. This feature can slow down the learning process for non-English speaking OSM contributors, but more importantly, it remains a barrier to adoption by audiences outside the OSM community: for example, public services accustomed to creating/distributing/using data in the official language, or one of the official languages, of their country. This presentation will introduce a download service of OpenStreetMap GIS layers in English, but also in French for French-speaking areas, created on a voluntary basis by the association Les Libres Géographes (The Free Geographers). In addition to the translation aspect, this service is intended as a proof of concept for an approach that is interoperable, flexible and replicable, while providing detailed OSM data for the countries in question. It allows users to download all OSM data and all its labels in various GIS formats, in the form of 16 thematic or generic layers that are updated daily. It uses open source software components (PostGIS, Imposm, GeoServer, GeoNetwork, MapStore), some of which are supported by OSGeo, and interoperable WMS and WFS protocols from the Open Geospatial Consortium (OGC), making it easy to replicate the service. The presentation will be an opportunity to demonstrate these different aspects, get feedback or suggestions from the audience, and possibly discuss extending the translation to other existing OSM data download services. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/MJKAC8/

The privacy that OpenStreetMap gives to users in its software and programming interface has lagged behind the requirements of the European Union’s General Data Protection Regulation in some respects. We are now taking steps to remedy this and the changes that mappers and API clients will see are discussed here. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/EBMVWS/

This talk aims to share the efforts of OSM contributors to improve national datasets based on experiences in the Democratic Republic of the Congo. From data analyses to the setup of crowdmapping projects through welcoming new mappers to join and writing documentation. This talk is going to introduce a number of existing tools that proved to be useful in the DRC community. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/ZHME3F/

MapLibre Tiles (MLT) is a new vector tiles format which offers a significant tile size reduction and accelerated decoding performance compared to the de-facto standard Mapbox Vector Tiles (MVT). MLT also adds support for missing features like nested properties, linear referencing and M-values. Our evaluation against MVT on a OpenMapTiles schema based OSM tileset shows a reduction in tile size of nearly up to 80% with even faster decoding times. This talk explains how MLT can be used in combination with OSM data and the advantages it offers. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/T3CXBD/

[Cloud-native approaches and formats](https://cloudnativegeo.org/) are increasingly becoming the defaults for geospatial data analysis, visualisation and distribution. Standards like STAC and formats like GeoParquet, FlatGeobuf and PMTiles are being adopted to meet high volume and performance needs. OpenStreetMap can take advantage of these new approaches to increase adoption, interoperability and solve analysis and visualisation problems that were previously complex. This talk will discuss a few ways to bring cloud-native formats to OSM use-cases and present lessons on building new applications that take advantage of these improvements. Particularly, we will discuss how to use cloud-native approaches to improve OSM validation and change visualization efforts. OpenStreetMap data is core to projects used by millions of users worldwide. With the increase in volume of data, developers are turning to cloud-native approaches to efficiently manage geospatial operations and visualizations. The primary OSM data distribution are currently Shapefiles, GeoJSONs, Raster and Vector tiles, and OSM specific formats like osm and osc. What are the possibilities of other formats like GeoParquet, FlatGeobuf and GeoArrow? How can these formats improve existing workflows in OSM and allow users new ways to work with OSM data? How can we enable interoperability with other datasets and tool ecosystems? These are some of the questions we will address in this talk. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/BXGLWA/

Historically, the open mapping movement’s disaster focus has been response, but evolving local capacity, insight, technology and partnerships mean new anticipatory action and disaster preparedness open mapping methodologies enable a transition from reactive to proactive approaches to disaster management. This talk explores key findings on the transformative role of open mapping in this topic, demonstrated by three HOT collaborations; Anticipatory Response Program in Zimbabwe, Flood Tracking Project in Liberia, and Mapping for Anticipatory Action in Timor-Leste, plus analysis of post-disaster data demand from NGO and government responders. The talk will also surface insights on how OSM communities can increase their own disaster resilience and preparedness through mapping. Building on the foundations laid by the Missing Maps project, HOT is developing partnerships, data models and mapping campaigns that augment remote mapping efforts of humanitarian mappers (largely building / road network basemaps) through work with local communities, government and other actors creating data and analysis at a community level to conduct proactive disaster management. Anticipatory Response Program, Zimbabwe In Zimbabwe, a country impacted by disasters such as floods, cyclones, and droughts, the Anticipatory Response Program (ARP) explored anticipatory response through: - Identifying gaps in anticipatory planning, preparedness, and response that could be addressed with open geospatial data and tools. - Understanding the limitations in data tools and infrastructure supporting anticipatory action. - Stimulating opportunities for joint project collaboration between community organizations, disaster agencies, and the Open Mapping hub - East and Southern Africa. - Strengthening participation and ownership. - Developing data models through stakeholder identification of information relevant to the enhancement of disaster response Data collection involved mapping relevant features to inform better response capability by Caritas Zimbabwe and other stakeholders in Muzarabani district. This was conducted via remote and field mapping using open mapping tools and updating OpenStreetMap and Mapillary in 14 wards. Features included buildings, roads, and relevant points of interest (POIs) such as health facilities, shops, markets, schools, water sources, flood-prone road sections, religious centers, and public toilets. Data analyses evaluated suitability of POIs as evacuation centers and cash voucher assistance points and evaluated proximity to communities, thus visualizing community vulnerability to disasters and contributing to local contingency planning. Specifically, this information served as input to hazard maps and Multi-Hazard Contingency Plans (MHCP) for the pilot wards, ensuring authorities are prepared for disasters, and response efforts are supported by high-quality relevant geospatial data. Tracking Flooding in Rural Liberia Coastal settlements of Liberia - 565kms of mainly lowlands, hosting mangroves, swamps and nine river estuaries - face climate risks such as flooding, rising sea levels, and coastal erosion. Coastal cities experience annual average precipitation of >500mm, causing floods and sea erosion that disrupt livelihoods and damage properties and infrastructure. HOT’S Open Mapping Hub - West and Northern Africa, iLab Liberia and OSM Liberia collaborated to address urgent needs of the Commonwealth District in Grand Cape Mount County. An open, participatory mapping approach was used with key stakeholders to accomplish: - Mapping stakeholders and climate change-related interventions - Filling gaps in existing datasets, plus visualizations and decision-making tools for government and humanitarian actors. - Increasing climate resilience awareness through focus groups, radio programs, talk shows, town hall meetings, and resources for use by local authorities, youth, and women’s groups. - Mapping building footprints, road networks, waterways, and land use and collecting field data on buildings, education, health, commerce, infrastructure, waterways, and other amenities to provide detailed information on the vulnerability, exposure, and flood hazards for more robust data analysis. Data products were developed based on requirements and use cases from the National Disaster Management Agency, Liberia National Police, Ministry of Health, Environmental Protection Agency, Liberia Water and Sewer Corporation, Liberia Land Authority, Liberia National Red Cross Society, Ministry of Internal Affairs, Liberia Institute for Statistics and Geo-Information Services, Ministry of Public Works, Liberian Hydrological Services, Liberia Geographical Society, and others. Access to reliable data analysis hinders stakeholder’s disaster mitigation, preparedness, response, and recovery initiatives. These actors can now develop resilience programs to support the people in the district and adopt tools and workflows for other affected counties across

Map Kibera arose from a desire to expand OSM beyond the confines of Europe and North America. In 2009, it pushed the boundaries of what then-new technologies could do. What have the mappers learned over the years? This talk will welcome you to Nairobi and through the ups and downs of mapping in Kenya - from the history of mapping in 20th Kenya, through Map Kibera’s start, into slums and rural parts of Kenya, and finally to current-day Kibera, where mappers are mapping street lights, waste disposal, schools, and more. How has Map Kibera and OSM had a community impact even as drones, satellite technology and AI are revolutionizing mapping? What has changed, and what has remained the same? We will discuss the global impact of Map Kibera, on community-based mapping in OSM and on the general application of technology in developing countries. This talk will include a discussion of mapping in Kenya dating back to the colonial era, the establishment of Kibera as a region of Nairobi, and its growth into a massive informal settlement. Kibera has been viewed as a place to develop by the Kenya government, International aid agencies, charities, and missionaries. It was a flashpoint of the post-election violence of 2007/8. Map Kibera’s Kenyan leaders will discuss the most recent mapping and local impacts made by the use of OSM. Mapping of street lights in Kibera led to new and more street lights installed in Kibera. Mapping of waste management in Mukuru led to the placement of dumping waste bins. Data on schools has led to a pilot project to install solar panels on selected schools. None of these impacts have been easy, but we will share lessons learned about OSM, open data and communities. Finally, we conclude with a discussion of emerging and ever-changing technology, and the fate of the techno-optimism of the early 21st century. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/B3EH7D/

Improving the map in OpenStreetMap involves data availability and reliability. Through the operations in which they are involved, organizations have a vast amount of data at their disposal, much of which is confidential and goes against the spirit of Open Data, which is all about sharing. This session will show how the OSMer in Residence program has fostered this paradigm shift through collaboration between HOT's Western and Northern Africa Hub and Médeçins Sans Frontières The shared vision of creating a detailed, accessible, and community-driven map of the world continues to drive the open mapping movement. Increasingly, organizations across diverse sectors are recognizing the value of open data and its potential impact on societal development. Several organizations have made a commitment to open up their data and actively contribute to OpenStreetMap (OSM). However, as OSM is an open-source community project, decisions and action are guided by community consensus. For any organization seeking to integrate its data into this platform, obtaining local community support is essential. The OSMer in Residence program serves as a crucial link between these organizations and the dynamic OSM community in Africa. Based on the concept of the Wikipedian in Residence, the OSMer in Residence program is designed to embed an OSM subject matter expert in a host organization or institution to maximize the value for the host organization in terms of their ability to leverage OpenStreetMap as a database that supports improved humanitarian and development outcomes in accordance with OSM community norms. In its pilot phase, Open Mapping - West and Northern Africa Hub(WNAH) collaboratedwith Médecins Sans Frontières/Doctors Without Borders (MSF), a humanitarian organization that provides emergency relief to some of the most vulnerable and excluded communities around the world, with data and maps being of high importance to supporting their operations. This pilot phase has led to workflow proposals facilitating the integration of an organization's data into OpenStreetMap by involving the local community where the data was collected. Data confidentiality and organized editing in OpenStreetMap will be addressed during this session. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/MRBEFX/

Lightning talks are short presentations (maximum 5 minutes) about a topic related to OpenStreetMap. ## Discover OSM with free GIS tools in Africa geoportal _by Esri Eastern Africa_ ## Disaster hackathon 2.0 in Bangladesh _by Ibtehal_ ## Impact of anticipatory mapping in disaster preparedness _by Jacques Niyigena_ ## Geospatial conference Tanzania (GIS day) November 15-16 2024) _by Kawamala Antidius_ ## Discover the UN maps learning hub! _by Séverin Ménard_ Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/87HBBY/

Examining the case of OSM Kenya, we showcase and exhibit how initiatives have empowered local communities through open mapping, fostering collaborations, and skill development. We have catalyzed sustainable impact driving positive change across the country and around the world through empowering over 200+ young people who are members of our community. Gaining insights and strategies for enhancing community engagement and capacity within the OpenStreetMap ecosystem in Kenya and beyond borders. The presentation will be centered around Community Capacity Building as a model for building sustainable communities for the global south, Sharing tricks and skills that we have adapted over five years, we desire to showcase our growth as a community through the contribution of open data. During the presentation, we will recognize the community for working together to achieve such a milestone as we seek to enlighten other communities toward achieving greater goals. Creative Commons Attribution 3.0 Unported https://creativecommons.org/licenses/by/3.0/ about this event: https://2024.stateofthemap.org/sessions/99HWEX/