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Schott, M., Zell, A., Lautenbach, S., Demir, B., & Zipf, A. (2022). Returning the favor - Leveraging quality insights of OpenStreetMap- based land-use/land-cover multi-label modeling to the community. In: Minghini, M., Liu, P., Li, H., Grinberger, A.Y., & Juhász, L. (Eds.). Proceedings of the Academic Track at State of the Map 2022, Florence, Italy, 19-21 August 2022. Available at https://zenodo.org/communities/sotm-22

Dsouza, A., Schott, M., & Lautenbach, S. (2022). Comparative Integration Potential Analyses of OSM and Wikidata – the Case Study of Railway Stations. In: Minghini, M., Liu, P., Li, H., Grinberger, A.Y., & Juhász, L. (Eds.). Proceedings of the Academic Track at State of the Map 2022, Florence, Italy, 19-21 August 2022. Available at https://zenodo.org/communities/sotm-22&nbsp

Herfort, B., Lautenbach, S., Porto de Albuquerque, J., Anderson, J., & Zipf, A. (2022). Inequalities in the completeness of OpenStreetMap buildings in urban centers. In: Minghini, M., Liu, P., Li, H., Grinberger, A.Y., & Juhász, L. (Eds.). Proceedings of the Academic Track at State of the Map 2022, Florence, Italy, 19-21 August 2022. Available at https://zenodo.org/communities/sotm-22

Additional file 2: Fig. S3. Accessibility workflow: raster- and network-based methods. Fig. S4. Accessibility to health facility, raster- vs. network-based method comparison A. Under six hours walking time to access healthcare estimations between raster- and network-based methods and B. mapped infrastructures. Fig. S5 Accessibility to health facilities in urban context, raster- vs. network-based method comparison A. population estimation B. extent of under 6 walking hours access to health facilities. Table S1. Comparison of mapped health facilities in OSM with field observation: facilities inventory, localisation comparison and damages inventory.

Additional file 3: Performance and computational environment.

Additional file 1: Data availability and completeness. Fig. S1. Indicator comparing population building densities from Ohsome Quality Analyst to evaluate the completeness of OSM data. Fig. S2. Comparison of mapped health facilities in OSM with field observation: facilities inventory, localisation comparison and damages inventory.

Routes with a high share of greenery are attractive for cyclist and pedestrians. We analyze how strongly such green routes differ from the respective fast routes using the openrouteservice. Greenness of streets was estimated based on OpenStreetMap data in combination with Sentinel-II imagery, 3d laser scan data and administrative information on trees on public ground. We assess the effect both at the level of the individual route and at the urban level for two German cities: Dresden and Heidelberg. For individual routes, we study how strongly green routes differ from the respective fast routes. In addition, we identify parts of the road network which represent important green corridors as well as unattractive parts which can or cannot be avoided at the cost of reasonable detours. In both cities, our results show the importance of urban green spaces for the provision of attractive green routes and provide new insights for urban planning by identifying unvegetated bottlenecks in the street network for which no green alternatives exist at this point., LIPIcs, Vol. 208, 11th International Conference on Geographic Information Science (GIScience 2021) - Part II, pages 3:1-3:15

Die Art und Weise, wie Land genutzt wird hat entscheidenden Einfluss darauf, welche Güter und Dienstleistungen aus der Natur dem Menschen zur Verfügung stehen. Landwirtschaftliche Produktion nimmt weltweit große Flächen in Anspruch, um die wachsende Weltbevölkerung mit Nahrungsmitteln zu versorgen. Die damit einhergehenden Veränderungen der Vegetationsbedeckung haben bedeutende Auswirkungen auf vielfältige ökologische Dienstleistungen. Angesichts der globalen Herausforderungen scheint es daher angebracht, darüber nachzudenken, welches Optimierungspotential in der Verteilung der landwirtschaftlichen Flächen besteht. Wir untersuchen hier, welche Zielkonflikte zwischen Nahrungsmittelproduktion, Kohlenstoffspeicherung und Wasserverfügbarkeit auf globaler Ebene bestehen. Hierzu wurden Szenariorechnungen des dynamischen globalen Vegetationsmodells LPJ-GUESS genutzt, um Pareto-optimale Lösungen verschiedener Landnutzungszuweisungen hinsichtlich der drei Zieldimensionen zu schätzen. Als Optimierungsverfahren kam ein genetischer Algorithmus (NSGA-II) zum Einsatz. Die Ergebnisse zeigen deutliche Optimierungspotentiale gegenüber der heutigen Landutzungskonfiguration, die jedoch für einzelne Weltregionen nicht unerhebliche Veränderungen nach sich ziehen würden.

Ludwig et al. (2019). Assessing the Completeness of Urban Green Spaces in OpenStreetMap In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 21-22. Heidelberg, Germany, September 21-23, 2019. Available at https://zenodo.org/communities/sotm-2019 DOI: 10.5281/zenodo.3387701

The estimation of the value of georeferenced spaces is challenging because the value of a space depends on many factors such as recreation potential, sociability, cultural points of interest, etc. To account for these personal values, interviews and surveys can be conducted but this is costly and elaborate, and this requires a user action. Recent approaches use social media to attribute positive or negative associations of users to a georeferenced space by analysing the semantic linkages. This approach is promising but at the same time prone to biases due to a user selection bias and the interpretation of the semantics. In this paper, an alternative method based on the systematic analysis of georeferenced paths of people is proposed, which does not require direct user interaction. Underlying the assumption that people are willing to take a longer and more time-consuming path if the detour has a personal value for them, this work proposes to estimate these personal values systematically and partly automated by analysing the pathways taken. The authors suggest to conduct a first feasibility study analysing the GPS positions of people to obtain information and find patterns of the personal preferences of places, and matching them to interview or social media derived information on platial preferences.