One of the contributions showcased by the HeiGIT/GeoScience team in last year’s Free and Open Source Software for Geospatial (FOSS4G) 2022 conference in Florence (Italy) was Moritz Schott, Sven Lautenbach, Leonie Großchen, and Alexander Zipf’s novel paper “OpenStreetMap Element Vectorisation: A Tool for High Resolution Data Insights and its Usability in the Land-use and Land-cover Domain.”
The contribution presents a tool to address the much-discussed issue of fitness for purpose. As researchers and users take advantage of OpenStreetMap in spatial analysis and location-based service applications, they often miss information on the data quality. Available information in that regard is often limited to certain regions or data aspects. The new tool combines a large number of such information into a single tool and at the highest possible resolution: single OSM elements.
The new tool, OpenStreetMap Element Vectorisation (repository), currently provides access to 32 attributes or indicators at the level of single OpenStreetMap objects. These indicators cover aspects concerning the element itself, surrounding objects and the editors of the object. A graphic workflow illustrates the steps required to use the tool from file configuration and database setup to result output. Yet, the tool provides a Docker workflow that simplifies the process into a few simple commands. Alternatively, the website and the api provide a starting point to test a few precomputed examples.
Testing Grounds
The paper not only introduced but sought to test OpenStreetMap Element Vectorisation’s usability for the use case of LULC elements of land-use and land-cover polygons. The data and all related figures of the analysis are openly available in the respective repository.
The researchers found that OpenStreetMap objects in more densely-populated areas tended to be smaller, while the age and size of the objects differed across continents. In Europe and North America, the researchers detected older and smaller objects. These findings were backed up by statistical test. Yet it became clear that such trends are hardly visible on a global perspective and more localised analyses are required.
In addition, a k-means cluster analysis was used to identify groups in the data in search of global data practices. One finding of the method was a cluster highly influenced by North American lakes that originate from imports.
Eye to the Future
The tool offers amble opportunities for future research, supports the OpenStreetMap community by making informed planning decisions for future activities and enables data consumers to make informed decisions on data usage. While the development was made with land-use and land-cover information in mind, the tool can be seamlessly applied to any polygonal OpenStreetMap data and also supports linear and point data. In addition it is not restricted to quality analyses but rather provides a universal collection of data attributes that have relevance in different applications.
For full paper and all references:
Schott, M., Lautenbach, S., Großchen , L., & Zipf, A. (2022). openstreetmap element vectorisation – a tool for high resolution data insights and its usability in the land-use and land-cover domain. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-4/W1-2022. https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-395-2022
Related work:
- Bruckner, J., Schott, M., Zipf, A., Lautenbach, S., 2021. Assessing shop completeness in OpenStreetMap for two federal states in Germany. AGILE: GIScience Series, 2, 20.
- Herfort, B., Lautenbach, S., de Albuquerque, J. P., Anderson, J., Zipf, A., 2021. The evolution of humanitarian mapping within the OpenStreetMap community. Scientific Reports, 11(1), 1–15.
- Jokar Arsanjani, J., Mooney, P., Zipf, A., Schauss, A., 2015. Quality assessment of the contributed land use information from openstreetmap versus authoritative datasets. J. Jokar Arsanjani, A. Zipf, P. Mooney, M. Helbich (eds), OpenStreetMap in GIScience: Experiences, Research, and Applications, Springer International Publishing, Cham, 37–58.
- Neis, P., Zielstra, D., Zipf, A., 2013. Comparison of Volunteered Geographic Information Data Contributions and Community Development for Selected World Regions. Future Internet, 5(2), 282–300.
- Neis, P., Zipf, A., 2012. Analyzing the Contributor Activity of a Volunteered Geographic Information Project — The Case of OpenStreetMap. ISPRS International Journal of GeoInformation, 1(2), 146–165.
- Raifer, M., Troilo, R., Kowatsch, F., Auer, M., Loos, L., Marx, S., Przybill, K., Fendrich, S., Mocnik, F.-B., Zipf, A., 2019. OSHDB: a framework for spatio-temporal analysis of OpenStreetMap history data. Open Geospatial Data, Software and Standards, 4(1), 1–12.
- Schott, M., Grinberger, A. Y., Lautenbach, S., Zipf, A., 2021. The Impact of Community Happenings in OpenStreetMap—Establishing a Framework for Online Community Member Activity Analyses. ISPRS International Journal of GeoInformation, 10(3), 164.
- Schott, M., Zell, A., Lautenbach, S., Zipf, A., Demir, B., 2022. LULC multi-tags based on OSM, Version 0.1. https://gitlab.gistools.geog.uni-heidelberg.de/giscience/idealvgi/osm-multitag.
- Schultz, M., Voss, J., Auer, M., Carter, S., Zipf, A., 2017. Open land cover from OpenStreetMap and remote sensing. International Journal of Applied Earth Observation and Geoinformation, 63, 206-213.
- Zielstra, D., Zipf, A., 2010. A comparative study of proprietary geodata and volunteered geographic information for Germany. 13th AGILE international conference on geographic information science, 2010, 1–15.