The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVIII-4/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W1-2022, 395–402, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-395-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W1-2022, 395–402, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-395-2022
 
06 Aug 2022
06 Aug 2022

OPENSTREETMAP ELEMENT VECTORISATION - A TOOL FOR HIGH RESOLUTION DATA INSIGHTS AND ITS USABILITY IN THE LAND-USE AND LAND-COVER DOMAIN

M. Schott1, S. Lautenbach1,2, L. Größchen1, and A. Zipf1,2 M. Schott et al.
  • 1GIScience, Institute of Geography, Heidelberg University, Germany
  • 2HeiGIT at Heidelberg University, Germany

Keywords: Data Analysis, OpenStreetMap, Software, Data Quality, Land-use and Land-cover, Intrinsic Data Quality

Abstract. OpenStreetMap offers manifold possibilities for spatial analysis and location based services. However, fitness for purpose is a commonly discussed issue. As external datasets of high quality are frequently missing, many assessments rely on intrinsic methods. Existing tools for intrinsic data analysis tend to focus on specific topics and/or regions. We present a tool that provides access to currently 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. The usability of the tool was proven on the use case of land-use and land-cover polygons. We applied the tool to 1000 randomly sampled polygons. A tendency that OpenStreetMap objects in more densely populated areas were smaller was detected. Age and size of the objects differed across the continents with older and smaller objects in Europe and North America. A k-means cluster analysis was used to identify groups in the data. This detected a cluster highly influenced by North American lakes that originate from imports. 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.