OpenStreetMap in GIScience

Part of the series Lecture Notes in Geoinformation and Cartography pp 37-58


Quality Assessment of the Contributed Land Use Information from OpenStreetMap Versus Authoritative Datasets

  • Jamal Jokar ArsanjaniAffiliated withGIScience Research Group, Institute of Geography, Heidelberg University Email author 
  • , Peter MooneyAffiliated withDepartment of Computer Science, Maynooth University
  • , Alexander ZipfAffiliated withGIScience Research Group, Institute of Geography, Heidelberg University
  • , Anne SchaussAffiliated withGIScience Research Group, Institute of Geography, Heidelberg University

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Land use (LU) maps are an important source of information in academia and for policy-makers describing the usage of land parcels. A large amount of effort and monetary resources are spent on mapping LU features over time and at local, regional, and global scales. Remote sensing images and signal processing techniques, as well as land surveying are the prime sources to map LU features. However, both data gathering approaches are financially expensive and time consuming. But recently, Web 2.0 technologies and the wide dissemination of GPS-enabled devices boosted public participation in collaborative mapping projects (CMPs). In this regard, the OpenStreetMap (OSM) project has been one of the most successful representatives, providing LU features. The main objective of this paper is to comparatively assess the accuracy of the contributed OSM-LU features in four German metropolitan areas versus the pan-European GMESUA dataset as a reference. Kappa index analysis along with per-class user’s and producers’ accuracies are used for accuracy assessment. The empirical findings suggest OSM as an alternative complementary source for extracting LU information whereas exceeding 50 % of the selected cities are mapped by mappers. Moreover, the results identify which land types preserve high/moderate/low accuracy across cities for urban LU mapping. The findings strength the potential of collaboratively collected LU features for providing temporal LU maps as well as updating/enriching existing inventories. Furthermore, such a collaborative approach can be used for collecting a global coverage of LU information specifically in countries in which temporal and monetary efforts could be minimized.


Land use features Comparative assessment Global monitoring for environment and security urban atlas (GMESUA) OpenStreetMap Confusion matrix