Open land cover from OpenStreetMap and remote sensing

Michael Schultz*, Janek Voss, Michael Auer, Sarah Carter, Alexander Zipf

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

64 Citations (Scopus)


OpenStreetMap (OSM) tags were used to produce a global Open Land Cover (OLC) product with fractional data gaps available at Data gaps in the global OLC map were filled for a case study in Heidelberg, Germany using free remote sensing data, which resulted in a land cover (LC) prototype with complete coverage in this area. Sixty tags in the OSM were used to allocate a Corine Land Cover (CLC) level 2 land use classification to 91.8% of the study area, and the remaining gaps were filled with remote sensing data. For this case study, complete are coverage OLC overall accuracy was estimated 87%, which performed better than the CLC product (81% overall accuracy) of 2012. Spatial thematic overlap for the two products was 84%. OLC was in large parts found to be more detailed than CLC, particularly when LC patterns were heterogeneous, and outperformed CLC in the classification of 12 of the 14 classes. Our OLC product represented data created in different periods; 53% of the area was 2011–2016, and 46% of the area was representative of 2016–2017.
Original languageEnglish
Pages (from-to)206-213
JournalInternational Journal of applied Earth Observation and Geoinformation
Publication statusPublished - Dec 2017


  • Land cover
  • OpenStreetMap
  • Random forest
  • Remote sensing
  • Tag based


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