About
OSM Landuse Landcover is a WebGIS application to explore the OpenStreetMap database specifically in terms of landuse and landcover information.
Info
- Project lead - Alexander Zipf, Michael Schultz
- Website - Michael Auer, Moritz Schott
- Cartographic styling of landuse/landcover overlay - Michael Auer, Janek Voss
- Data of landuse/landcover overlay by OpenStreetMap contibutors, under ODbL.
Missing OSM data (gaps) were filled for Europe (ongoing) using data derived from Sentinel-2 10m RGB imagery provided through Daniel Weill of Food and Agriculture Organization (FAO) and Deep Learning Methods developed by Michael Schultz, Hao Li and Zhaoyan Wu - Geoserver WMS & MapProxy tilecache maintanance - Michael Auer (HeiGIT gGmbH)
- PostgreSQL/PostGIS Database with OpenStreetMap Data - Enrico Steiger, Maxim Rylov, Johannes Lauer, Markus Götz, Michael Auer
- Background map - © OpenStreetMap contributors under CC BY-SA 2.0
- Background Aerial Imagery - © Esri, source: Esri, Maxar, Earthstar Geographics, and the GIS User Community
- Place Search - powered by Nominatim
Layer Description
- OSMLanduse: OSM land-use/land-cover information mapped to CORINE classes (see below)
- The layer is also provided as a Web Map Service (WMS) at https://maps.heigit.org/osmlanduse/service that you can use in your GIS software. The Layer name is osmlanduse:osm_lulc
- Gap-filled OSMLanduse: Classification of Sentinel-2 imagery using a machine learning model trained on OSMLanduse. Land use tags were predicted when absent using belows (Schultz et al. 2020 in prep, Schultz et al. 2017) method. This was first addressed for Germany (2017) and (2020) - with the improved methods - for all EU countries.
OSM keys used for classification
Classification of the landuse/landcover classes are similar to the classification level 2 of the CORINE Landcover classes.
The following OSM keys were used to form the respective class:
residential, garages
1.2. Industrial, commercial and transport unitsrailway, industrial, commercial, retail, harbour, port, lock, marina
1.3. Mine, dump and construction sitesquarry, construction, landfill, brownfield
1.4. Artificial, non-agricultural vegetated areasstadium, recreation_ground, golf_course, sports_center, playground
pitch, village_green, allotments, cemetery, park, zoo, track, garden, raceway
greenhouse_horticulture, greenhouse, farmland, farm, farmyard
2.2. Permanent Cropsvineyard, orchard
2.3. Pasturesmeadow
3.1. Forestsforest, wood
3.2. Shrub and/or herbaceous vegetation associationsgrass, greenfield, scrub, heath, grassland
3.3. Open spaces with little or no vegetationcliff, fell, sand, scree, beach, mud, glacier, rock
4.1. Inland wetlandsmarsh, wetland
4.2. Coastal wetlandssalt_pond, tidal
5. Water bodieswater, riverbank, reservoir, basin, dock, canal, pond
Acknowledgements
This work is supported by 
.
Terms of use
Overlay tiles of "OSM Landuse Landcover" can be used freely and without charge by any individuals through this website.
If you intend to use data from "OSM Landuse Landcover" in your own applications, please use our dataset available at the following DOI: doi.org/10.11588/data/IUTCDN under the CC BY 4.0 license.
OpenStreetMap data is available under the Open Database License.
Related Project & Literature
- LandSense: A Citizen Observatory and Innovation Marketplace for Land Use and Land Cover Monitoring
- 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. https://doi.org/10.1016/j.jag.2017.07.014
- Voß, J., Auer, M., Schultz, M. & Zipf, A. (2017): Einsatz von OpenStreetMap Daten zur Erstellung von Landnutzungsprodukten am Beispiel von OSM Landuse Landcover. Symposium für Angewandte Geoinformatik AGIT 2017. Salzburg
Contact
Prof. Dr. Alexander Zipf
HeiGIT gGmbH
Heidelberg Institute for Geoinformation Technology (HeiGIT)
heigit.org
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