Dataset Open Access

OpenStreetMap+ Land Use / Land Cover classes and administrative regions of Europe

Martijn Witjes

This dataset contains 23 30m resolution raster data of continental Europe land use / land cover classes extracted from OpenStreetMap, as well as administrative areas, and a harmonized building dataset based on OpenStreetMap and Copernicus HRL Imperviousness data.

The land use / land cover classes are:

  1. buildings.commercial
  2. buildings.industrial
  3. buildings.residential
  4. cemetery
  5. construction.site
  6. dump.site (landfill)
  7. farmland
  8. farmyard
  9. forest
  10. grass
  11. greenhouse
  12. harbour
  13. meadow
  14. military
  15. orchard
  16. quarry
  17. railway
  18. reservoir
  19. road
  20. salt
  21. vineyard

The land use / land cover data was generated by extracting OSM vector layers from https://download.geofabrik.de/). These were then transformed into a 30 m density raster for each feature type. This was done by first creating a 10 m raster where each pixel intersecting a vector feature was assigned the value 100. These pixels were then aggregated to 10 m resolution by calculating the average of every 9 adjacent pixels. This resulted in a 0—100 density layer for the three feature types. Although the digitized building data from OSM offers the highest level of detail, its coverage across Europe is inconsistent. To supplement the building density raster in regions where crowd-sourced OSM building data was unavailable, we combined it with Copernicus High Resolution Layers (HRL) (obtained from https://land.copernicus.eu/pan-european/ high-resolution-layers), filling the non-mapped areas in OSM with the Impervious Built-up 2018 pixel values, which was averaged to 30 m. The probability values produced by the averaged aggregation were integrated in such a way that values between 0—100 refer to OSM (lowest and highest probabilities equal to 0 and 100 respectively), and the values between 101—200 refer to Copernicus HRL (lowest and highest probability equal to 200 and 101 respectively). This resulted in a raster layer where values closer to 100 are more likely to be buildings than values closer to 0 and 200. Structuring the data in this way allows us to select the higher probability building pixels in both products by the single boolean expression: Pixel > 50 AND pixel <150.

This dataset is part of the OpenStreetMap+ was used to pre-process the LUCAS/CORINE land use / land cover samples (https://doi.org/10.5281/zenodo.4740691) used to train machine learning models in Witjes et al., 2022 (https://doi.org/10.21203/rs.3.rs-561383/v4)

Each layer can be viewed interactively on the Open Data Science Europe data viewer at maps.opendatascience.eu.

This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
Files (4.1 GB)
Name Size
adm_county_nuts.osm_c_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:c8fabe36b0926701590b99b2ea1715aa
119.2 MB Download
lcv_building.commercial_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:aa2e9a1e3159a905f8bbf9936feaeef4
51.5 MB Download
lcv_building.industrial_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:f27186066f59eb6c0e5568c3e2d658fa
59.1 MB Download
lcv_building.residential_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:4d25650d0b0d0ee04febacde419ab059
103.7 MB Download
lcv_building_copernicus.osm_p_30m_0..0cm_2018..2021_eumap_epsg3035_v0.1.tif
md5:b9507b4f4154c532071768b0372ef224
550.5 MB Download
lcv_cemetery_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:f8e0e6394f7db06b175ac63e5769b641
53.9 MB Download
lcv_construction.site_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:1b2f05c7a0599f137e645b138b8c1322
43.9 MB Download
lcv_dump.site_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:1ebf203f305823e30cbb006c452b00c1
40.8 MB Download
lcv_farmland_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:f588533626e4b63d93d910b084c312a4
447.9 MB Download
lcv_farmyard_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:8aa60f4f0dadf81372938bdfacb0acf1
82.5 MB Download
lcv_forest_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:cc23ed6563a0bc51000276889e8dafec
635.6 MB Download
lcv_grass_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:4ba40e876267524a116e5b1f60f3ae47
109.0 MB Download
lcv_greenhouse_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:59734b5123d0dc2bed53d9c4787638e8
40.7 MB Download
lcv_harbour_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:462494a15a4afbe5ade13cd83616ed37
38.7 MB Download
lcv_meadow_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:c3f0810fd674b525e39921f2d002930a
282.0 MB Download
lcv_military_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:286f5f84398cc4176e472aac6c14299b
39.0 MB Download
lcv_orchard_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:5b8b7f24dfe50b93590e14d6d268072d
82.9 MB Download
lcv_quarry_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:ae8b2a2e5788bbb7530c77b75b15653a
50.3 MB Download
lcv_railway_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:530f9eb248fb3628f68b0ae7eaccd240
89.6 MB Download
lcv_reservoir_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:4eef64705151bec194895d9360b25ee2
51.5 MB Download
lcv_road_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:06f12cd14c5153bbef38c3d575a1ee79
1.0 GB Download
lcv_salt_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:a105e0a73fe5e909b54700d60a6fb712
38.7 MB Download
lcv_vineyard_osm_p_30m_0..0cm_2021_eumap_epsg3035_v0.1.tif
md5:f21b56f48f453e98d25d32cc41eccd41
64.1 MB Download
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