Building types map of Germany

This dataset features a map of building types for Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. A random forest classification was used to map the predominant type of buildings within a pixel. We distinguish single-family residential buildings, multi-family residential buildings, commercial and industrial buildings and lightweight structures. Building types were predicted for all pixels where building density &gt; 25 %. Please refer to the publication for details. <strong>Temporal extent</strong> Sentinel-2 time series data are from 2018. Sentinel-1 time series data are from 2017. <strong>Data format</strong> The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building type values are categorical, according to the following scheme: 0 - No building 1 - Commercial and industrial buildings 2 - Single-family residential buildings 3 - Lightweight structures 4 - Multi-family residential buildings <strong>Further information</strong> For further information, please see the publication or contact Franz Schug ([e-mail]).<br> A web-visualization of this dataset is available here. <strong>Publication</strong> Schug, F., Frantz, D., van der Linden, S., &amp; Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044 <strong>Acknowledgements</strong> The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. &gt;= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission. <strong>Funding</strong><br> This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).