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Building types map of Germany

Schug, Franz; Frantz, David; van der Linden, Sebastian; Hostert, Patrick


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        "title": "Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society", 
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    "keywords": [
      "Remote Sensing", 
      "Earth Observation", 
      "Sentinel-1", 
      "Sentinel-2", 
      "Copernicus", 
      "Germany", 
      "Building", 
      "Building Types", 
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    "publication_date": "2021-03-12", 
    "creators": [
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        "affiliation": "Humboldt-Universit\u00e4t zu Berlin", 
        "name": "Schug, Franz"
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        "affiliation": "Humboldt-Universit\u00e4t zu Berlin", 
        "name": "Frantz, David"
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      {
        "affiliation": "University of Greifswald", 
        "name": "van der Linden, Sebastian"
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        "affiliation": "Humboldt-Universit\u00e4t zu Berlin", 
        "name": "Hostert, Patrick"
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    "description": "<p>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.</p>\n\n<p><strong>Temporal extent</strong></p>\n\n<p>Sentinel-2 time series data are from 2018. Sentinel-1 time series data are from 2017.</p>\n\n<p><strong>Data format</strong></p>\n\n<p>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:</p>\n\n<p>0 - No building</p>\n\n<p>1 - Commercial and industrial buildings</p>\n\n<p>2 - Single-family residential buildings</p>\n\n<p>3 - Lightweight structures</p>\n\n<p>4 - Multi-family residential buildings</p>\n\n<p><strong>Further information</strong></p>\n\n<p>For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).<br>\nA web-visualization of this dataset is available <a href=\"https://ows.geo.hu-berlin.de/webviewer/population/\">here</a>.</p>\n\n<p><strong>Publication</strong></p>\n\n<p>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</p>\n\n<p><strong>Acknowledgements</strong></p>\n\n<p>The dataset was generated by FORCE v. 3.1 (<a href=\"https://doi.org/10.3390/rs11091124\">paper</a>, <a href=\"https://github.com/davidfrantz/force\">code</a>), which is freely available software under the terms of the GNU General Public License v. &gt;= 3. Sentinel imagery were obtained from the <a href=\"https://scihub.copernicus.eu/\">European Space Agency and the European Commission</a>.</p>\n\n<p><strong>Funding</strong><br>\nThis dataset was produced with funding from the European Research Council (ERC) under the European Union&#39;s Horizon 2020 research and innovation programme (<a href=\"https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks\">MAT_STOCKS</a>, grant agreement No 741950).</p>"
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