Dataset Open Access

Building types map of Germany

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


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    <dct:title>Building types map of Germany</dct:title>
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    <dcat:keyword>Remote Sensing</dcat:keyword>
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    <dcat:keyword>Sentinel-1</dcat:keyword>
    <dcat:keyword>Sentinel-2</dcat:keyword>
    <dcat:keyword>Copernicus</dcat:keyword>
    <dcat:keyword>Germany</dcat:keyword>
    <dcat:keyword>Building</dcat:keyword>
    <dcat:keyword>Building Types</dcat:keyword>
    <dcat:keyword>Settlement</dcat:keyword>
    <dcat:keyword>Map</dcat:keyword>
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    <dct:description>&lt;p&gt;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 &amp;gt; 25 %. Please refer to the publication for details.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Temporal extent&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Sentinel-2 time series data are from 2018. Sentinel-1 time series data are from 2017.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Data format&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;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:&lt;/p&gt; &lt;p&gt;0 - No building&lt;/p&gt; &lt;p&gt;1 - Commercial and industrial buildings&lt;/p&gt; &lt;p&gt;2 - Single-family residential buildings&lt;/p&gt; &lt;p&gt;3 - Lightweight structures&lt;/p&gt; &lt;p&gt;4 - Multi-family residential buildings&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Further information&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).&lt;br&gt; A web-visualization of this dataset is available &lt;a href="https://ows.geo.hu-berlin.de/webviewer/population/"&gt;here&lt;/a&gt;.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Publication&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Schug, F., Frantz, D., van der Linden, S., &amp;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&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Acknowledgements&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;The dataset was generated by FORCE v. 3.1 (&lt;a href="https://doi.org/10.3390/rs11091124"&gt;paper&lt;/a&gt;, &lt;a href="https://github.com/davidfrantz/force"&gt;code&lt;/a&gt;), which is freely available software under the terms of the GNU General Public License v. &amp;gt;= 3. Sentinel imagery were obtained from the &lt;a href="https://scihub.copernicus.eu/"&gt;European Space Agency and the European Commission&lt;/a&gt;.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;Funding&lt;/strong&gt;&lt;br&gt; This dataset was produced with funding from the European Research Council (ERC) under the European Union&amp;#39;s Horizon 2020 research and innovation programme (&lt;a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks"&gt;MAT_STOCKS&lt;/a&gt;, grant agreement No 741950).&lt;/p&gt;</dct:description>
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