Dataset Open Access

Building types map of Germany

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


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  <identifier identifierType="DOI">10.5281/zenodo.4601219</identifier>
  <creators>
    <creator>
      <creatorName>Schug, Franz</creatorName>
      <givenName>Franz</givenName>
      <familyName>Schug</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1534-5610</nameIdentifier>
      <affiliation>Humboldt-Universität zu Berlin</affiliation>
    </creator>
    <creator>
      <creatorName>Frantz, David</creatorName>
      <givenName>David</givenName>
      <familyName>Frantz</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9292-3931</nameIdentifier>
      <affiliation>Humboldt-Universität zu Berlin</affiliation>
    </creator>
    <creator>
      <creatorName>van der Linden, Sebastian</creatorName>
      <givenName>Sebastian</givenName>
      <familyName>van der Linden</familyName>
      <affiliation>University of Greifswald</affiliation>
    </creator>
    <creator>
      <creatorName>Hostert, Patrick</creatorName>
      <givenName>Patrick</givenName>
      <familyName>Hostert</familyName>
      <affiliation>Humboldt-Universität zu Berlin</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Building types map of Germany</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Remote Sensing</subject>
    <subject>Earth Observation</subject>
    <subject>Sentinel-1</subject>
    <subject>Sentinel-2</subject>
    <subject>Copernicus</subject>
    <subject>Germany</subject>
    <subject>Building</subject>
    <subject>Building Types</subject>
    <subject>Settlement</subject>
    <subject>Map</subject>
    <subject>Machine Learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-03-12</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4601219</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsSupplementTo" resourceTypeGeneral="JournalArticle">10.1371/journal.pone.0249044</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4601218</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&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;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/741950/">741950</awardNumber>
      <awardTitle>Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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