October 5, 2020 Dataset Open Access

Frantz, David; Schug, Franz; Okujeni, Akpona; Navacchi, Claudio; Wagner, Wolfgang; van der Linden, Sebastian; Hostert, Patrick

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  <identifier identifierType="DOI">10.5281/zenodo.4066295</identifier>
  <creators>
    <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>Schug, Franz</creatorName>
      <givenName>Franz</givenName>
      <familyName>Schug</familyName>
      <affiliation>Humboldt-Universität zu Berlin</affiliation>
    </creator>
    <creator>
      <creatorName>Okujeni, Akpona</creatorName>
      <givenName>Akpona</givenName>
      <familyName>Okujeni</familyName>
      <affiliation>Humboldt-Universität zu Berlin</affiliation>
    </creator>
    <creator>
      <creatorName>Navacchi, Claudio</creatorName>
      <givenName>Claudio</givenName>
      <familyName>Navacchi</familyName>
      <affiliation>TU Wien</affiliation>
    </creator>
    <creator>
      <creatorName>Wagner, Wolfgang</creatorName>
      <givenName>Wolfgang</givenName>
      <familyName>Wagner</familyName>
      <affiliation>TU Wien</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 height map of Germany</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Remote Sensing</subject>
    <subject>Earth Observation</subject>
    <subject>Copernicus</subject>
    <subject>Germany</subject>
    <subject>Built-up</subject>
    <subject>City</subject>
    <subject>Building height</subject>
    <subject>FORCE</subject>
    <subject>Sentinel-1</subject>
    <subject>Sentinel-2</subject>
    <subject>Settlement</subject>
    <subject>Machine Learning</subject>
    <subject>Map</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-10-05</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4066295</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsSupplementTo" resourceTypeGeneral="JournalArticle">10.1016/j.rse.2020.112128</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4066294</relatedIdentifier>
  </relatedIdentifiers>
  <version>v. 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;Urban areas have a manifold and far-reaching impact on our environment, and the three-dimensional structure is a key aspect for characterizing the urban environment.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;This dataset features a map of building height predictions for entire Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. We utilized machine learning regression to extrapolate building height reference information to the entire country. The reference data were obtained from several freely and openly available 3D Building Models originating from official data sources (building footprint: cadaster, building height: airborne laser scanning), and represent the average building height within a radius of 50m relative to each pixel. Building height was only estimated for built-up areas (European Settlement Mask), and building height predictions &amp;lt;2m were set to 0m.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Temporal extent&lt;/strong&gt;&lt;br&gt;
The acquisition dates of the different data sources vary to some degree:&lt;br&gt;
- Independent variables: Sentinel-2 data are from 2018; Sentinel-1 data are from 2017.&lt;br&gt;
- Dependent variables: the 3D building models are from 2012-2020 depending on data provider.&lt;br&gt;
- Settlement mask: the ESM is based on a mosaic of imagery from 2014-2016.&lt;br&gt;
Considering that net change of building stock is positive in Germany, the building height map is representative for ca. 2015.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Data format&lt;/strong&gt;&lt;br&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 height values are in meters, scaled by 10, i.e. a pixel value of 69 = 6.9m.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Further information&lt;/strong&gt;&lt;br&gt;
For further information, please see the publication or contact David Frantz (david.frantz@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/building-height/"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Publication&lt;/strong&gt;&lt;br&gt;
Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., &amp;amp; Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. DOI: &lt;a href="https://doi.org/10.1016/j.rse.2020.112128"&gt;https://doi.org/10.1016/j.rse.2020.112128&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Acknowledgements&lt;/strong&gt;&lt;br&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;. The European Settlement Mask was obtained from the &lt;a href="https://data.jrc.ec.europa.eu/dataset/8bd2b792-cc33-4c11-afd1-b8dd60b44f3b"&gt;European Commission&lt;/a&gt;. 3D building models were obtained from &lt;a href="https://www.businesslocationcenter.de/en/economic-atlas/download-portal/"&gt;Berlin Partner f&amp;uuml;r Wirtschaft und Technologie GmbH&lt;/a&gt;, &lt;a href="http://suche.transparenz.hamburg.de/dataset/3d-stadtmodell-lod2-de-hamburg4?forceWeb=true"&gt;Freie und Hansestadt Hamburg / Landesbetrieb Geoinformation und Vermessung&lt;/a&gt;, &lt;a href="https://opendata.potsdam.de/explore/dataset/3d-gebaudemodell-lod2-citygml/information"&gt;Landeshauptstadt Potsdam&lt;/a&gt;, &lt;a href="https://www.bezreg-koeln.nrw.de/brk_internet/geobasis/3d_gebaeudemodelle/index.html"&gt;Bezirksregierung K&amp;ouml;ln / Geobasis NRW&lt;/a&gt;, and &lt;a href="https://www.geoportal-th.de/de-de/Downloadbereiche/Download-Offene-Geodaten-Th%C3%BCringen/Download-3D-Geb%C3%A4ude"&gt;Kompetenzzentrum Geodateninfrastruktur Th&amp;uuml;ringen&lt;/a&gt;. This dataset was partly produced on &lt;a href="https://eodc.eu"&gt;EODC&lt;/a&gt;&amp;nbsp;- we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.&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>
    <description descriptionType="Other">{"references": ["Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., &amp; Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. DOI: https://doi.org/10.1016/j.rse.2020.112128"]}</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</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>
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