Poster Open Access

Semi-supervised Sentinel-2 tree species detection

Vermeer, Martijn; Sørensen, Tord K.; Völgyes, David; Fantin, Daniele; Miller, Heidrun


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  <identifier identifierType="DOI">10.5281/zenodo.6563721</identifier>
  <creators>
    <creator>
      <creatorName>Vermeer, Martijn</creatorName>
      <givenName>Martijn</givenName>
      <familyName>Vermeer</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2777-6584</nameIdentifier>
      <affiliation>Science and Technology AS</affiliation>
    </creator>
    <creator>
      <creatorName>Sørensen, Tord K.</creatorName>
      <givenName>Tord K.</givenName>
      <familyName>Sørensen</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5879-3969</nameIdentifier>
      <affiliation>Science and Technology AS</affiliation>
    </creator>
    <creator>
      <creatorName>Völgyes, David</creatorName>
      <givenName>David</givenName>
      <familyName>Völgyes</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9723-5532</nameIdentifier>
      <affiliation>Science and Technology AS</affiliation>
    </creator>
    <creator>
      <creatorName>Fantin, Daniele</creatorName>
      <givenName>Daniele</givenName>
      <familyName>Fantin</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5339-0239</nameIdentifier>
      <affiliation>Science and Technology AS</affiliation>
    </creator>
    <creator>
      <creatorName>Miller, Heidrun</creatorName>
      <givenName>Heidrun</givenName>
      <familyName>Miller</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1637-9719</nameIdentifier>
      <affiliation>Allskog SA</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Semi-supervised Sentinel-2 tree species detection</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2022</publicationYear>
  <dates>
    <date dateType="Issued">2022-05-19</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Poster</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/6563721</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.6563720</relatedIdentifier>
  </relatedIdentifiers>
  <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;For forest management the availability of complete and up-to-date forest inventories is essential, with one of the most important parameters&amp;nbsp;being the volumetric tree species distribution. Unfortunately, tree species mapping in Norwegian&amp;nbsp;production forests is a time-consuming and largely&amp;nbsp;manual process, leading to forest inventories that&amp;nbsp;are often incomplete and/or outdated. Indeed, the&amp;nbsp;determination of the tree species distribution is&amp;nbsp;currently performed by a forestry expert, mainly&lt;br&gt;
by visual interpretation of aerial imagery and in&amp;nbsp;some cases lidar data. High resolution aerial imagery is available, however&amp;nbsp; campaigns are expensive and therefore infrequent. Satellite imagery, on&amp;nbsp;the other hand, provides dense time series, but has&amp;nbsp;a much lower resolution. The primary goal of the&amp;nbsp;SENTREE project is to automate the classification&amp;nbsp;of Norwegian main production tree species (Norway spruce, Scots pine and Birch) using semantic&amp;nbsp;segmentation networks on a fusion of aerial and&amp;nbsp;satellite data sources.&lt;/p&gt;</description>
  </descriptions>
</resource>
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