Poster Open Access

Semi-supervised Sentinel-2 tree species detection

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


Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Vermeer, Martijn</dc:creator>
  <dc:creator>Sørensen, Tord K.</dc:creator>
  <dc:creator>Völgyes, David</dc:creator>
  <dc:creator>Fantin, Daniele</dc:creator>
  <dc:creator>Miller, Heidrun</dc:creator>
  <dc:date>2022-05-19</dc:date>
  <dc:description>For forest management the availability of complete and up-to-date forest inventories is essential, with one of the most important parameters being the volumetric tree species distribution. Unfortunately, tree species mapping in Norwegian production forests is a time-consuming and largely manual process, leading to forest inventories that are often incomplete and/or outdated. Indeed, the determination of the tree species distribution is currently performed by a forestry expert, mainly
by visual interpretation of aerial imagery and in some cases lidar data. High resolution aerial imagery is available, however  campaigns are expensive and therefore infrequent. Satellite imagery, on the other hand, provides dense time series, but has a much lower resolution. The primary goal of the SENTREE project is to automate the classification of Norwegian main production tree species (Norway spruce, Scots pine and Birch) using semantic segmentation networks on a fusion of aerial and satellite data sources.</dc:description>
  <dc:identifier>https://zenodo.org/record/6563721</dc:identifier>
  <dc:identifier>10.5281/zenodo.6563721</dc:identifier>
  <dc:identifier>oai:zenodo.org:6563721</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>doi:10.5281/zenodo.6563720</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Semi-supervised Sentinel-2 tree species detection</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePoster</dc:type>
  <dc:type>poster</dc:type>
</oai_dc:dc>
52
51
views
downloads
All versions This version
Views 5252
Downloads 5151
Data volume 681.5 MB681.5 MB
Unique views 4848
Unique downloads 4141

Share

Cite as