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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        <foaf:name>Sørensen, Tord K.</foaf:name>
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    <dct:title>Semi-supervised Sentinel-2 tree species detection</dct:title>
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    <dct:description>&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and largely&amp;nbsp;manual process, leading to forest inventories that&amp;nbsp;are often incomplete and/or outdated.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;</dct:description>
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May 19, 2022
DOI:
10.5281/zenodo.6563721

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