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dc.contributor.authorWaldeland, Anders Ueland
dc.contributor.authorSalberg, Arnt-Børre
dc.contributor.authorTrier, Øivind Due
dc.contributor.authorVollrath, Andreas
dc.date.accessioned2021-03-11T09:37:36Z
dc.date.available2021-03-11T09:37:36Z
dc.date.created2021-03-09T11:22:47Z
dc.date.issued2020
dc.identifier.isbn9781728163741
dc.identifier.urihttps://hdl.handle.net/11250/2732775
dc.description.abstractThe deep learning revolution in computer vision has enabled a potential for creating new value chains for Earth observation that significantly enhances the analysis of satellite data for tasks like land cover mapping, change analysis, and object detection. We demonstrate a deep learning based value chain for the task of mapping vegetation height in the Liwale region in Tanzania using Sentinel-1 and −2 data. As ground truth data we use lidar measurements, which are processed to provide the average vegetation height per Sentinel-2 pixel grid (10 m). We apply the UNet, which is a widely used neural network for segmentation tasks in computer vision, to estimate average vegetation height from the Sentinel data. Preliminary results show that we are able to map the forest extent with high accuracy, with an RMSE of 3.5 m for Sentinel-2 data and 4.6 m for the Sentinel-1 data.
dc.language.isoengen_US
dc.relation.ispartofIGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. Proceedings
dc.rightsNavngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/deed.no*
dc.titleLarge-Scale Vegetation Height Mapping from Sentinel Data Using Deep Learningen_US
dc.typeChapteren_US
dc.description.versionsubmittedVersion
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1
dc.identifier.cristin1896597


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Navngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal
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