Delineating giant Antarctic Icebergs with Deep Learning ...
<!--!introduction!--> Icebergs account for half of all ice loss from Antarctica. Their melting affects the surrounding ocean properties through the intrusion of cold, fresh meltwater and the release of terrigenious nutrients. This in turn influences the local ocean circulation, sea ice formati...
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GFZ German Research Centre for Geosciences
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ftdatacite:10.57757/iugg23-2663 2023-07-23T04:15:29+02:00 Delineating giant Antarctic Icebergs with Deep Learning ... Braakmann-Folgmann, Anne Shepherd, Andrew Hogg, David Redmond, Ella 2023 https://dx.doi.org/10.57757/iugg23-2663 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019231 unknown GFZ German Research Centre for Geosciences Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Article ConferencePaper Oral 2023 ftdatacite https://doi.org/10.57757/iugg23-2663 2023-07-03T18:58:42Z <!--!introduction!--> Icebergs account for half of all ice loss from Antarctica. Their melting affects the surrounding ocean properties through the intrusion of cold, fresh meltwater and the release of terrigenious nutrients. This in turn influences the local ocean circulation, sea ice formation and biological production. To locate and quantify the fresh water flux from Antarctic icebergs, we need to track them and monitor changes in their area and thickness. While the locations of large icebergs are tracked operationally by manual inspection, delineation of iceberg extent requires detailed analysis – either also manually or through automated segmentation of high resolution satellite imagery. In this study, we propose a U-net approach to automatically segment giant icebergs in nearly 200 Sentinel-1 images. It is the first study to apply a deep learning algorithm to iceberg segmentation. Furthermore, most previous studies to detect icebergs have focused on smaller bergs. In contrast, we aim to segment ... : The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) ... Conference Object Antarc* Antarctic Antarctica Iceberg* Sea ice DataCite Metadata Store (German National Library of Science and Technology) Antarctic |
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DataCite Metadata Store (German National Library of Science and Technology) |
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<!--!introduction!--> Icebergs account for half of all ice loss from Antarctica. Their melting affects the surrounding ocean properties through the intrusion of cold, fresh meltwater and the release of terrigenious nutrients. This in turn influences the local ocean circulation, sea ice formation and biological production. To locate and quantify the fresh water flux from Antarctic icebergs, we need to track them and monitor changes in their area and thickness. While the locations of large icebergs are tracked operationally by manual inspection, delineation of iceberg extent requires detailed analysis – either also manually or through automated segmentation of high resolution satellite imagery. In this study, we propose a U-net approach to automatically segment giant icebergs in nearly 200 Sentinel-1 images. It is the first study to apply a deep learning algorithm to iceberg segmentation. Furthermore, most previous studies to detect icebergs have focused on smaller bergs. In contrast, we aim to segment ... : The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) ... |
format |
Conference Object |
author |
Braakmann-Folgmann, Anne Shepherd, Andrew Hogg, David Redmond, Ella |
spellingShingle |
Braakmann-Folgmann, Anne Shepherd, Andrew Hogg, David Redmond, Ella Delineating giant Antarctic Icebergs with Deep Learning ... |
author_facet |
Braakmann-Folgmann, Anne Shepherd, Andrew Hogg, David Redmond, Ella |
author_sort |
Braakmann-Folgmann, Anne |
title |
Delineating giant Antarctic Icebergs with Deep Learning ... |
title_short |
Delineating giant Antarctic Icebergs with Deep Learning ... |
title_full |
Delineating giant Antarctic Icebergs with Deep Learning ... |
title_fullStr |
Delineating giant Antarctic Icebergs with Deep Learning ... |
title_full_unstemmed |
Delineating giant Antarctic Icebergs with Deep Learning ... |
title_sort |
delineating giant antarctic icebergs with deep learning ... |
publisher |
GFZ German Research Centre for Geosciences |
publishDate |
2023 |
url |
https://dx.doi.org/10.57757/iugg23-2663 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019231 |
geographic |
Antarctic |
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Antarctic |
genre |
Antarc* Antarctic Antarctica Iceberg* Sea ice |
genre_facet |
Antarc* Antarctic Antarctica Iceberg* Sea ice |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.57757/iugg23-2663 |
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1772176332328796160 |