Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto
Antarctica plays a key role in the hydrological cycle of the Earth's climate system, with an ice sheet that is the largest block of ice that reserves Earth's 90% of total ice volume and 70% of fresh water. Furthermore, the sustainability of the region is an important concern due to the cha...
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ftaperta:oai:aperta.ulakbim.gov.tr:265146 2024-06-23T07:47:55+00:00 Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto Selbesoglu, Mahmut Oguz Bakirman, Tolga Vassilev, Oleg Ozsoy, Burcu 2023-01-01 https://aperta.ulakbim.gov.tr/record/265146 unknown https://aperta.ulakbim.gov.tr/record/265146 oai:aperta.ulakbim.gov.tr:265146 info:eu-repo/semantics/openAccess http://www.opendefinition.org/licenses/cc-by DRONES 7(2) 11 info:eu-repo/semantics/article publication-article 2023 ftaperta 2024-06-12T23:31:53Z Antarctica plays a key role in the hydrological cycle of the Earth's climate system, with an ice sheet that is the largest block of ice that reserves Earth's 90% of total ice volume and 70% of fresh water. Furthermore, the sustainability of the region is an important concern due to the challenges posed by melting glaciers that preserve the Earth's heat balance by interacting with the Southern Ocean. Therefore, the monitoring of glaciers based on advanced deep learning approaches offers vital outcomes that are of great importance in revealing the effects of global warming. In this study, recent deep learning approaches were investigated in terms of their accuracy for the segmentation of glacier landforms in the Antarctic Peninsula. For this purpose, high-resolution orthophotos were generated based on UAV photogrammetry within the Sixth Turkish Antarctic Expedition in 2022. Segformer, DeepLabv3+ and K-Net deep learning methods were comparatively analyzed in terms of their accuracy. The results showed that K-Net provided efficient results with 99.62% accuracy, 99.58% intersection over union, 99.82% precision, 99.76% recall and 99.79% F1-score. Visual inspections also revealed that K-Net was able to preserve the fine details around the edges of the glaciers. Our proposed deep-learning-based method provides an accurate and sustainable solution for automatic glacier segmentation and monitoring. Article in Journal/Newspaper Antarc* Antarctic Antarctic Peninsula Antarctica Horseshoe Island Ice Sheet Southern Ocean Aperta - Türkiye Açık Arşiv Antarctic Antarctic Peninsula Horseshoe Island ENVELOPE(-67.189,-67.189,-67.836,-67.836) Southern Ocean The Antarctic |
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Aperta - Türkiye Açık Arşiv |
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ftaperta |
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description |
Antarctica plays a key role in the hydrological cycle of the Earth's climate system, with an ice sheet that is the largest block of ice that reserves Earth's 90% of total ice volume and 70% of fresh water. Furthermore, the sustainability of the region is an important concern due to the challenges posed by melting glaciers that preserve the Earth's heat balance by interacting with the Southern Ocean. Therefore, the monitoring of glaciers based on advanced deep learning approaches offers vital outcomes that are of great importance in revealing the effects of global warming. In this study, recent deep learning approaches were investigated in terms of their accuracy for the segmentation of glacier landforms in the Antarctic Peninsula. For this purpose, high-resolution orthophotos were generated based on UAV photogrammetry within the Sixth Turkish Antarctic Expedition in 2022. Segformer, DeepLabv3+ and K-Net deep learning methods were comparatively analyzed in terms of their accuracy. The results showed that K-Net provided efficient results with 99.62% accuracy, 99.58% intersection over union, 99.82% precision, 99.76% recall and 99.79% F1-score. Visual inspections also revealed that K-Net was able to preserve the fine details around the edges of the glaciers. Our proposed deep-learning-based method provides an accurate and sustainable solution for automatic glacier segmentation and monitoring. |
format |
Article in Journal/Newspaper |
author |
Selbesoglu, Mahmut Oguz Bakirman, Tolga Vassilev, Oleg Ozsoy, Burcu |
spellingShingle |
Selbesoglu, Mahmut Oguz Bakirman, Tolga Vassilev, Oleg Ozsoy, Burcu Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto |
author_facet |
Selbesoglu, Mahmut Oguz Bakirman, Tolga Vassilev, Oleg Ozsoy, Burcu |
author_sort |
Selbesoglu, Mahmut Oguz |
title |
Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto |
title_short |
Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto |
title_full |
Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto |
title_fullStr |
Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto |
title_full_unstemmed |
Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto |
title_sort |
mapping of glaciers on horseshoe island, antarctic peninsula, with deep learning based on high-resolution orthophoto |
publishDate |
2023 |
url |
https://aperta.ulakbim.gov.tr/record/265146 |
long_lat |
ENVELOPE(-67.189,-67.189,-67.836,-67.836) |
geographic |
Antarctic Antarctic Peninsula Horseshoe Island Southern Ocean The Antarctic |
geographic_facet |
Antarctic Antarctic Peninsula Horseshoe Island Southern Ocean The Antarctic |
genre |
Antarc* Antarctic Antarctic Peninsula Antarctica Horseshoe Island Ice Sheet Southern Ocean |
genre_facet |
Antarc* Antarctic Antarctic Peninsula Antarctica Horseshoe Island Ice Sheet Southern Ocean |
op_source |
DRONES 7(2) 11 |
op_relation |
https://aperta.ulakbim.gov.tr/record/265146 oai:aperta.ulakbim.gov.tr:265146 |
op_rights |
info:eu-repo/semantics/openAccess http://www.opendefinition.org/licenses/cc-by |
_version_ |
1802638170602340352 |