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 challenges po...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/7/2/72 |
| _version_ | 1797621437382524928 |
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| author | Mahmut Oğuz Selbesoğlu Tolga Bakirman Oleg Vassilev Burcu Ozsoy |
| author_facet | Mahmut Oğuz Selbesoğlu Tolga Bakirman Oleg Vassilev Burcu Ozsoy |
| author_sort | Mahmut Oğuz Selbesoğlu |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T08:55:56Z |
| format | Article |
| id | doaj.art-7192db5dc04c47fbbb37995ebf117382 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T08:55:56Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-7192db5dc04c47fbbb37995ebf1173822023-11-16T20:06:09ZengMDPI AGDrones2504-446X2023-01-01727210.3390/drones7020072Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution OrthophotoMahmut Oğuz Selbesoğlu0Tolga Bakirman1Oleg Vassilev2Burcu Ozsoy3Department of Geomatics, Istanbul Technical University, 34469 Istanbul, TürkiyeDepartment of Geomatics, Yildiz Technical University, 34220 Istanbul, TürkiyeBulgarian Antarctic Institute, 15 Tsar Osvoboditel Boulevard, 1504 Sofia, BulgariaPolar Research Institute, TÜBİTAK Marmara Research Center, 41470 Kocaeli, TürkiyeAntarctica 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.https://www.mdpi.com/2504-446X/7/2/72Antarcticadeep learningHorseshoeglacierorthophoto |
| spellingShingle | Mahmut Oğuz Selbesoğlu Tolga Bakirman Oleg Vassilev Burcu Ozsoy Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto Drones Antarctica deep learning Horseshoe glacier orthophoto |
| title | 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_short | 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 |
| topic | Antarctica deep learning Horseshoe glacier orthophoto |
| url | https://www.mdpi.com/2504-446X/7/2/72 |
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