Mapping of Glaciers on Horseshoe Island, Antarctic Peninsula, with Deep Learning Based on High-Resolution Orthophoto

Mahmut Oğuz Selbesoğlu*, Tolga Bakirman, Oleg Vassilev, Burcu Ozsoy

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Makalebilirkişi

7 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Makale numarası72
DergiDrones
Hacim7
Basın numarası2
DOI'lar
Yayın durumuYayınlandı - Şub 2023

Bibliyografik not

Publisher Copyright:
© 2023 by the authors.

Finansman

This study was funded by the Scientific and Technological Research Council of Turkey (TÜBİTAK), 1071 program, project no: 121N033. The GNSS measurements used in this study were obtained with the support of the TÜBİTAK project under the 1001 program, project no: 118Y322.

FinansörlerFinansör numarası
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu121N033, 118Y322

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