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Evaluating deep-learning models for debris-covered glacier mapping

In recent decades, mountain glaciers have experienced the impact of climate change in the form of accelerated glacier retreat and other glacier-related hazards such as mass wasting and glacier lake outburst floods. Since there are wide-ranging societal consequences of glacier retreat and hazards, monitoring these glaciers as accurately and repeatedly as possible is important. However, the accurate glacier boundary, especially the debris-covered glacier (DCG) boundary, which is one of the primary inputs in many glacier analyses, remains a challenge even after many years of research using conventional remote sensing methods or machine-learning methods. The GlacierNet, a deep-learning-based approach, utilized the convolutional neural network (CNN) segmentation model to delineate DCG at a high level of accuracy. In this study, the performance of GlacierNet's CNN is compared with several advanced CNN segmentation models, including Mobile-UNet, Res-UNet, FCDenseNet, R2UNet, and DeepLabV3+, to identify the most salient features that could improve the DCG segmentation accuracy. The experimental evaluation shows the highest intersection over union (IOU) of 0.8623 for the DeepLabV3+ and, therefore, is recommended for the regional and large-scale DCG mapping. Moreover, GlacierNet's CNN with the second-highest IOU of 0.8599 is a suitable and light structure for regional DCG mapping.

Availability
121551.136Perpustakaan BIG (Eksternal Harddisk)Available
Detail Information
Series Title
Applied Computing and Geoscience - Open Access
Call Number
551.136
Publisher
Amsterdam : Elsevier.,
Collation
17 hlm PDF, 31.040 KB
Language
Inggris
ISBN/ISSN
2590-1974
Classification
551.136
Content Type
text
Media Type
-
Carrier Type
-
Edition
Vol.12, December 2021
Subject(s)
Deep-learning
Image segmentation
Convolutional neural network
Satellite imagery
Glacier mapping
Specific Detail Info
-
Statement of Responsibility
Other version/related

No other version available

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  • Evaluating deep-learning models for debris-covered glacier mapping
    In recent decades, mountain glaciers have experienced the impact of climate change in the form of accelerated glacier retreat and other glacier-related hazards such as mass wasting and glacier lake outburst floods. Since there are wide-ranging societal consequences of glacier retreat and hazards, monitoring these glaciers as accurately and repeatedly as possible is important. However, the accurate glacier boundary, especially the debris-covered glacier (DCG) boundary, which is one of the primary inputs in many glacier analyses, remains a challenge even after many years of research using conventional remote sensing methods or machine-learning methods. The GlacierNet, a deep-learning-based approach, utilized the convolutional neural network (CNN) segmentation model to delineate DCG at a high level of accuracy. In this study, the performance of GlacierNet's CNN is compared with several advanced CNN segmentation models, including Mobile-UNet, Res-UNet, FCDenseNet, R2UNet, and DeepLabV3+, to identify the most salient features that could improve the DCG segmentation accuracy. The experimental evaluation shows the highest intersection over union (IOU) of 0.8623 for the DeepLabV3+ and, therefore, is recommended for the regional and large-scale DCG mapping. Moreover, GlacierNet's CNN with the second-highest IOU of 0.8599 is a suitable and light structure for regional DCG mapping.
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