Evaluating Deep-Learning Models for Debris-Covered Glacier Mapping

Author(s)

Zhiyuan Xie, University of Dayton
Vijayan K. Asari, University of DaytonFollow
Umesh K. Haritashya, University of DaytonFollow

Document Type

Article

Publication Date

12-2021

Publication Source

Applied Computing and Geosciences

Abstract

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 debriscovered 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.

ISBN/ISSN

2590-1974

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1016/j.acags.2021.100071

Publisher

Elsevier

Volume

12

Peer Reviewed

yes

eCommons Citation

Xie, Zhiyuan; Asari, Vijayan K.; and Haritashya, Umesh K., "Evaluating Deep-Learning Models for Debris-Covered Glacier Mapping" (2021). Electrical and Computer Engineering Faculty Publications. 464.
https://ecommons.udayton.edu/ece_fac_pub/464