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Application of Deep Learning in Glacier Boundary Extraction: A Case Study of the Tomur Peak Region, Tianshan, Xinjiang

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  • Yan Zhang

    (College of Information Science and Technology, Shihezi University, Shihezi 832002, China
    Geospatial Information Engineering Research Center, Xinjiang Production and Construction Corps, Shihezi 832002, China)

  • Feng Han

    (College of Information Science and Technology, Shihezi University, Shihezi 832002, China
    Geospatial Information Engineering Research Center, Xinjiang Production and Construction Corps, Shihezi 832002, China
    Industrial Technology Research Institute, Xinjiang Production and Construction Corps, Shihezi 832002, China)

  • Mingfeng Zhou

    (College of Information Science and Technology, Shihezi University, Shihezi 832002, China
    Geospatial Information Engineering Research Center, Xinjiang Production and Construction Corps, Shihezi 832002, China)

  • Yichen Hou

    (College of Information Science and Technology, Shihezi University, Shihezi 832002, China
    Geospatial Information Engineering Research Center, Xinjiang Production and Construction Corps, Shihezi 832002, China)

  • Song Wang

    (College of Information Science and Technology, Shihezi University, Shihezi 832002, China
    Geospatial Information Engineering Research Center, Xinjiang Production and Construction Corps, Shihezi 832002, China)

Abstract

Glaciers are one of the most important water resources in the arid regions of Xinjiang, making it crucial to accurately monitor glacier changes for the region’s sustainable development. However, due to their typical distribution in remote, high-altitude areas, large-scale and long-term field observations are often constrained by the high costs of manpower, resources, and finances. Globally, fewer than 40 glaciers have been monitored for more than 20 years, and, in China, only Glacier No. 1 at the headwaters of the Urumqi River has monitoring records exceeding 50 years. To address these challenges, this study analyzed glacier changes in the Tomur Peak region of the Tianshan Mountains over the past 35 years using Landsat satellite imagery. Through experiments with deep learning models, the results show that the 3-4-5 band combination performed best for glacier boundary extraction. The DeepLabV3+ model, with MobileNetV2 as the backbone, achieved an overall accuracy of 90.44%, a recall rate of 82.75%, and a mean Intersection over Union (IoU) that was 1.6 to 5.94 percentage points higher than other models. Based on these findings, the study further analyzed glacier changes in the Tomur Peak region, revealing an average annual glacier reduction rate of 0.18% and a retreat rate of 6.97 km 2 ·a −1 over the past 35 years. This research provides a more precise and comprehensive scientific reference for understanding glacier changes in arid regions, with significant implications for enhancing our understanding of the impacts of climate change on glaciers, optimizing water resource management, and promoting regional sustainable development.

Suggested Citation

  • Yan Zhang & Feng Han & Mingfeng Zhou & Yichen Hou & Song Wang, 2025. "Application of Deep Learning in Glacier Boundary Extraction: A Case Study of the Tomur Peak Region, Tianshan, Xinjiang," Sustainability, MDPI, vol. 17(8), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3678-:d:1637958
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    References listed on IDEAS

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    1. Thomas Jacob & John Wahr & W. Tad Pfeffer & Sean Swenson, 2012. "Recent contributions of glaciers and ice caps to sea level rise," Nature, Nature, vol. 482(7386), pages 514-518, February.
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