CryoNet: A Deep Learning Framework for Multi-Modal Debris-Covered Glacier Mapping. A Case Study of the Poiqu Basin, Central Himalaya

2026-05-19

AI Summary

This study addresses the challenge of automatically identifying debris-covered glaciers, whose spectral signatures closely resemble those of surrounding terrain. To overcome this, the authors propose CryoNet, a multimodal deep learning framework that systematically integrates multisource data—including Sentinel-2 optical imagery, digital elevation models (DEM), InSAR coherence and phase, spectral indices, principal component analysis (PCA), tasseled cap transformation, and GLCM texture features—into a ResNet101-based encoder-decoder architecture enhanced with nested skip connections and spatial-channel Squeeze-and-Excitation (scSE) attention mechanisms. Evaluated in the Poiqu River basin, CryoNet achieves an overall intersection-over-union (IoU) of 90.52% and 90.46% for debris-covered glaciers, significantly outperforming state-of-the-art models such as DeepLabV3+, SegFormer, and U-Net, while also demonstrating robust cross-region transferability.

Abstract

Glaciers play a critical role as freshwater reserves and indicators of climate change, yet their automatic delineation, especially for debris-covered glaciers, remains challenging due to spectral similarity with surrounding terrain. This study introduces CryoNet, a deep learning framework that leverages a rich multi-modal dataset combining Sentinel-2 optical imagery, DEM-derived topographic variables, spectral indices, Principal Component Analysis (PCA), InSAR coherence and phase, tasseled-cap features, and GLCM texture to discriminate clean-ice glaciers, debris-covered glaciers, and glacial lakes. CryoNet is an encoder-decoder CNN with nested skip connections and spatial-channel Squeeze-and-Excitation (scSE) attention, built upon a ResNet101 encoder to capture hierarchical contextual and spatial features. The study is conducted in the Poiqu Basin in the central Himalaya, and transferability is evaluated by applying the trained model to the Mont Blanc Massif in the Alps. We additionally analyse the importance of each data layer in improving glacier mapping performance. The proposed model achieves an overall IoU of 90.52%, mean Recall of 98.08%, and mean Precision of 92.26%. For debris-covered glaciers specifically, CryoNet obtains an IoU of 90.46%, a recall of 95.79%, and a precision of 94.21%. Across both per-class and overall metrics, CryoNet surpasses DeepLabV3+, SegFormer, and U-Net, taken as state-of-the-art (SOTA) references, demonstrating its effectiveness for robust glacier mapping in complex high-mountain environments.

Problem

• debris-covered glaciers
• glacier mapping
• spectral similarity
• automatic delineation
• high-mountain environments

Innovation

• multi-modal fusion
• debris-covered glacier mapping
• scSE attention
• deep learning for remote sensing
• transferable glacier delineation