CryoNet: A Deep Learning Framework for Multi-Modal Debris-Covered Glacier Mapping. A Case Study of the Poiqu Basin, Central Himalaya
arXiv:2605.21527v1 Announce Type: cross 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-ic
The increasing availability of multi-modal satellite data and advancements in deep learning algorithms are enabling more sophisticated environmental monitoring solutions.
Accurate mapping of debris-covered glaciers is crucial for understanding climate change impacts, water resource management, and predicting glacial hazards.
This deep learning framework offers a more precise and automated method for glacier delineation, improving the accuracy of climate models and hydrological forecasts.
- · Climate scientists
- · Hydrologists
- · Remote sensing companies
- · Environmental monitoring agencies
- · Manual mapping methods
- · Less sophisticated glacier modeling techniques
Improved understanding of glacial melt rates and their contribution to sea-level rise.
More reliable water resource management strategies for regions dependent on glacial meltwater.
Enhanced early warning systems for glacial lake outburst floods, potentially saving lives and infrastructure.
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Read at arXiv cs.LG