CryoNet, a ResNet101-based encoder-decoder CNN with scSE attention, reports 90.52% overall IoU and 90.46% IoU on debris-covered glaciers in the Poiqu Basin using multi-modal Sentinel-2 and auxiliary data, outperforming DeepLabV3+, SegFormer, and U-Net while transferring to the Mont Blanc Massif.
Challenges and recommendations in mapping of glacier parameters from space: results of the 2008 Global Land Ice Measurements from Space (GLIMS) workshop, Boulder, Colorado, USA,
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CryoNet: A Deep Learning Framework for Multi-Modal Debris-Covered Glacier Mapping. A Case Study of the Poiqu Basin, Central Himalaya
CryoNet, a ResNet101-based encoder-decoder CNN with scSE attention, reports 90.52% overall IoU and 90.46% IoU on debris-covered glaciers in the Poiqu Basin using multi-modal Sentinel-2 and auxiliary data, outperforming DeepLabV3+, SegFormer, and U-Net while transferring to the Mont Blanc Massif.