GlacierCastAI: Predicting Glacier Retreat from Multi-Modal Satellite Imagery and Climate Signals
ERA5 seasonal climate variables contain predictive information about future glacier retreat beyond what satellite imagery alone provides, yet existing deep learning methods focus on mapping current boundaries rather than forecasting future ones. This paper presents GlacierCastAI, which reframes glacier boundary prediction as a multi-modal spatiotemporal forecasting problem, fusing multi-temporal Landsat imagery with ERA5 reanalysis climate variables and Copernicus DEM terrain features to forecast glacier boundaries across five glaciers spanning four climate regimes. The architecture couples a ResNet50 spatial encoder with a ConvLSTM temporal model and a cross-attention climate fusion modu...
| Authors | Arunkumar Ramachandran |
|---|---|
| Primary category | cs.LG |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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