  Accurate global glacier mapping is critical for understanding climate change
impacts. Despite its importance, automated glacier mapping at a global scale
remains largely unexplored. Here we address this gap and propose
Glacier-VisionTransformer-U-Net (GlaViTU), a convolutional-transformer deep
learning model, and five strategies for multitemporal global-scale glacier
mapping using open satellite imagery. Assessing the spatial, temporal and
cross-sensor generalisation shows that our best strategy achieves intersection
over union >0.85 on previously unobserved images in most cases, which drops to
>0.75 for debris-rich areas such as High-Mountain Asia and increases to >0.90
for regions dominated by clean ice. A comparative validation against human
expert uncertainties in terms of area and distance deviations underscores
GlaViTU performance, approaching or matching expert-level delineation. Adding
synthetic aperture radar data, namely, backscatter and interferometric
coherence, increases the accuracy in all regions where available. The
calibrated confidence for glacier extents is reported making the predictions
more reliable and interpretable. We also release a benchmark dataset that
covers 9% of glaciers worldwide. Our results support efforts towards automated
multitemporal and global glacier mapping.
