Towards Global Glacier Mapping with Deep Learning and Open Earth
  Observation Data
Konstantin A. Maslov Claudio Persello Thomas Schellenberger Alfred Stein
Accurate global glacier mapping is critical for understanding climate change
impacts. It is challenged by glacier diversity, difficult-to-classify debris
and big data processing. Here we 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.
Additionally, 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.