Globally scalable glacier mapping by deep learning matches expert delineation accuracy.

ABSTRACT: 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

SUBMITTER: Maslov KA 

PROVIDER: S-EPMC11695715 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature