Glacier Area Change Assessment over 2015-2023 in the European Alps with Deep Learning

Glacier retreat is a key indicator of climate change and requires regular updates of the glacier area. The most recent inventory for the European Alps, released in 2020, showed that  glaciers retreated approximately 1.3% per year from 2003 to 2015. This ongoing retreat underscores the urgent need for accurate and efficient monitoring techniques.

Recent advancements in Deep Learning have led to significant progress in the development of fully automated glacier mapping techniques. In this work, we use DL4GAM, a multi-modal Deep Learning-based framework for Glacier Area Monitoring, to assess the change in glacier area in the European Alps over 2015-2023. The main data modality used for training is based on Sentinel-2 imagery, combined with additional features derived from a Digital Elevation Model, along with a surface elevation change map, which is particularly useful for debris-covered glaciers. The framework provides an area (change) estimate independently for each glacier, with uncertainties quantified using an ensemble of models. Region-wide, we estimate a retreat of -1.90 ± 0.71%, which is greater than the rate observed during the previous decade. Our estimates also present a significant inter-glacier variability which we analyze with respect to various topographical parameters such as slope, aspect, or elevation.

Several challenges persist, including model limitations, data availability issues, and the impact of debris, cloud cover, and seasonal snow. We discuss these challenges, the design choices made to address them, and the remaining open issues.