Detailed Glacier Area Change Analysis in the European Alps with Deep Learning (Papers Track)

Codrut-Andrei Diaconu (DLR); Jonathan Bamber (Technical University of Munich)

Paper PDF Poster File Recorded Talk NeurIPS 2023 Poster Cite
Earth Observation & Monitoring Computer Vision & Remote Sensing


Glacier retreat is a key indicator of climate change and requires regular updates of the glacier area. Recently, the release of a new inventory for the European Alps showed that glaciers continued to retreat at about 1.3% per year from 2003 to 2015. The outlines were produced by manually correcting the results of a semi-automatic method applied to Sentinel-2 imagery. In this work we develop a fully-automatic pipeline based on Deep Learning to investigate the evolution of the glaciers in the Alps from 2015 to present (2023). After outlier filtering, we provide individual estimates for around 1300 glaciers, representing 87% of the glacierized area. Regionally we estimate an area loss of -1.8% per year, with large variations between glaciers. Code and data are available at

Recorded Talk (direct link)