Comparison Study: Glacier Calving Front Delineation in Synthetic Aperture Radar Images With Deep Learning

arXiv:2501.05281v2 Announce Type: replace-cross Abstract: Continuous monitoring of glacier calving fronts is essential for sea level rise projections. This study benchmarks Deep Learning systems for front delineation in Synthetic Aperture Radar imagery. While Deep Learning systems exhibit errors up to 221 m, human annotators deviate by only 38 m, underscoring the need for further research.