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arXiv · 2501.05281

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

Nora Gourmelon·Konrad Heidler·Erik Loebel·Daniel Cheng·Julian Klink·Anda Dong·Fei Wu·Noah Maul·Moritz Koch·Marcel Dreier·Dakota Pyles·Thorsten Seehaus·Matthias Braun·Andreas Maier·Vincent Christlein
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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.

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Nora Gourmelon, Konrad Heidler, Erik Loebel, Daniel Cheng, Julian Klink, Anda Dong, Fei Wu, Noah Maul, Moritz Koch, Marcel Dreier, Dakota Pyles, Thorsten Seehaus, Matthias Braun, Andreas Maier, Vincent Christlein. 2025-01-09. Comparison Study: Glacier Calving Front Delineation in Synthetic Aperture Radar Images With Deep Learning. https://arxiv.org/abs/2501.05281

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