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

IEEE Transactions on Pattern Analysis and Machine Intelligence 2026 Open access

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Authors: Nora Gourmelon, Konrad Heidler, Erik Loebel, Daniel Cheng, Julian Klink, Anda Dong, Fei Wu, Noah Maul, Moritz Koch, Marcel Dreier, D. R. Pyles, Thorsten Seehaus
+3 more Matthias H. Braun, Andreas Maier, Vincent Christlein
DOI: 10.1109/tpami.2026.3685700
Cited by: 0

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.

Fields of Study & Subjects

Cryospheric studies and observations Winter Sports Injuries and Performance Arctic and Antarctic ice dynamics

Publication details

Journal / source
IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume
48
Issue
9
Pages
11224–11230
Publication date
20 April 2026
Language
EN