Articles | Volume 17, issue 11
Research article
24 Nov 2023
Research article |  | 24 Nov 2023

Out-of-the-box calving-front detection method using deep learning

Oskar Herrmann, Nora Gourmelon, Thorsten Seehaus, Andreas Maier, Johannes J. Fürst, Matthias H. Braun, and Vincent Christlein

Data sets

Out-of-the-box calving front detection method using deep learning Oskar Herrmann

CaFFe (CAlving Fronts and where to Find thEm: a benchmark dataset and methodology for automatic glacier calving front extraction from sar imagery) Nora Gourmelon, Thorsten Seehaus, Matthias Holger Braun, Andreas Maier, and Vincent Christlein

Model code and software

Pretrained_nnUNet_calvingfront_detection Oskar Herrmann

nnU-Ne Fabian Isensee

nnUNet_calvingfront_detection Oskar Herrmann and Nora Gourmelon

nnUNet_calvingfront_detection Oskar Herrmann


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Short summary
Delineating calving fronts of marine-terminating glaciers in satellite images is a labour-intensive task. We propose a method based on deep learning that automates this task. We choose a deep learning framework that adapts to any given dataset without needing deep learning expertise. The method is evaluated on a benchmark dataset for calving-front detection and glacier zone segmentation. The framework can beat the benchmark baseline without major modifications.