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

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

doi:https://doi.org/10.5194/tc-17-4957-2023

Articles | Volume 17, issue 11

https://doi.org/10.5194/tc-17-4957-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-17-4957-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

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 https://doi.org/10.5281/zenodo.8379954

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 https://doi.org/10.1594/PANGAEA.940950

Model code and software

Pretrained_nnUNet_calvingfront_detection Oskar Herrmann https://doi.org/10.5281/zenodo.7837300

nnU-Ne Fabian Isensee https://github.com/MIC-DKFZ/nnUNet/tree/nnunetv1

nnUNet_calvingfront_detection Oskar Herrmann and Nora Gourmelon https://doi.org/10.5281/zenodo.10168770

nnUNet_calvingfront_detection Oskar Herrmann https://doi.org/10.5281/zenodo.10169965

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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.