The Cryosphere
The Cryosphere
TC
Articles | Volume 17, issue 11
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

Viewed

Total article views: 1,832 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,291 462 79 1,832 63 56
  • HTML: 1,291
  • PDF: 462
  • XML: 79
  • Total: 1,832
  • BibTeX: 63
  • EndNote: 56
Views and downloads (calculated since 28 Feb 2023)
Cumulative views and downloads (calculated since 28 Feb 2023)

Viewed (geographical distribution)

Total article views: 1,832 (including HTML, PDF, and XML) Thereof 1,726 with geography defined and 106 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

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.
Read more