Articles | Volume 17, issue 11
https://doi.org/10.5194/tc-17-4675-2023
https://doi.org/10.5194/tc-17-4675-2023
Research article
 | 
09 Nov 2023
Research article |  | 09 Nov 2023

Mapping the extent of giant Antarctic icebergs with deep learning

Anne Braakmann-Folgmann, Andrew Shepherd, David Hogg, and Ella Redmond

Viewed

Total article views: 2,359 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,799 490 70 2,359 42 42
  • HTML: 1,799
  • PDF: 490
  • XML: 70
  • Total: 2,359
  • BibTeX: 42
  • EndNote: 42
Views and downloads (calculated since 11 May 2023)
Cumulative views and downloads (calculated since 11 May 2023)

Viewed (geographical distribution)

Total article views: 2,359 (including HTML, PDF, and XML) Thereof 2,326 with geography defined and 33 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (final revised paper)

Latest update: 12 May 2024
Download
Short summary
In this study, we propose a deep neural network to map the extent of giant Antarctic icebergs in Sentinel-1 images automatically. While each manual delineation requires several minutes, our U-net takes less than 0.01 s. In terms of accuracy, we find that U-net outperforms two standard segmentation techniques (Otsu, k-means) in most metrics and is more robust to challenging scenes with sea ice, coast and other icebergs. The absolute median deviation in iceberg area across 191 images is 4.1 %.