Articles | Volume 19, issue 11
https://doi.org/10.5194/tc-19-6043-2025
https://doi.org/10.5194/tc-19-6043-2025
Research article
 | 
21 Nov 2025
Research article |  | 21 Nov 2025

Automatic detection of Arctic polynyas using hybrid supervised-unsupervised deep learning

Céline Heuzé and Carmen Hau Man Wong

Viewed

Total article views: 5,242 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,375 665 202 5,242 166 212
  • HTML: 4,375
  • PDF: 665
  • XML: 202
  • Total: 5,242
  • BibTeX: 166
  • EndNote: 212
Views and downloads (calculated since 23 Jun 2025)
Cumulative views and downloads (calculated since 23 Jun 2025)

Viewed (geographical distribution)

Total article views: 5,242 (including HTML, PDF, and XML) Thereof 5,066 with geography defined and 176 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 19 Oct 2026
Download
Short summary
Polynyas are areas with no- or thin-ice within the ice pack. They play a crucial role for the Earth system, yet their monitoring in the Arctic is challenging because polynya detection is non-trivial. We here demonstrate that polynyas can successfully be detected with a novel, machine-learning based method. In fact, we argue that they are better detected than with traditional methods, which seem to fail as sea ice decreases because of climate change.
Share