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

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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 %.