Articles | Volume 17, issue 11
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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Cited articles

Barbat, M. M., Wesche, C., Werhli, A. V., and Mata, M. M.: An adaptive machine learning approach to improve automatic iceberg detection from SAR images, ISPRS J. Photogramm., 156, 247–259,, 2019a. 
Barbat, M. M., Rackow, T., Hellmer, H. H., Wesche, C., and Mata, M. M.: Three Years of Near-Coastal Antarctic Iceberg Distribution From a Machine Learning Approach Applied to SAR Imagery, J. Geophys. Res.-Oceans, 124, 6658–6672,, 2019b. 
Barbat, M. M., Rackow, T., Wesche, C., Hellmer, H. H., and Mata, M. M.: Automated iceberg tracking with a machine learning approach applied to SAR imagery: A Weddell sea case study, ISPRS J. Photogramm., 172, 189–206,, 2021. 
Baumhoer, C. A., Dietz, A. J., Kneisel, C., and Kuenzer, C.: Au- 55 tomated extraction of antarctic glacier and ice shelf fronts from Sentinel-1 imagery using deep learning, Remote Sens., 11, 1–22,, 2019. 
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 %.