Automatic eddy detection in Antarctic marginal ice zone using Sentinel-1 SAR data
Studying oceanic eddies in the Antarctic marginal ice zone (MIZ) is essential due to their unique characteristics and their significant influence on polar climate systems. However, the automated detection of such features remains largely underexplored in general. Moreover, even manual eddy detection...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1648021/full |
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| author | Nikita Sandalyuk Eduard Khachatrian Ekaterina Marchuk |
| author_facet | Nikita Sandalyuk Eduard Khachatrian Ekaterina Marchuk |
| author_sort | Nikita Sandalyuk |
| collection | DOAJ |
| description | Studying oceanic eddies in the Antarctic marginal ice zone (MIZ) is essential due to their unique characteristics and their significant influence on polar climate systems. However, the automated detection of such features remains largely underexplored in general. Moreover, even manual eddy detection has been practically neglected within the Antarctic MIZ specifically. This work presents the first study on the implementation of the machine learning approach for automatic eddy identification in the Antarctic MIZ. We investigate the potential of YOLOv11, a state-of-theart deep learning model, to detect and classify Antarctic eddies using high-resolution synthetic aperture radar imagery. By fine-tuning YOLOv11 on a specialized dataset representing the dynamic Antarctic MIZ, we achieved robust detection of submesoscale and mesoscale eddies. Special significance was placed on distinguishing between cyclonic and anticyclonic eddies, providing essential insights for compiling statistical datasets. Moreover, YOLOv11 architecture was evaluated through a variety of quantitative metrics and visual inspection. The integration of SAHI module with YOLOv11 demonstrated its capability to improve detection of small eddies and increased the mAP0.5 -0.95 by 50% in comparison with the baseline YOLOv11 model.Experimental results highlight the model’s capability to reliably identify eddies across diverse scales and environmental conditions. Overall, this study addresses a significant gap in Antarctic eddy research and sets the stage for advancing automated oceanographic studies in polar regions. |
| format | Article |
| id | doaj-art-e5e35f83f6ec45cf935d5c40b2b8fdf3 |
| institution | Kabale University |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-e5e35f83f6ec45cf935d5c40b2b8fdf32025-08-21T09:12:22ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-08-011210.3389/fmars.2025.16480211648021Automatic eddy detection in Antarctic marginal ice zone using Sentinel-1 SAR dataNikita Sandalyuk0Eduard Khachatrian1Ekaterina Marchuk2Laboratory of Arctic Oceanography, The Moscow Institute of Physics and Technology, Moscow, RussiaDepartment of Physics and Technology, UiT The Arctic University of Norway, Tromsø, NorwayAtmosphere-Ocean Interaction Laboratory, Obukhov Institute of Atmospheric Physics, Moscow, RussiaStudying oceanic eddies in the Antarctic marginal ice zone (MIZ) is essential due to their unique characteristics and their significant influence on polar climate systems. However, the automated detection of such features remains largely underexplored in general. Moreover, even manual eddy detection has been practically neglected within the Antarctic MIZ specifically. This work presents the first study on the implementation of the machine learning approach for automatic eddy identification in the Antarctic MIZ. We investigate the potential of YOLOv11, a state-of-theart deep learning model, to detect and classify Antarctic eddies using high-resolution synthetic aperture radar imagery. By fine-tuning YOLOv11 on a specialized dataset representing the dynamic Antarctic MIZ, we achieved robust detection of submesoscale and mesoscale eddies. Special significance was placed on distinguishing between cyclonic and anticyclonic eddies, providing essential insights for compiling statistical datasets. Moreover, YOLOv11 architecture was evaluated through a variety of quantitative metrics and visual inspection. The integration of SAHI module with YOLOv11 demonstrated its capability to improve detection of small eddies and increased the mAP0.5 -0.95 by 50% in comparison with the baseline YOLOv11 model.Experimental results highlight the model’s capability to reliably identify eddies across diverse scales and environmental conditions. Overall, this study addresses a significant gap in Antarctic eddy research and sets the stage for advancing automated oceanographic studies in polar regions.https://www.frontiersin.org/articles/10.3389/fmars.2025.1648021/fullmesoscale eddiessubmesoscale eddieseddy detectionmarginal ice zonedeep learningYOLOv11 |
| spellingShingle | Nikita Sandalyuk Eduard Khachatrian Ekaterina Marchuk Automatic eddy detection in Antarctic marginal ice zone using Sentinel-1 SAR data Frontiers in Marine Science mesoscale eddies submesoscale eddies eddy detection marginal ice zone deep learning YOLOv11 |
| title | Automatic eddy detection in Antarctic marginal ice zone using Sentinel-1 SAR data |
| title_full | Automatic eddy detection in Antarctic marginal ice zone using Sentinel-1 SAR data |
| title_fullStr | Automatic eddy detection in Antarctic marginal ice zone using Sentinel-1 SAR data |
| title_full_unstemmed | Automatic eddy detection in Antarctic marginal ice zone using Sentinel-1 SAR data |
| title_short | Automatic eddy detection in Antarctic marginal ice zone using Sentinel-1 SAR data |
| title_sort | automatic eddy detection in antarctic marginal ice zone using sentinel 1 sar data |
| topic | mesoscale eddies submesoscale eddies eddy detection marginal ice zone deep learning YOLOv11 |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2025.1648021/full |
| work_keys_str_mv | AT nikitasandalyuk automaticeddydetectioninantarcticmarginalicezoneusingsentinel1sardata AT eduardkhachatrian automaticeddydetectioninantarcticmarginalicezoneusingsentinel1sardata AT ekaterinamarchuk automaticeddydetectioninantarcticmarginalicezoneusingsentinel1sardata |