Automatic eddy detection in Antarctic marginal ice zone using Sentinel-1 SAR data - Sandalyuk, Nikita; Khachatrian, Eduard; Marchuk, Ekaterina (Frontiers in Marine Science, 2025)
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.