<ul><li>Accurate segmentation of sea ice types is crucial for mapping and operational forecasting in ice-covered waters.</li><li>Deep learning methods often require extensive labeled datasets, which are time-consuming to create.</li><li>This study evaluates ten remote sensing foundation models (FMs) for sea ice type segmentation using Sentinel-1 SAR imagery.</li><li>Among the selected models, Prithvi-600M outperforms the baseline models, while CROMA achieves a similar performance in F1-score.</li></ul>

Assessing Foundation Models for Sea Ice Type Segmentation in Sentinel-1 SAR Imagery

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