  Fully supervised deep learning approaches have demonstrated impressive
accuracy in sea ice classification, but their dependence on high-resolution
labels presents a significant challenge due to the difficulty of obtaining such
data. In response, our weakly supervised learning method provides a compelling
alternative by utilizing lower-resolution regional labels from expert-annotated
ice charts. This approach achieves exceptional pixel-level classification
performance by introducing regional loss representations during training to
measure the disparity between predicted and ice chart-derived sea ice type
distributions. Leveraging the AI4Arctic Sea Ice Challenge Dataset, our method
outperforms the fully supervised U-Net benchmark, the top solution of the
AutoIce challenge, in both mapping resolution and class-wise accuracy, marking
a significant advancement in automated operational sea ice mapping.
