  Up-to-date sea ice charts are crucial for safer navigation in ice-infested
waters. Recently, Convolutional Neural Network (CNN) models show the potential
to accelerate the generation of ice maps for large regions. However, results
from CNN models still need to undergo scrutiny as higher metrics performance
not always translate to adequate outputs. Sea ice type classes are imbalanced,
requiring special treatment during training. We evaluate how three different
loss functions, some developed for imbalanced class problems, affect the
performance of CNN models trained to predict the dominant ice type in
Sentinel-1 images. Despite the fact that Dice and Focal loss produce higher
metrics, results from cross-entropy seem generally more physically consistent.
