  Sea ice is a crucial component of the Earth's climate system and is highly
sensitive to changes in temperature and atmospheric conditions. Accurate and
timely measurement of sea ice parameters is important for understanding and
predicting the impacts of climate change. Nevertheless, the amount of satellite
data acquired over ice areas is huge, making the subjective measurements
ineffective. Therefore, automated algorithms must be used in order to fully
exploit the continuous data feeds coming from satellites. In this paper, we
present a novel approach for sea ice segmentation based on SAR satellite
imagery using hybrid convolutional transformer (ConvTr) networks. We show that
our approach outperforms classical convolutional networks, while being
considerably more efficient than pure transformer models. ConvTr obtained a
mean intersection over union (mIoU) of 63.68% on the AI4Arctic data set,
assuming an inference time of 120ms for a 400 x 400 squared km product.
