  An essential climate variable to determine the tidewater glacier status is
the location of the calving front position and the separation of seasonal
variability from long-term trends. Previous studies have proposed deep
learning-based methods to semi-automatically delineate the calving fronts of
tidewater glaciers. They used U-Net to segment the ice and non-ice regions and
extracted the calving fronts in a post-processing step. In this work, we show a
method to segment the glacier calving fronts from SAR images in an end-to-end
fashion using Attention U-Net. The main objective is to investigate the
attention mechanism in this application. Adding attention modules to the
state-of-the-art U-Net network lets us analyze the learning process by
extracting its attention maps. We use these maps as a tool to search for proper
hyperparameters and loss functions in order to generate higher qualitative
results. Our proposed attention U-Net performs comparably to the standard U-Net
while providing additional insight into those regions on which the network
learned to focus more. In the best case, the attention U-Net achieves a 1.5%
better Dice score compared to the canonical U-Net with a glacier front line
prediction certainty of up to 237.12 meters.
