  Ice thickness estimation is an important aspect of ice sheet studies. In this
work, we use convolutional neural networks with multiple output nodes to
regress and learn the thickness of internal ice layers in Snow Radar images
collected in northwest Greenland. We experiment with some state-of-the-art
networks and find that with the residual connections of ResNet50, we could
achieve a mean absolute error of 1.251 pixels over the test set. Such
regression-based networks can further be improved by embedding domain knowledge
and radar information in the neural network in order to reduce the requirement
of manual annotations.
