  Understanding the structure of Earth's polar ice sheets is important for
modeling how global warming will impact polar ice and, in turn, the Earth's
climate. Ground-penetrating radar is able to collect observations of the
internal structure of snow and ice, but the process of manually labeling these
observations is slow and laborious. Recent work has developed automatic
techniques for finding the boundaries between the ice and the bedrock, but
finding internal layers - the subtle boundaries that indicate where one year's
ice accumulation ended and the next began - is much more challenging because
the number of layers varies and the boundaries often merge and split. In this
paper, we propose a novel deep neural network for solving a general class of
tiered segmentation problems. We then apply it to detecting internal layers in
polar ice, evaluating on a large-scale dataset of polar ice radar data with
human-labeled annotations as ground truth.
