  Ground-penetrating radar on planes and satellites now makes it practical to
collect 3D observations of the subsurface structure of the polar ice sheets,
providing crucial data for understanding and tracking global climate change.
But converting these noisy readings into useful observations is generally done
by hand, which is impractical at a continental scale. In this paper, we propose
a computer vision-based technique for extracting 3D ice-bottom surfaces by
viewing the task as an inference problem on a probabilistic graphical model. We
first generate a seed surface subject to a set of constraints, and then
incorporate additional sources of evidence to refine it via discrete energy
minimization. We evaluate the performance of the tracking algorithm on 7
topographic sequences (each with over 3000 radar images) collected from the
Canadian Arctic Archipelago with respect to human-labeled ground truth.
