  Digital Surface Model generation from satellite imagery is a difficult task
that has been largely overlooked by the deep learning community. Stereo
reconstruction techniques developed for terrestrial systems including self
driving cars do not translate well to satellite imagery where image pairs vary
considerably. In this work we present neural network tailored for Digital
Surface Model generation, a ground truthing and training scheme which maximizes
available hardware, and we present a comparison to existing methods. The
resulting models are smooth, preserve boundaries, and enable further
processing. This represents one of the first attempts at leveraging deep
learning in this domain.
