  Though machine learning has achieved notable success in modeling sequential
and spatial data for speech recognition and in computer vision, applications to
remote sensing and climate science problems are seldom considered. In this
paper, we demonstrate techniques from unsupervised learning of future video
frame prediction, to increase the accuracy of ice flow tracking in
multi-spectral satellite images. As the volume of cryosphere data increases in
coming years, this is an interesting and important opportunity for machine
learning to address a global challenge for climate change, risk management from
floods, and conserving freshwater resources. Future frame prediction of ice
melt and tracking the optical flow of ice dynamics presents modeling
difficulties, due to uncertainties in global temperature increase, changing
precipitation patterns, occlusion from cloud cover, rapid melting and glacier
retreat due to black carbon aerosol deposition, from wildfires or human fossil
emissions. We show the adversarial learning method helps improve the accuracy
of tracking the optical flow of ice dynamics compared to existing methods in
climate science. We present a dataset, IceNet, to encourage machine learning
research and to help facilitate further applications in the areas of
cryospheric science and climate change.
