Predicting ice flow using machine learning

TL;DR

This paper applies unsupervised machine learning techniques, specifically adversarial learning, to improve the accuracy of ice flow tracking in satellite images, addressing challenges in climate science and cryosphere monitoring.

Contribution

It introduces a novel application of adversarial learning for optical flow prediction in ice dynamics and provides a new dataset, IceNet, for further research.

Findings

Adversarial learning improves ice flow tracking accuracy.

The IceNet dataset facilitates cryospheric machine learning research.

Method outperforms existing climate science techniques.

Abstract

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.