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Workshop: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems

Deep Glacier Image Velocimetry: Mapping glacier velocities from Sentinel-2 imagery with deep learning

James Tlhomole · Matthew Piggott · Graham Hughes


Glacier systems are highly sensitive to climate change and play a pivotal role in global mean sea level rise. As such, it is important to monitor how glacier velocities and ice dynamics evolve under a changing climate. The growing wealth of satellite observations has facilitated the inference of glacier velocities from remote sensing imagery through feature tracking algorithms. At present, these rely on sparse cross-correlation estimates as well as computationally expensive optical flow solutions. Here we present a novel use of deep-learning for estimating annual glacier velocities, utilizing the recurrent optical-flow based architecture, RAFT, on consecutive pairs of optical Sentinel-2 imagery. Our results highlight that deep learning can generate dense per-pixel velocity estimates within an automated framework that utilizes Sentinel-2 images over the French Alps.

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