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

Antarctic bed topography super-resolution via transfer learning

Kim Bente · Roman Marchant · Fabio Ramos


High-fidelity topography models of the bedrock underneath the thick Antarctic ice sheet can improve scientists’ understanding of ice flow and its contributions to global sea level rise. However, the bed topography of Antarctica is one of the most challenging surfaces on Earth to map, requiring airplanes with ice-penetrating radars to survey the vast and remote continent. We propose a model that leverages readily available surface topography data from satellites as an auxiliary input modality for bed topography super-resolution. We use a non-parametric Gaussian Process model to transfer local, non-stationary covariance patterns from surface to bedrock. In a controlled reconstruction experiment over complex East Antarctic terrain, our proposed method outperforms bicubic interpolation at all five tested magnification factors, reducing RMSE by 67% at x2 and 25% at x6 magnification. This work demonstrates the opportunity for data fusion methods to advance downstream climate modelling and steward climate change adaptation strategies.

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