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

Probabilistic land cover modeling via deep autoregressive models

Christopher Krapu · Ryan Calder · Mark Borsuk


Land use and land cover (LULC) modeling is a challenging task due to long-range dependencies between geographic features and distinct spatial patterns related in topography, ecology, and human development. We explore the usage of a modified Pixel Constrained CNN as applied to inpainting for categorical image data from the National Land Cover Database for producing a diverse set of land use counterfactual scenarios. We find that this approach is effective for producing a distribution of realistic image completions in certain masking configurations. However, the resulting distribution is not well-calibrated in terms of spatial summary statistics commonly used with LULC data and exhibits substantial underdispersion.

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