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arXiv · 2401.01395

Deep autoregressive modeling for land use land cover

Christopher Krapu·Mark Borsuk·Ryan Calder
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Abstract

Land use / land cover (LULC) modeling is a challenging task due to long-range dependencies between geographic features and distinct spatial patterns related to topography, ecology, and human development. We identify a close connection between modeling of spatial patterns of land use and the task of image inpainting from computer vision and conduct a study of a modified PixelCNN architecture with approximately 19 million parameters for modeling LULC. In comparison with a benchmark spatial statistical model, we find that the former is capable of capturing much richer spatial correlation patterns such as roads and water bodies but does not produce a calibrated predictive distribution, suggesting the need for additional tuning. We find evidence of predictive underdispersion with regard to important ecologically-relevant land use statistics such as patch count and adjacency which can be ameliorated to some extent by manipulating sampling variability.

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Christopher Krapu, Mark Borsuk, Ryan Calder. 2024-01-02. Deep autoregressive modeling for land use land cover. https://arxiv.org/abs/2401.01395

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