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Land use / land cover (LULC) modeling is a challenging task due to long-rangedependencies between geographic features and distinct spatial patterns relatedto topography, ecology, and human development. We identify a close connectionbetween modeling of spatial patterns of land use and the task of imageinpainting from computer vision and conduct a study of a modified PixelCNNarchitecture with approximately 19 million parameters for modeling LULC. Incomparison with a benchmark spatial statistical model, we find that the formeris capable of capturing much richer spatial correlation patterns such as roadsand water bodies but does not produce a calibrated predictive distribution,suggesting the need for additional tuning. We find evidence of predictiveunderdispersion with regard to important ecologically-relevant land usestatistics such as patch count and adjacency which can be ameliorated to someextent by manipulating sampling variability.

Deep autoregressive modeling for land use land cover