Deep autoregressive modeling for land use land cover
Christopher Krapu Mark Borsuk Ryan Calder
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.