Neural networks for geospatial data

Analysis of geospatial data has traditionally been model-based, with a mean model, customarily specified as a linear regression on the covariates, and a covariance model, encoding the spatial dependence. We relax the strong assumption of linearity and propose embedding neural networks directly within the traditional geostatistical models to accommodate non-linear mean functions while retaining all other advantages including use of Gaussian Processes to explicitly model the spatial covariance, enabling inference on the covariate effect through the mean and on the …

analysis covariance cs.lg data data analysis embedding encoding functions geospatial linear linear regression mean networks neural networks non-linear regression spatial stat.me stat.ml