Neural networks for geospatial data. (arXiv:2304.09157v2 [stat.ML] UPDATED)

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 arxiv covariance data embedding encoding functions geospatial linear linear regression mean networks neural networks non-linear regression spatial stat.ml