Spatial Interpolation
15 papers with code • 0 benchmarks • 0 datasets
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Latest papers
A Markov Reward Process-Based Approach to Spatial Interpolation
The interpolation of spatial data can be of tremendous value in various applications, such as forecasting weather from only a few measurements of meteorological or remote sensing data.
Auxiliary-task learning for geographic data with autoregressive embeddings
In this study, we propose SXL, a method for embedding information on the autoregressive nature of spatial data directly into the learning process using auxiliary tasks.
$π$VAE: a stochastic process prior for Bayesian deep learning with MCMC
We show that our framework can accurately learn expressive function classes such as Gaussian processes, but also properties of functions to enable statistical inference (such as the integral of a log Gaussian process).
On identifiability and consistency of the nugget in Gaussian spatial process models
We formally establish results on the identifiability and consistency of the nugget in spatial models based upon the Gaussian process within the framework of in-fill asymptotics, i. e. the sample size increases within a sampling domain that is bounded.
Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks
amoodie/StratGAN
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An important problem in geostatistics is to build models of the subsurface of the Earth given physical measurements at sparse spatial locations.
