13 papers with code • 0 benchmarks • 0 datasets
These leaderboards are used to track progress in Spatial Interpolation
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).
Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks
An important problem in geostatistics is to build models of the subsurface of the Earth given physical measurements at sparse spatial locations.
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.
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.
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.
For that, we propose a hybrid attention mechanism that weights neighbors based on their similarity to the house in terms of structural features and geographic location.
Intelligent Spatial Interpolation-based Frost Prediction Methodology using Artificial Neural Networks with Limited Local Data
In this article, a frost prediction method based on spatial interpolation is proposed.