Spatial Interpolation
8 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Spatial Interpolation
Most implemented papers
$π$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).
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.
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.
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.
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.
Positional Encoder Graph Neural Networks for Geographic Data
This assumption can be improbable in many real-world settings, where the spatial structure is more complex and explicitly non-Euclidean (e. g., road networks).
Improving trajectory calculations using deep learning inspired single image superresolution
In a test setup using the Lagrangian particle dispersion model FLEXPART and reduced-resolution wind fields, we demonstrate that absolute horizontal transport deviations of calculated trajectories from "ground-truth" trajectories calculated with undegraded 0. 5{\deg} winds are reduced by at least 49. 5% (21. 8%) after 48 hours relative to trajectories using linear interpolation of the wind data when training on 2{\deg} to 1{\deg} (4{\deg} to 2{\deg}) resolution data.
Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training
Our proposed approach is extensively validated on the ViSha dataset and a self-annotated dataset.
