# Spatial Interpolation

8 papers with code • 0 benchmarks • 0 datasets

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# $π$VAE: a stochastic process prior for Bayesian deep learning with MCMC

17 Feb 2020

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).

2

# Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks

8 Feb 2018

An important problem in geostatistics is to build models of the subsurface of the Earth given physical measurements at sparse spatial locations.

1

# On identifiability and consistency of the nugget in Gaussian spatial process models

15 Aug 2019

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.

1

# Auxiliary-task learning for geographic data with autoregressive embeddings

18 Jun 2020

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.

1

# A Markov Reward Process-Based Approach to Spatial Interpolation

1 Jun 2021

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.

1

# Positional Encoder Graph Neural Networks for Geographic Data

19 Nov 2021

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).

1

# 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.

1

# Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training

Our proposed approach is extensively validated on the ViSha dataset and a self-annotated dataset.

1