Spatial Interpolation
15 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.
Attention-Based Spatial Interpolation for House Price Prediction
darniton/ASI
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International Conference on Advances in Geographic Information Systems 2021
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.
Positional Encoder Graph Neural Networks for Geographic Data
Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data.
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.
Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training
Our proposed approach is extensively validated on the ViSha dataset and a self-annotated dataset.
Deep Spatial Domain Generalization
dyu62/deep-domain-generalization
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Spatial domain generalization is a spatial extension of domain generalization, which can generalize to unseen spatial domains in continuous 2D space.
