Spatial Interpolation

13 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

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

mlglobalhealth/pi-vae 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).

Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks

amoodie/StratGAN 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.

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

LuZhangstat/nugget_consistency 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.

Auxiliary-task learning for geographic data with autoregressive embeddings

konstantinklemmer/sxl 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.

A Markov Reward Process-Based Approach to Spatial Interpolation

LaurensArp/VPInt 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.

Attention-Based Spatial Interpolation for House Price Prediction

darniton/ASI 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

konstantinklemmer/pe-gnn 19 Nov 2021

Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data.

Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training

yihong-97/stict CVPR 2022

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

Deep Spatial Domain Generalization

dyu62/deep-domain-generalization 3 Oct 2022

Spatial domain generalization is a spatial extension of domain generalization, which can generalize to unseen spatial domains in continuous 2D space.