# Aggregated Gaussian Processes with Multiresolution Earth Observation Covariates

arXiv: https://arxiv.org/abs/2105.01460 · HTML: https://mlsanity.com/paper/2105.01460/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2021.

## Built on by (most cited first, 2 of 2)

- [Convolutional Neural Processes for Inpainting Satellite Images](https://mlsanity.com/paper/2205.12407/) (2022)
- [Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya](https://mlsanity.com/paper/2305.19779/) (2023)

## Abstract (arXiv)

For many survey-based spatial modelling problems, responses are observed as spatially aggregated over survey regions due to limited resources. Covariates, from weather models and satellite imageries, can be observed at many different spatial resolutions, making the pre-processing of covariates a key challenge for any spatial modelling task. We propose a Gaussian process regression model to flexibly handle multiresolution covariates by employing an additive kernel that can efficiently aggregate features across resolutions. Compared to existing approaches that rely on resolution matching, our approach better maintains distributional information across resolutions, leading to better performance and interpretability. Our model yields stronger predictive performance and interpretability on both simulated and crop yield datasets.