@inproceedings{
wang2023model,
title={Model Evaluation for Geospatial Problems},
author={Jing Wang and Tyler Hallman and Laurel Hopkins and John Burns Kilbride and W. Douglas Robinson and Rebecca Hutchinson},
booktitle={NeurIPS 2023 Computational Sustainability: Promises and Pitfalls from Theory to Deployment},
year={2023},
url={https://openreview.net/forum?id=z5dAdYOgbs}
}
Geospatial problems often involve spatial autocorrelation and covariate shift, which violate the independent, identically distributed assumption underlying standard cross-validation. In this work, we establish a theoretical criterion for unbiased cross-validation, introduce a preliminary categorization framework to guide practitioners in choosing suitable cross-validation strategies for geospatial problems, reconcile conflicting recommendations on best practices, and develop a novel, straightforward method with both theoretical guarantees and empirical success.
Model Evaluation for Geospatial Problems
Jing Wang Tyler Hallman Laurel Hopkins John Burns Kilbride W. Douglas Robinson Rebecca Hutchinson