Abstract:In computer vision and machine learning for geographic data, out-of-domain generalization is a pervasive challenge, arising from uneven global data coverage and distribution shifts across geographic regions. Though models are frequently trained in one region and deployed in another, there is no principled method for determining when this cross-region adaptation will be successful. A well-defined notion of distance between distributions can effectively quantify how different a new target domain is compared to the domains used for model training, which in turn could support model training and deployment decisions. In this paper, we propose a strategy for computing distances between geospatial domains that leverages geographic information with Optimal Transport methods (GeoSpOT). In our experiments, GeoSpOT distances emerge as effective predictors of cross-domain transfer difficulty. We further demonstrate that embeddings from pretrained location encoders provide information comparable to image/text embeddings, despite relying solely on longitude-latitude pairs as input. This allows users to get an approximation of out-of-domain performance for geospatial models, even when the exact downstream task is unknown, or no task-specific data is available. Building on these findings, we show that GeoSpOT distances can preemptively guide data selection and enable predictive tools to analyze regions where a model is likely to underperform.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.16220 [cs.LG] |
| (or arXiv:2604.16220v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.16220 arXiv-issued DOI via DataCite (pending registration) |
From: Livia Betti [view email]
[v1]
Fri, 17 Apr 2026 16:33:38 UTC (44,484 KB)
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