OT on the Map: Quantifying Domain Shifts in Geographic Space

2026-04-17

AI Summary

This work addresses the challenge of out-of-distribution generalization in geospatial data across regions, where distributional shifts hinder model transfer and existing approaches lack effective means to quantify domain discrepancies. To this end, we propose GeoSpOT, which for the first time integrates optimal transport theory with geographic coordinate encoding to construct a geospatial inter-domain distance metric using only latitude and longitude. Notably, GeoSpOT requires no downstream task data to quantify domain shift or predict model transfer performance. Experimental results demonstrate that the GeoSpOT distance accurately forecasts cross-regional generalization outcomes and effectively guides data selection and identification of high-risk regions.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• domain shift
• geospatial data
• out-of-domain generalization
• cross-region adaptation
• distribution distance

Innovation

• Optimal Transport
• Geospatial Domain Shift
• Location Embeddings
• Out-of-Domain Generalization
• GeoSpOT