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.

翻译：在计算机视觉与地理数据机器学习中，域外泛化是一个普遍存在的挑战，其根源在于全球数据覆盖的不均衡性以及地理区域间的分布偏移。尽管模型通常在一个区域训练后部署至另一个区域，但尚缺乏用于判断跨区域适配能否成功的原则性方法。分布间明确定义的距离概念，可有效量化新目标域与模型训练所用域之间的差异程度，进而为模型训练与部署决策提供支持。本文提出一种结合地理信息与最优传输方法（GeoSpOT）来计算地理空间域间距离的策略。实验表明，GeoSpOT距离能够有效预测跨域迁移的难度。我们进一步证明，尽管预训练位置编码器仅依赖经纬度对作为输入，其生成的嵌入向量却可提供与图像/文本嵌入相当的信息。这使得用户即便在未知具体下游任务或缺乏任务特定数据的情况下，也能近似评估地理空间模型的域外性能。基于这些发现，我们展示了GeoSpOT距离可预先指导数据选择，并构建预测工具以分析模型可能表现不佳的区域。