Spatial Meta-Learning-Based Representation for Unseen Geographic Entities

Geographic entity representation learning (GERL) is an emerging method that represents natural, administrative divisions, road networks, and points of interest (POIs) in a low-dimensional continuous vector space. GERL provides a fundamental approach for geographic entities to underpin a variety of intelligent applications by learning their representation vectors to capture the semantics and interactions of the entities. Previous GERL methods mainly focus on the representation learning of the geographic entities that are seen at the time of training, which struggle to accurately generate representation vectors for the growing number of unseen geographic entities that were not involved in model training. To address this issue, this article proposes spatial meta-learning-based representation learning (SMRL), which integrates spatial subgraphs and meta-learning to improve the representation vectors of unseen geographic entities. Specifically, SMRL first designs a spatial-aware subgraph sampling module based on attributes and relationships of geographic entities to divide entities into spatial subgraphs. It develops a local-level representation module to learn entity features at the subgraph level. Finally, SMRL proposes a meta-learning-driven representation strategy that integrates meta-learning to learn the representation of unseen geographic entities. Extensive experiments show that the proposed SMRL method outperforms baselines with both higher accuracy and higher computational efficiency. This study provides new explorations for the representation of unseen geographic entities and offers methodological References for the various geographic applications.