Embedding Spatial and Semantic Contexts for Geo-Entity Typing in Smart City Applications

Basel Shbita, Binh Vu, Fandel Lin, Craig A. Knoblock

Abstract: Geospatial data are critical for urban planning and smart city applications, yet understanding and classifying geo-entities in diverse datasets remains challenging. Accurate representation and classification of geo-entities are essential for tasks such as geo-entity typing and linking, enabling better map understanding and informed decision-making. This paper presents a self-supervised learning approach to classify geo-entities by embedding their geometric, spatial, and semantic neighborhood contexts, creating robust representations for geo-entity typing. Using OpenStreetMap (OSM) data, our method links geo-referenced entities to Wikidata classes and OSM tags with high performance, achieving an F1 score of approximately 0.85. Beyond the technical contribution, our method addresses Responsible AI challenges, including transparency, and data standardization on the Web, aligning with sustainable smart city development.

External IDs:dblp:conf/www/ShbitaVLK25