NARA: Anchor-Conditioned Representation Learning for Heterogeneous Vector Geoentities

Vector geospatial data represent the world as discrete geoentities, such as roads, buildings, and points of interest, each with semantic attributes, geometry, and spatial relations to other geoentities, including metric proximity and topology. Existing methods for learning geoentity representations