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Representation learning of spatial and geographic data is a rapidlydeveloping field which allows for similarity detection between areas andhigh-quality inference using deep neural networks. Past approaches howeverconcentrated on embedding raster imagery (maps, street or satellite photos),mobility data or road networks. In this paper we propose the first approach tolearning vector representations of OpenStreetMap regions with respect to urbanfunctions and land-use in a micro-region grid. We identify a subset of OSM tagsrelated to major characteristics of land-use, building and urban regionfunctions, types of water, green or other natural areas. Through manualverification of tagging quality, we selected 36 cities were for training regionrepresentations. Uber's H3 index was used to divide the cities into hexagons,and OSM tags were aggregated for each hexagon. We propose the hex2vec methodbased on the Skip-gram model with negative sampling. The resulting vectorrepresentations showcase semantic structures of the map characteristics,similar to ones found in vector-based language models. We also present insightsfrom region similarity detection in six Polish cities and propose a regiontypology obtained through agglomerative clustering.

Hex2vec -- Context-Aware Embedding H3 Hexagons with OpenStreetMap Tags