GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale

TL;DR

GeoVectors is a comprehensive, world-scale linked open corpus of OSM entity embeddings that captures semantic and geographic information, enabling machine learning and semantic applications on over 980 million geographic entities.

Contribution

It introduces a novel, large-scale linked corpus of OSM embeddings with semantic links to Wikidata and DBpedia, facilitating advanced geographic data analysis.

Findings

Coverage of over 980 million entities across 180 countries

Semantic links to Wikidata and DBpedia for contextual information

Accessible via a SPARQL endpoint for direct querying

Abstract

OpenStreetMap (OSM) is currently the richest publicly available information source on geographic entities (e.g., buildings and roads) worldwide. However, using OSM entities in machine learning models and other applications is challenging due to the large scale of OSM, the extreme heterogeneity of entity annotations, and a lack of a well-defined ontology to describe entity semantics and properties. This paper presents GeoVectors - a unique, comprehensive world-scale linked open corpus of OSM entity embeddings covering the entire OSM dataset and providing latent representations of over 980 million geographic entities in 180 countries. The GeoVectors corpus captures semantic and geographic dimensions of OSM entities and makes these entities directly accessible to machine learning algorithms and semantic applications. We create a semantic description of the GeoVectors corpus, including identity links to the Wikidata and DBpedia knowledge graphs to supply context information. Furthermore, we provide a SPARQL endpoint - a semantic interface that offers direct access to the semantic and latent representations of geographic entities in OSM.