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

arXiv: https://arxiv.org/abs/2108.13092 · HTML: https://mlsanity.com/paper/2108.13092/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [PyTorch-BigGraph: A Large-scale Graph Embedding System](https://mlsanity.com/paper/1903.12287/) (2019)
- [Presence-Only Geographical Priors for Fine-Grained Image Classification](https://mlsanity.com/paper/1906.05272/) (2019)
- [Geo-Aware Networks for Fine-Grained Recognition](https://mlsanity.com/paper/1906.01737/) (2019)
- [OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing](https://mlsanity.com/paper/2007.06277/) (2020)
- [Incorporating Literals into Knowledge Graph Embeddings](https://mlsanity.com/paper/1802.00934/) (2018)
- [Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2004.11861/) (2020)
- [EventKG - the Hub of Event Knowledge on the Web - and Biographical Timeline Generation](https://mlsanity.com/paper/1905.08794/) (2019)
- [SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting](https://mlsanity.com/paper/2004.14171/) (2020)
- [Joint Spatio-Textual Reasoning for Answering Tourism Questions](https://mlsanity.com/paper/2009.13613/) (2020)
- [Linking OpenStreetMap with Knowledge Graphs -- Link Discovery for Schema-Agnostic Volunteered Geographic Information](https://mlsanity.com/paper/2011.05841/) (2020)
- [GeoSPARQL+: Syntax, Semantics and System for Integrated Querying of Graph, Raster and Vector Data -- Technical Report](https://mlsanity.com/paper/2009.05032/) (2020)
- [Turning Transport Data to Comply with EU Standards while Enabling a Multimodal Transport Knowledge Graph](https://mlsanity.com/paper/2011.06423/) (2020)

## Built on by (most cited first, 12 of 12)

- [Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey](https://mlsanity.com/paper/2503.13502/) (2025)
- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [City Foundation Models for Learning General Purpose Representations from OpenStreetMap](https://mlsanity.com/paper/2310.00583/) (2023)
- [FlexiReg: Flexible Urban Region Representation Learning](https://mlsanity.com/paper/2503.09128/) (2025)
- [S2Vec: Self-Supervised Geospatial Embeddings for the Built Environment](https://mlsanity.com/paper/2504.16942/) (2025)
- [From Points to Places: Towards Human Mobility-Driven Spatiotemporal Foundation Models via Understanding Places](https://mlsanity.com/paper/2506.14570/) (2025)
- [Attention-Based Vandalism Detection in OpenStreetMap](https://mlsanity.com/paper/2201.10406/) (2022)
- [PlaceRep: Geospatial Place Representation Learning from Large-Scale Point-of-Interest Data](https://mlsanity.com/paper/2507.02921/) (2025)
- [Creating Knowledge Graphs for Geographic Data on the Web](https://mlsanity.com/paper/2302.08823/) (2023)
- [Urban context and delivery performance: Modelling service time for cargo bikes and vans across diverse urban environments](https://mlsanity.com/paper/2409.06730/) (2024)
- [NARA: Anchor-Conditioned Representation Learning for Heterogeneous Vector Geoentities](https://mlsanity.com/paper/2605.12276/) (2026)
- [MARCUS: Missing-Aware Region Representation with Contextual Urban Signals for Rent Prediction](https://mlsanity.com/paper/2608.18546/) (2026)

## Abstract (arXiv)

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.