mlsanityData to 7 Oct 2026

A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid

arXiv 2405.15375 ↗First posted 24 May 2024cs.AI cs.DB cs.DCcited by 2

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Abstract (arXiv)

Geospatial data plays a central role in modeling our world, for which OpenStreetMap (OSM) provides a rich source of such data. While often spatial data is represented in a tabular format, a graph based representation provides the possibility to interconnect entities which would have been separated in a tabular representation. We propose in our paper a framework which supports a planet scale transformation of OpenStreetMap data into a Spatial Temporal Knowledge Graph. In addition to OpenStreetMap data, we align the different OpenStreetMap geometries on individual h3 grid cells. We compare our constructed spatial knowledge graph to other spatial knowledge graphs and outline our contribution in this paper. As a basis for our computation, we use Apache Sedona as a computational framework for our Spatial Temporal Knowledge Graph construction

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 2 of its 17 references that are in the map. First posted on arXiv 24 May 2024.

Builds on

2 of 17 references.

References · 2

  1. 2021WorldKG: A World-Scale Geographic Knowledge Graphcited by 11
  2. 2015S2RDF: RDF Querying with SPARQL on Sparkcited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.