A Planet Scale Spatial-Temporal Knowledge Graph Based On OpenStreetMap And H3 Grid
Martin Böckling, Heiko Paulheim, Sarah Detzler
TL;DR
The paper proposes a planet-scale framework to convert OpenStreetMap data into a Spatio-Temporal Knowledge Graph by aligning OSM geometries to the h3 hexagonal grid and modeling them over time. It leverages Apache Sedona for scalable processing and employs a GeoSPARQL-like ontology with DE-9IM-based spatial relations to connect OSM geometries with grid cells. The approach yields a large-scale STKG with billions of triples and hundreds of millions of entities, enabling joint geographic and semantic analysis across time. Compared to existing graphs like WorldKG and KnowWhereGraph, this work emphasizes full geometry coverage, temporal dynamics, and grid-based hierarchical relations to support downstream spatial analytics on a planetary scale.
Abstract
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