Integrating 3D city data through knowledge graphs

Abstract

CityGML is a widely adopted standard for representing and exchanging 3D city models. The representation of semantic and topological properties in CityGML makes it possible to query such 3D city data for analysis in various applications. Nevertheless, the potential of querying CityGML data has not been fully exploited. The official GML encoding of CityGML is mainly an information model used for data storage and exchange, but not suitable for performing complex queries. The most common way of dealing with CityGML data is to store them as tables in the 3DCityDB system. However, it remains a challenging task for end users to formulate SQL queries over 3DCityDB directly for their ad-hoc analytical tasks because of the gap between the semantics of CityGML and the relational schema adopted in 3DCityDB. The technology of Knowledge Graphs (KGs), where an ontology is at the core, is a good solution to bridge such a gap. Moreover, embracing KGs makes it easier to integrate with other spatial data sources, e.g. OpenStreetMap, and to perform queries combining information from multiple data sources. In this work, we describe a CityGML-KG framework to expose the CityGML data in 3DCityDB as a KG. To evaluate our approach, we use CityGML data from the city of Munich as a test area and integrate OpenStreetMap data.

Keywords

• CityGML
• OpenStreetMap
• data integration
• query answering
• geospatial knowledge graph
• ontology