Integrating 3D City Data through Knowledge Graphs
Linfang Ding, Guohui Xiao, Albulen Pano, Mattia Fumagalli, Dongsheng, Chen, Yu Feng, Diego Calvanese, Hongchao Fan, Liqiu Meng

TL;DR
This paper presents a framework that converts CityGML city models into knowledge graphs, enabling more flexible querying and integration with other spatial data sources, demonstrated with Munich city data.
Contribution
The authors propose a novel method to represent CityGML data as a knowledge graph using declarative mappings, enhancing query capabilities and data integration.
Findings
CityGML data can be effectively modeled as a knowledge graph.
The framework facilitates integration with other spatial data sources like OpenStreetMap.
Demonstrated feasibility with Munich city data and OpenStreetMap integration.
Abstract
CityGML is a widely adopted standard by the Open Geospatial Consortium (OGC) 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 to perform analysis in various applications, e.g., security management and emergency response, energy consumption and estimation, and occupancy measurement. However, the potential of querying CityGML data has not been fully exploited. The official GML/XML encoding of CityGML is only intended as an exchange format but is not suitable for query answering. The most common way of dealing with CityGML data is to store them in the 3DCityDB system as relational tables and then query them with the standard SQL query language. Nevertheless, for end users, it remains a challenging task to formulate queries over 3DCityDB directly for their ad-hoc analytical…
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Taxonomy
Topics3D Modeling in Geospatial Applications · Geographic Information Systems Studies · Data Management and Algorithms
MethodsOntology
