Digital Twin Smart City: Integrating IFC and CityGML with Semantic Graph for Advanced 3D City Model Visualization

Area of Science:

• Geoinformatics
• Urban Planning
• Data Management

Background:

• Building Information Modeling (BIM) is increasingly vital for urban management and supply chain analysis.
• Integrating BIM with 3D Geographic Information System (GIS) tools presents ongoing challenges.
• Existing methods struggle with seamless data exchange between BIM and GIS.

Purpose of the Study:

• To propose and validate data transformation methods for integrating BIM (IFC) with 3D GIS (CityGML, OWL/RDF).
• To enhance interoperability and visualization of 3D city models.
• To facilitate improved urban management and planning through seamless data integration.

Main Methods:

• Data transformation mapping between Industry Foundation Classes (IFC), City Geometry Markup Language (CityGML), and Web Ontology Framework (OWL)/Resource Description Framework (RDF).
• Utilizing Feature Manipulation Engine (FME) for IFC to CityGML (LOD4) conversion.
• Converting CityGML to OWL/RDF for validation and semantic analysis using Neo4j graph database.
• Visualizing integrated BIM and GIS data via Cesium Ion and Unreal Engine.

Main Results:

• Successful conversion of IFC data to CityGML (LOD4) and subsequently to OWL/RDF.
• Enhanced interoperability between BIM and GIS data formats.
• Effective visualization of 3D city models using web services and game engines.
• Demonstrated utility of RDF graph analysis for semantic mapping and urban data insights.

Conclusions:

• The proposed data transformation methods significantly improve BIM-GIS integration.
• Enhanced interoperability and visualization capabilities support better urban management and planning.
• The study provides a robust framework for managing and analyzing complex 3D urban data.