IFC-Based Indoor Space Extraction with Topological Graph Modeling

The semantic enrichment of building information models (BIMs) has been widely explored, with various approaches utilizing graph neural networks (GNNs) to infer the types of indoor spaces. However, there is a gap in the intermediate process that translates the building information model into a graph model suitable for GNNs. To address this problem, we propose a structured graph model designed to represent the attributes and topological relationships of indoor spaces for indoor space classification. Based on the Industry Foundation Classes (IFC) file format, we define the concept of indoor space and propose an automated method for its extraction. The extracted interior subspaces are analyzed based on their geometric and topological properties, focusing on their relationship to the overall layout of the building's interior spaces. The subspaces are represented as nodes in the graph model, and the edges between the nodes are defined according to the topological relationships between the subspaces. A result was carried out to demonstrate the effect of the proposed indoor space extraction algorithm, which provided the basis for the inference of indoor spatial semantic types in building information models.