An indoor navigation network, simplified from complex building structures, is of primary importance for indoor navigation. Methods for generating such networks are becoming increasingly important as buildings are becoming larger and more complex. However, studies based on the geometry and topology of indoor spaces do not sufficiently account for the key and decision nodes of human wayfinding. This is mainly because the visible perception of pedestrians regarding the corridor path structure is not taken into consideration. In this study, we first propose the indoor visibility map (IVM), calculate the visibility of corridor space, and analyze the relationship between different types of corridors and quantitative visibility parameters of IVM. Based on the visibility analysis, corresponding network construction strategies are suggested for different types of indoor corridors that contain nodes and edges. The experimental results show that the proposed network identifies key locations in the corridor structure that have a decisive role in navigation. Furthermore, the network is simplified to some extent.

Indoor space information extraction is an important aspect of reconstruction for building information modeling and a necessary process for geographic information system from outdoor to indoor. Entity model extracting methods provide advantages in terms of accuracy for building indoor spaces, as compared with network and grid model methods, and the extraction results can be converted into a network or grid model. However, existing entity model extracting methods based on a search loop do not consider the complex indoor environment of a building, such as isolated columns and walls or cross-floor spaces. In this study, such complex indoor environments are analyzed in detail, and a new approach for extracting buildings’ indoor space information is proposed. This approach is based on indoor space boundary calculation, the Boolean difference for single-floor space extraction, relationship reconstruction, and cross-floor space extraction. The experimental results showed that the proposed method can accurately extract indoor space information from the complex indoor environment of a building with geometric, semantic, and relationship information. This study is theoretically important for better understanding the complexity of indoor space extraction and practically important for improving the modeling accuracy of buildings.

Presently, most models of crowd evacuations within a building are created manually. This is inefficient. To address this issue, this paper focuses on the differences and relationship between industry foundation classes (IFC) and the macroscopic network model for evacuation (MNME), and studies the mapping and construction methods used to convert entity objects into nodes and arcs in the MNME. Furthermore, corresponding attribute information in the MNME are established from IFC. Based on this process, the MNME is created and used as an input to EVACNET4 to obtain simulation results. Finally, the results, together with a 3D building model, are expressed in a unified environment. Our study results show that a network as well as the relevant attributes can be automatically generated from IFC, and can be adapted to different working conditions. The method proposed in this paper can automatically map the semantic information model to the MNME and the simulation result as well as the building model can be integrated in a Geographic Information System (GIS) environment.