ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-X-4-W8-2025-649-2026 Introducing Building Indoor Topology Model (BITM) to Store Indoor Topology and Supporting Semantic Objects for Indoor Navigation Sadidi Javad 1 Joudaki Zahra 1 Rezayan Hani 1 Department of Remote Sensing and GIS, Kharazmi University, Tehran, Iran 29 05 2026 X-4/W8-2025 649 654 Copyright: © 2026 Javad Sadidi et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/X-4-W8-2025/649/2026/isprs-annals-X-4-W8-2025-649-2026.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/X-4-W8-2025/649/2026/isprs-annals-X-4-W8-2025-649-2026.pdf

Indoor navigation models are a key part of indoor navigation systems, especially in microscale routing. Nowadays, indoor navigation approaches are using semantic information to extract indoor navigation graphs. However, the current algorithms suffer from a lack of detection of the semantics of indoor space. This paper proposes a Building Indoor Topology Model (BITM) for extracting indoor topology from BIM and storing the nodes and edges of the indoor navigation graph. BITM simplifies the complex indoor space by using Space and Portal classes and includes the geometric and semantic information required for the indoor navigation graph. BITM can support all indoor space objects. To clarify the structure and usage of BITM, a UML class diagram and a general approach to converting indoor topology to BITM using CityGML data model are provided. XMLSchema complexity comparison between BITM and three other standard navigation models proves the simplification influence of BITM. Hence, the proposed models can enhance the capability of current services for indoor navigation.