Comparative analysis of building models to develop a generic indoor feature model

Misun Kim, Hyun Sang Choi, Jiyeong Lee

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Around the world, there is an increasing interest in Digital Twin cities. Although geospatial data is critical for building a digital twin city, currently-established spatial data cannot be used directly for its implementation. Integration of geospatial data is vital in order to construct and simulate the virtual space. Existing studies for data integration have focused on data transformation. The conversion method is fundamental and convenient, but the information loss during this process remains a limitation. With this, standardization of the data model is an approach to solve the integration problem while hurdling conversion limitations. However, the standardization within indoor space data models is still insufficient compared to 3D building and city models. Therefore, in this study, we present a comparative analysis of data models commonly used in indoor space modeling as a basis for establishing a generic indoor space feature model. By comparing five models of IFC (Industry Foundation Classes), CityGML (City Geographic Markup Language), AIIM (ArcGIS Indoors Information Model), IMDF (Indoor Mapping Data Format), and OmniClass, we identify essential elements for modeling indoor space and the feature classes commonly included in the models. The proposed generic model can serve as a basis for developing further indoor feature models through specifying minimum required structure and feature classes.

Original languageEnglish
Pages (from-to)297-311
Number of pages15
JournalJournal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
Issue number5
StatePublished - 2021


  • 3D indoor data model
  • Feature model comparative analysis
  • Indoor feature class
  • Indoor feature model


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