국제학술발표논문집 (International conference on construction engineering and project management)

한국건설관리학회 (Korea Institute of Construction Engineering and Management)
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Automated Generation of BIM Models with Indoor Spaces Using Street View Façade Images

  • Joonho Jeong (Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University) ;
  • Sohyun Kim (Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University) ;
  • Junwoo Park (Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University) ;
  • Jungmin Lee (Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University) ;
  • Kwangbok Jeong (Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University) ;
  • Jaewook Lee (Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University)
  • 발행 : 2024.07.29
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초록

The importance of 3D city models for sustainable urban development and management is underscored, but existing models often overlook indoor spaces and attribute information. This issue can be tackled with BIM models, though the conventional method requires accurate and extensive information, incurring considerable time and cost in data collection and processing. To overcome these limitations, this study proposes a method to automatically generate BIM models that include indoor spaces using street view images. The proposed method uses YOLOv5 to identify façade elements and DBSCAN to normalize façade layouts, facilitating the generation of detailed BIM models with a parametric algorithm. To validate the method, a case study of a building in Korea was conducted. The results showed that indoor spaces similar to the actual building were generated, with an error rate of object quantities between 8.46% and 9.03%. This study is anticipated to contribute to the efficient generation of 3D city models that incorporate indoor spaces.

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과제정보

This research was supported by grants from the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (NRF-2020R1A2C1010421) and the Ministry of Education (RS-2023-00271991) of the Korean government.

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