DOI QR코드

DOI QR Code

A Study of IndoorGML Automatic Generation using IFC - Focus on Primal Space -

IFC를 이용한 IndoorGML 데이터 자동 생성에 관한 연구 - Primal Space를 중심으로 -

  • Received : 2020.11.19
  • Accepted : 2020.12.11
  • Published : 2020.12.31

Abstract

As the time spent in indoor space has increased, the demand for services targeting indoor spaces also continues to increase. To provide indoor spatial information services, the construction of indoor spatial information should be done first. In the study, a method of generation IndoorGML, which is the international standard data format for Indoor space, from existing BIM data. The characteristics of IFC objects were investigated, and objects that need to be converted to IndoorGML were selected and classified into objects that restrict the expression of Indoor space and internal passages. Using the proposed method, a part of data set provided by the BIMserver github and the IFC model of the 21st Century Building in University of Seoul were used to perform experiments to generate PrimalSpaceFeatures of IndoorGML. As a result of the experiments, the geometric information of IFC objects was represented completely as IndoorGML, and it was shown that NavigableBoundary, one of major features of PrimalSpaceFeatures in IndoorGML, was accurately generated. In the future, the proposed method will improve to generate various types of objects such as IfcStair, and additional method for automatically generating MultiLayeredGraph of IndoorGML using PrimalSpaceFeatures should be developed to be sure of completeness of IndoorGML.

사람들이 실내공간에서 소비하는 시간이 증가함에 따라, 실내공간을 대상으로 하는 서비스들에 대한 수요가 지속적으로 증가하고 있다. 실내공간정보 서비스를 제공하기 위해서는 실내공간정보를 구축하는 것이 우선적으로 이루어져야 하며 본 연구에서는 기 구축되어 있는 BIM (Building Information Management) 데이터로부터 실내공간정보 국제표준 데이터인 IndoorGML을 생성하는 방법을 제안하였는데 대표적인 BIM 데이터 자료인 IFC (Industry Foundation Class) 데이터를 IndoorGML로 변환하기 위하여 IFC 객체들의 특성을 조사하고, IndoorGML로 변환이 필요한 객체들을 선별하였으며 실내공간의 표현과 내부통행에 제약을 주는 객체로 구분하였다. 또한 개발된 변환도구를 이용하여 빔서버(BIMserver) 깃헙(github)에서 제공하는 데이터 셋 일부와 서울시립대학교 21세기관 IFC모델을 IndoorGML의 PrimalSpaceFeatures 데이터로 구축하는 실험을 수행하였다. 각각의 IFC 데이터를 IndoorGML로 변환한 결과 기하정보를 손실 없이 IndoorGML문서가 생성되었으며, IndoorGML이 가지고 있는 특징인 NavigableBoundary가 정확하게 생성되는 결과를 보였다. 추후, 다양한 형태의 IfcStair 객체를 변환하는 방법에 대한 추가적인 연구가 필요하며, 프라이멀 스페이스(PrimalSpace) 데이터를 이용하여 듀얼 스페이스(DualSpace) 데이터인 MultiLayeredGraph를 자동 생성하는 방법에 대한 개발이 필요하다.

Keywords

References

  1. Borrmann, A., Beetz, J., Koch, C., Liebich, Thomas, and Muhic, S. (2018), Industry Foundation Classes: A Standardized Data Model for the Vendor-Neutral Exchange of Digital Building Models, Building Information Modeling, Springer, pp. 81-126
  2. Diakite, A.A., and Zlatanova, S. (2016), EXTRACTION OF THE 3D FREE SPACE FROM BUILDING MODELS FOR INDOOR NAVIGATION. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, vol. IV-2/W1, pp. 241-248.
  3. Donkers, S., Ledoux, H., Zhao, J., and Stoter, J. (2016) Automatic conversion of IFC datasets to geometrically and semantically correct CityGML LOD3 buildings, Transaction in GIS vol.20, pp. 547-569. https://doi.org/10.1111/tgis.12162
  4. ISO (2004), Industrial Automation Systems and Integration. Product Data Representation and Exchange. Part 11L Description Method: The EXPRESS Language Reference Manual, International Standard ISO 10303-11, International Organization for Standardization, Geneva, Switzerland.
  5. Khan, A.A., Donaubauer, A., and Kolbe, T.H. (2014), A Multi-Step Transformation Process for Automatically Generating Indoor Routing Graphs from Existing Semantic 3D Building Models, 9th 3DGeoInfo Conference 2014 - Proceedings, 11-13 November, Dubai, pp. 123-156.
  6. Kwak, J., Kang, H., Kim, S., and Nam, S.(2020), A case study of cloud-based IndoorGML database storage and management technique, 2020 KASGIS Fall Conference, 12-14 November, Jeju, Korea, pp.233-234. (in Korean)
  7. OGC (2012), OGC City Geography Markup Language(CityGML) Encoding Standard 2.0.0, OGC 12-019, Open Geospatial Consortium, Wayland, MA, USA.
  8. OGC (2019), OGC IndoorGML 1.1 OGC 19-011r4, Open Geospatial Consortium, Wayland, MA, USA.
  9. OpensourceBIM (2016a), IfcOpenShell-BIMserver-plugin, Github, https://github.com/opensourceBIM/IfcOpenShell-BIMserver-plugin (last date accessed: 23 October 2020).
  10. OpensourceBIM (2016b), TestFiles, Github, https://github.com/opensourceBIM/TestFiles.git (last date accessed: 12 November 2020).
  11. Pavel Tobias (2015), An Investigation into the Possibilities of BIM and GIS Cooperation and Utilization of GIS in the BIM Process, Geoinformatics FCE CTU, vol.14(1), pp.65-78 https://doi.org/10.14311/gi.14.1.5
  12. STEMLab (2018), InViewer, Github, https://github.com/STEMLab/InViewer (last date accessed: 12 November 2020).
  13. Stouffs, R., Tauscher, H., and Biljecki, F. (2018) Achieving Complete and Near-Lossless Conversion from IFC to CityGML, International Journal of Geo-Information, vol.7, pp. 355-371. https://doi.org/10.3390/ijgi7090355
  14. Teo, T.A., Yu, S.C. (2017), The extraction of indoor building information from BIM to OGC IndoorGML, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.XLII-4/W2, pp. 167-170.
  15. TuDelft (2017), val3dity, Github, https://github.com/tudelft3d/val3dity (last date accessed: 12 November 2020).
  16. Wu, I. and Hsieh, S.(2007), Transformation from IFC data model to GML data model: Methodology and tool development, Journal of the Chinese Institute of Engineers, vol.30(6), pp. 1085-1090. https://doi.org/10.1080/02533839.2007.9671335