Advertisement

Modeling Cities and Landscapes in 3D with CityGML

  • Ken Arroyo Ohori
  • Filip Biljecki
  • Kavisha Kumar
  • Hugo Ledoux
  • Jantien Stoter
Chapter

Abstract

CityGML is the most important international standard used to model cities and landscapes in 3D with extensive semantics. Compared to BIM standards such as IFC, CityGML models are usually less detailed but they cover a much greater spatial extent. They are also available in any of five standardized levels of detail. CityGML serves as an exchange format and as a data source for visualizations, either in dedicated applications or in a web browser. It can also be used for a wide range of spatial analyses, such as visibility studies and solar potential. Ongoing research will improve the integration of BIM standards with CityGML, making improved data exchange possible throughout the life-cycle of urban and environmental processes.

References

  1. 3D.
    3D City Database. (2017). (3D city DB: The CityGML database). Retrieved from http://www.3dcitydb.org/ Google Scholar
  2. Agugiaro, G. (2016). Energy planning tools and CityGML-based 3D virtual city models: Experiences from Trento (Italy). Applied Geomatics, 8(1), 41–56.CrossRefGoogle Scholar
  3. Amirebrahimi, S., Rajabifard, A., Mendis, P., & Ngo, T. (2016). A BIM-GIS integration method in support of the assessment and 3D visualisation of flood damage to a building. Journal of Spatial Science, 61(2), 317–350.CrossRefGoogle Scholar
  4. Amorim, J. H., Valente, J., Pimentel, C., Miranda, A. I., & Borrego, C. (2012). Detailed modelling of the wind comfort in a city avenue at the pedestrian level. In: Leduc, T., Moreau, G., Billen, R. (Eds.), Usage, usability, and utility of 3D city models – European COST action TU0801 (pp. (03,008)1–6). EDP Sciences, Nantes.Google Scholar
  5. Arroyo Ohori, K., Ledoux, H., & Stoter, J. (2015). A dimension-independent extrusion algorithm using generalised maps. International Journal of Geographical Information Science, 29(7), 1166–1186.CrossRefGoogle Scholar
  6. Biljecki, F., Stoter, J., Ledoux, H., Zlatanova, S., & Çöltekin, A. (2015). Applications of 3D city models: State of the art review. ISPRS International Journal of Geo-Information, 4(4), 2842–2889.CrossRefGoogle Scholar
  7. Biljecki, F., Ledoux, H., & Stoter, J. (2016). An improved LOD specification for 3D building models. Computers, Environment and Urban Systems, 59, 25–37.CrossRefGoogle Scholar
  8. Biljecki, F., Heuvelink, G. B. M., Ledoux, H., & Stoter, J. (2018). The effect of acquisition error and level of detail on the accuracy of spatial analyses. Cartography and Geographic Information Science, 45(2), 156–176. https://doi.org/10.1080/15230406.2017.1279986 CrossRefGoogle Scholar
  9. Boeters, R., Arroyo Ohori, K., Biljecki, F., & Zlatanova, S. (2015). Automatically enhancing CityGML LOD2 models with a corresponding indoor geometry. International Journal of Geographical Information Science, 29(12), 2248–2268.CrossRefGoogle Scholar
  10. Brasebin, M., Perret, J., Mustière, S., & Weber, C. (2012). Measuring the impact of 3D data geometric modeling on spatial analysis: Illustration with Skyview factor. In: T. Leduc, G. Moreau, & R. Billen (Eds.), Usage, usability, and utility of 3D city models – European COST action TU0801 (pp. (02,001)1–16). EDP Sciences, Nantes.Google Scholar
  11. Bremer, M., Mayr, A., Wichmann, V., Schmidtner, K., & Rutzinger, M. (2016). A new multi-scale 3D-GIS-approach for the assessment and dissemination of solar income of digital city models. Computers, Environment and Urban Systems, 57, 144–154.CrossRefGoogle Scholar
  12. Çağdaş, V. (2013). An application domain extension to CityGML for immovable property taxation: A Turkish case study. International Journal of Applied Earth Observation and Geoinformation, 21, 545–555.CrossRefGoogle Scholar
  13. Chaturvedi, K., Yao, Z., & Kolbe, T. H. (2015). Web-based exploration of and interaction with large and deeply structured semantic 3D city models using html5 and webgl. In Wissenschaftlich-Technische Jahrestagung der DGPF und Workshop on Laser Scanning Applications (Vol. 3).Google Scholar
  14. Donkers, S., Ledoux, H., Zhao, J., & Stoter, J. (2016). Automatic conversion of IFC datasets to geometrically and semantically correct CityGML LOD3 buildings. Transactions in GIS, 20(4), 547–569.CrossRefGoogle Scholar
  15. El-Mekawy, M., Östman, A., & Hijazi, I. (2012). A unified building model for 3D urban GIS. ISPRS International Journal of Geo-Information, 1(3), 120–145.CrossRefGoogle Scholar
  16. Geiger, A., Benner, J., & Haefele, K. H. (2015). Generalization of 3D IFC building models. In M. Breunig, M. Al-Doori, E. Butwilowski, P. V. Kuper, J. Benner, & K. H. Haefele (Eds.), 3D geoinformation science (pp. 19–35). Cham: Springer.Google Scholar
  17. Gröger, G., & Plümer, L. (2012). CityGML – interoperable semantic 3D city models. ISPRS Journal of Photogrammetry and Remote Sensing, 71, 12–33.CrossRefGoogle Scholar
  18. Haala, N., & Kada, M. (2010). An update on automatic 3D building reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 570–580.CrossRefGoogle Scholar
  19. Kim, K., & Wilson, J. P. (2014). Planning and visualising 3D routes for indoor and outdoor spaces using CityEngine. Journal of Spatial Science, 60(1), 179–193.CrossRefGoogle Scholar
  20. Kolbe, T. H. (2009). Representing and exchanging 3D city models with CityGML. In: S. Zlatanova & J. Lee (Eds.), 3D geo-information sciences (pp. 15–31). Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  21. Ledoux, H. (2013). On the validation of solids represented with the international standards for geographic information. Computer-Aided Civil and Infrastructure Engineering, 28(9), 693–706.CrossRefGoogle Scholar
  22. Mao, B., & Ban, Y. (2011). Online visualization of 3D city model using CityGML and X3DOM. Cartographica: The International Journal for Geographic Information and Geovisualization, 46(2), 109–114.CrossRefGoogle Scholar
  23. Monien, D., Strzalka, A., Koukofikis, A., Coors, V., & Eicker, U. (2017). Comparison of building modelling assumptions and methods for urban scale heat demand forecasting. Future Cities and Environment, 3(2). https://doi.org/10.1186/s40984-017-0025-7 CrossRefGoogle Scholar
  24. Nouvel, R., Kaden, R., Bahu, J. M., Kaempf, J., Cipriano, P., Lauster, M., Benner, J., Munoz, E., Tournaire, O., & Casper, E. (2015). Genesis of the CityGML energy ADE. In: J. L. Scartezzini (Ed.), Proceedings of the International Conference on CISBAT 2015 Future Buildings and Districts – Sustainability from Nano to Urban Scale, LESO-PB, EPFL (Lausanne) (pp. 931–936).Google Scholar
  25. Nouvel, R., Zirak, M., Coors, V., & Eicker, U. (2017). The influence of data quality on urban heating demand modeling using 3D city models. Computers, Environment and Urban Systems, 64, 68–80.CrossRefGoogle Scholar
  26. OGC. (2012). OGC geography markup language (GML) – Extended schemas and encoding rules 3.3.0. Open Geospatial Consortium.Google Scholar
  27. OGC. (2016). OGC CityGML quality interoperability experiment. Open Geospatial Consortium inc., document OGC 16-064r1.Google Scholar
  28. Open Geospatial Consortium. (2012). OGC city geography markup language (CityGML) encoding standard 2.0.0. Technical report.Google Scholar
  29. Pedrinis, F., Morel, M., & Gesquiére, G. (2015). Change detection of cities. In M. Breunig, M. Al-Doori, E. Butwilowski, P. V. Kuper, J. Benner, & K. H. Haefele (Eds.), 3D geoinformation science (pp. 123–139). Cham: Springer.Google Scholar
  30. Previtali, M., Barazzetti, L., Brumana, R., Cuca, B., Oreni, D., Roncoroni, F., & Scaioni, M. (2014). Automatic façade modelling using point cloud data for energy-efficient retrofitting. Applied Geomatics, 6(2), 95–113.CrossRefGoogle Scholar
  31. Prieto, I., Izkara, J. L., & del Hoyo, F. J. D. (2012). Efficient visualization of the geometric information of CityGML: Application for the documentation of built heritage. In B. Murgante, O. Gervasi, S. Misra, N. Nedjah, A. M. A. C. Rocha, D. Taniar, & B. O. Apduhan (Eds.), International Conference on Computational Science and Its Applications (pp. 529–544). Berlin/Heidelberg: Springer.Google Scholar
  32. Sokolov, I., & Crosby, J. (2011). Utilizing gbXML with AECOsim building designer and speedikon.Google Scholar
  33. Stadler, A., & Kolbe, T. H. (2007). Spatio-semantic coherence in the integration of 3D city models. The ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVI-2/C43, 8.Google Scholar
  34. Steuer, H., Machl, T., Sindram, M., Liebel, L., & Kolbe, T. H. (2015). Voluminator—approximating the volume of 3D buildings to overcome topological errors. In F. Bacao, M. Y. Santos, & M. Painho (Eds.), AGILE 2015 (pp. 343–362). Cham: Springer.Google Scholar
  35. van den Brink, L., Stoter, J., & Zlatanova, S. (2013a). Establishing a national standard for 3D topographic data compliant to CityGML. International Journal of Geographical Information Science, 27(1), 92–113. http://dx.doi.org/10.1080/13658816.2012.667105 CrossRefGoogle Scholar
  36. van den Brink, L., Stoter, J., & Zlatanova, S. (2013b). UML-based approach to developing a CityGML application domain extension. Transactions in GIS, 17(6), 920–942.CrossRefGoogle Scholar
  37. van Walstijn, L. (2015). Requirements for an integral testing framework of CityGML instance documents. Master’s thesis, Institute of Geodesy and Geoinformation Science, Technische Universitaet, Berlin.Google Scholar
  38. Vanclooster, A., Van de Weghe, N., & De Maeyer, P. (2016). Integrating indoor and outdoor spaces for pedestrian navigation guidance: A review. Transactions in GIS, 20(4), 491–525.CrossRefGoogle Scholar
  39. Wagner, D., Alam, N., Wewetzer, M., Pries, M., & Coors, V. (2015). Methods for geometric data validation of 3D city models. Int Arch Photogramm Remote Sens Spatial Inf Sci, XL-1-W5, 729–735.CrossRefGoogle Scholar
  40. Wrózyński, R., Sojka, M., & Pyszny, K. (2016). The application of GIS and 3D graphic software to visual impact assessment of wind turbines. Renewable Energy, 96, 625–635.CrossRefGoogle Scholar
  41. Zucker, G., Judex, F., Blöchle, M., Köstl, M., Widl, E., Hauer, S., Bres, A., & Zeilinger, J. (2016). A new method for optimizing operation of large neighborhoods of buildings using thermal simulation. Energy and Buildings, 125, 153–160.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ken Arroyo Ohori
    • 1
  • Filip Biljecki
    • 2
  • Kavisha Kumar
    • 1
  • Hugo Ledoux
    • 1
  • Jantien Stoter
    • 1
  1. 1.3D Geoinformation, Faculty of the Built Environment and ArchitectureDelft University of TechnologyDelftThe Netherlands
  2. 2.Department of Architecture, School of Design & EnvironmentNational University of SingaporeSingaporeSingapore

Personalised recommendations