Hybrid 3D Segmentation Technique for 3D City Models

  • Khairul Hafiz SharkawiEmail author
  • Alias Abdul Rahman
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


3D city model is a virtual representation of a city or urban environment, where in GIS related context, represents existing cities in the world. Initially, they are used only as presentations that complement the results of 2D analyses and bear no analytical capabilities. The advancement in computer graphics technology has effectively sparked the effort towards realizing 3D GIS where the 3D models can be used directly in analyses rather than just purely visual enhancement. A lot of research has been conducted in an effort to provide analytical capabilities to the 3D models. Rapid developments in computer-related industries have led to cutting edge technologies that enable analyses to be conducted on the 3D models. The ability of 3D city models to represent real world objects more accurately have boosted its efficiency and usability in geospatial related analyses. Now, it has become the new trend in building and urban management while modelling 3D objects are getting easier with the emergence of user-friendly tools for 3D modelling available in the market. The Open Geospatial Consortium (OGC) has accepted City Geography Markup Language CityGML specifications as one of the international standards for representing and exchanging spatial data, making it easier to visualize, store and manage 3D city models data efficiently. CityGML represents the semantics, geometry, topology and appearance of 3D city models in five well-defined Level-of-Details (LoD), namely LoD0 to LoD4. However, complex building structures are making the 3D models unsuitable for analyses as it takes a lot of time to process large data. Thus, it is only logical to breakdown the complex building into manageable segments. Segmentation is basically a method to break down an object into simpler parts. This chapter introduces a hybrid 3D segmentation method based on semantic and geometric decomposition for 3D buildings in CityGML. The proposed method deals with segmentation of a 3D building based on its semantic value and surface characteristics, fitted by one of the predefined primitives. For future work, the segmentation method will be implemented as part of the change detection module that can detect any changes on the 3D buildings, store and retrieve semantic information of the changed structure.


3D city models 3D model segmentation CityGML 


  1. Agathos A, Pratikakis I, Perantonis S, Sapidis N, Azariadis P (2007) 3D Mesh segmentation methodologies for cad applications. Comput Aided Des Appl 4(6): 827–841Google Scholar
  2. Attene M, Falcidieno B, Spagnuolo M (2006) Hierarchical mesh segmentation based on fitting primitives. Visual Comput 22(3):181–193CrossRefGoogle Scholar
  3. Cheng SC, Kuo CT, Wu DC (2010) A novel 3D mesh compression using mesh segmentation with multiple principal plane analysis. Pattern Recogn 43(1):267–279CrossRefGoogle Scholar
  4. Gröger G, Plümer L (2012) CityGML—Interoperable semantic 3D city models. Isprs J Photogrammetry Remote Sens 71:12–33CrossRefGoogle Scholar
  5. Hu J, You S, Neumann U, Park KK (2004) Building modeling from LiDAR and aerial imagery. In: Proceedings of ASPRS, May 2004Google Scholar
  6. Isikdag U, Zlatanova S (2010) Interactive modelling of building in google earth: A 3D tool for urban planning. In: Neutens T, De Maeyer P (eds) Developments in 3D geo-information sciences, Springer, pp 52–70Google Scholar
  7. Kim C, Habib A, Chang YC (2008) Automatic generation of digital building models for complex structures from lidar data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. 37, Part B4. Beijing 2008Google Scholar
  8. Kolbe TH (2009) Representing and exchanging 3D city models with CityGML. In: Jiyeong L, Zlatanova S (eds): Proceedings of the 3rd international workshop on 3D geo-information, Seoul, Korea. Lecture Notes in Geoinformation and Cartography, Springer 2009Google Scholar
  9. Manferdini AM, Remondino F (2010) Reality-based 3D modeling, segmentation and web-based visualization. In: Ioannides M (ed) EuroMed 2010, LNCS 6436, 2010. © Springer Berlin, pp 110–124Google Scholar
  10. Mian AS, Bennamoun M, Owens R (2006) Three-dimensional model-based object recognition and segmentation in cluttered scenes. IEEE Trans Pattern Anal Mach Intell 28(10):1584–1601CrossRefGoogle Scholar
  11. Miliaresis G, Kokkas N (2007) Segmentation and object-based classification for the extraction of the building class from Lidar Dems. Comput Geosci 33(8):1076–1087CrossRefGoogle Scholar
  12. Poupeau B Bonin O (2006) 3D analysis with high-level primitives: a crystallographic approach. In: Riedl A, Kainz W, Elmes GA (eds) Progress in spatial data handling. © Springer, Berlin, pp 599–616Google Scholar
  13. Ribelles J, Heckbert P, Garland M, Stahovich T, Srivastava V. (2001) Finding and removing features from polyhedra. In: American association of mechanical engineers (ASME) Design automation conference, Pittsburgh PA, September 2001Google Scholar
  14. Sampath A, Shan J (2010) Segmentation and reconstruction of polyhedral building roofs from aerial lidar point clouds. IEEE Trans Geosci Remote Sens 48(3):1554–1567CrossRefGoogle Scholar
  15. Shamir A (2008) A survey on mesh segmentation techniques. Comput Graph Forum 27(6):1539–1556CrossRefGoogle Scholar
  16. Steinhage V, Behley J, Meisel S, Cremers AB (2010) Automated Updating and Maintenance of 3D City Models. Core Spatial Database—Updating, Maintenance and Services. ISPRS Archive 38(Part 4-8-2-W9)Google Scholar
  17. Sugihara K, Hayashi Y (2008) Automatic generation of 3D building models with multiple roofs. Tsinghua Sci Technol 13(S1):368–374Google Scholar
  18. Takase Y, Sho N, Sone A, Shimiya K (2003). Automatic Generation of 3D city Models and Related Applications. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 34-5/W10. Tarasp, Switzerland, 27–28 Feb 2003Google Scholar
  19. Thiemann F, Sester M (2004) Segmentation of buildings for 3D-generalisation. ICA workshop on generalisation and multiple representation, Leicester, 20–21 Aug 2004Google Scholar
  20. Tolt G, Persson A, Landgard J, Soderman U (2006) Segmentation and classification of airborne laser scanner data for ground and building detection–Art. No. 62140c. Laser Radar Technol Appl XI 6214:C2140–C2140Google Scholar
  21. You S, Hu J, Neumann U, Fox P (2003). Urban site modeling from LiDAR. In: 2nd international workshop on computer graphics and geometric modeling CGGM’2003, Montreal, CanadaGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.3D GIS Research Group, Department of Geoinformation, Faculty of Geoinformation and Real EstateUniversiti Teknologi MalaysiaJohor BahruMalaysia

Personalised recommendations