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Generalization and Visualization of 3D Building Models in CityGML

  • Siddique Ullah BaigEmail author
  • Alias Abdul Rahman
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Generally, cities are expanding due to rapid population growth and require 3D city models for effective town planning, communication and disaster management. Rendering of 3D scenes directly is not so much appropriate as appearance properties, textures and materials attached with city models drastically increase the loading time for visualization and spatial analysis. Additionally, different applications or users demand different Level of Detail (LoDs), thus one of the questions arises—how different LoDs can be made available to these applications? Generation of lower LoDs given by OGC standard CityGML from higher LoDs to reduce data volume is a generalization problem. Relying only on existing geometric-based generalization approaches can result in the elimination or merging of important features, hence, semantic information can be considered. A review of pertinent generalization algorithms proposed by several researchers is presented. Additionally, this paper provides a method for generalization of 3D structures with the aim to derive multiple LoDs keeping semantic information into account. For this purpose, height and positional accuracy of objects at different LoDs provided by CityGML are considered. Initially, building parts and installations are removed. 2D footprints of remaining 3D structures are projected onto ground and simplified to derive LoDs building geometry. An adoption of methods of Sester and Brenner (Continuous generalisation for visualisation on small mobile devices. Heidelberg, pp. 355–368, 2004) extended by Fan et al. (Lecture notes in geoinformation and cartography, advances in giscience. Springer, Heidelberg, pp. 387–405, 2009) are applied for simplification and aggregation of projected footprints. The experiments showed that due to repetition of coordinates of connected nodes in CityGML increase both the rendering time and memory space. However, elimination of important smaller features can be avoided by taking semantic information into account while performing generalization operations.

Keywords

3D building modeling Generalization Simplification Aggregation Level of details (LoDs) 

Notes

Acknowledgments

We would like to convey our deepest acknowledgement firstly to Universiti Teknologi Malaysia (UTM) for providing research grant Vote No. Q.J130000.7127.04J81, and also to Mr. Muhammad Imzan Hassan for managing the funding. Last, but not least, our sincere appreciations to Research Management Centre (RMC) of UTM and Ministry of Higher Education (MOHE), Malaysia for enabling us to carry out this research project.

References

  1. Anders K (2005) Level of detail generation of 3D building groups by aggregation and typification. In: Proceedings of the XXII international cartographic conference, La Coruna, SpainGoogle Scholar
  2. Bundy GL, Jones CB, Furse E (1995) Holistic generalisation of large-scale cartographic data. In: Müller JC, Lagrange JP, Weibel R (eds) GIS and generalisation, Gisdata 1. Taylor & Francis, London, pp 106–119Google Scholar
  3. Fan H, Meng L, Jahnke M (2009) Generalisation of 3D buildings modeled by CityGML. In: Paelke V, Sester M, Bernard L (eds) Lecture Notes in Geoinformation and Cartography, Advances in GIScience. Springer, Berlin, Heidelberg, pp 387–405Google Scholar
  4. Forberg A (2007) Generalisation of 3D building data based on scale-space approach. ISPRS J Photogrammetry Remote Sens 62(2):104–111CrossRefGoogle Scholar
  5. Glander T, Döllner J (2007) Cell-Based Generalization of 3D Building Groups with Outlier Management. In: Proceedings of the 15th international symposium on advances in geographic information systems, ACMGISGoogle Scholar
  6. Gröger G, Kolbe TH, Czerwinski A, Nagel C (2008) OpenGIS® City Geography Markup Language (CityGML) Implementation specification. ClinMed NetPrints. http://www.opengeospatial.org/legal/. Accessed 10 Oct 2011
  7. Harrie L (1973) An optimisation approach to cartographic generalization. Doctor thesis, Lund UniversityGoogle Scholar
  8. Kolbe TH (2008) Representing and exchanging 3D city models with CityGML. In: Zlatanova S, Lee J (eds) 3D geo-information sciences. Seoul, South Korea. Springer, Berlin, HeidelbergGoogle Scholar
  9. Mao B (2010) Visualization and generalization of 3D city models. Doctoral thesis, Lund, SwedenGoogle Scholar
  10. Mao B, Ban Y, Harrie L (2010) A multiple representation data structure for dynamic visualisation of generalised 3D city models. ISPRS J Photogrammetry Remote Sens, J Mol Med. doi: 10.1016/j.isprsjprs.2010.08.001 Google Scholar
  11. Mayer H (2005) Scale-spaces for generalisation of 3D buildings. Int J Geogr Inf Sci 19(8–9):975–997CrossRefGoogle Scholar
  12. OGC, (2009). CityGML specification. ClinMed NetPrints. http://www.opengeospatial.org/standards/citygml. Accessed 25 Nov 2011
  13. Powitz B (1992) Kartographische generalisierung topographischer daten in GIS. Kartographische Nachrichten 43(6):229–233Google Scholar
  14. Rainsford D, Mackaness W (2002) Template matching in support of generalization of rural buildings. In: Joint international symposium on “GeoSpatial theory, processing and applications” (ISPRS/Commission IV/SDH2002), Ottawa, CanadaGoogle Scholar
  15. Regnauld N (2001) Contextual building typification in automated map generalisation. Algorithmica 30(2):312–333CrossRefGoogle Scholar
  16. Sester M (2005) Optimization approaches for generalization and data abstraction. Int J Geogr Inf Sci 19(8):871–897CrossRefGoogle Scholar
  17. Sester M, Brenner C (2004) Continuous generalisation for visualisation on small mobile devices. In: 11th international symposium on spatial data handling, Heidelberg, p 355–368Google Scholar
  18. Staufenbiel W (1973) Zur Automation der Generalisierung topographischer Karten mit besonderer Berücksichtigung grossmasstäbiger Gebäudedarstellungen. PhD Thesis, Fachrichtung Vermessungswesen, Universitate Hanover, HanoverGoogle Scholar
  19. Thiemann F (2002) Generalization of 3D building data. The international archives of the photogrammetry, remote sensing and spatial information science, 34 (Part 4)Google Scholar
  20. Thiemann F, Sester M (2005) Interpretation of building parts from boundary representation, workshop on next generation 3D city models, BonnGoogle Scholar
  21. Thiemann F, Sester M (2006) 3D symbolization using adaptive templates. In: ISPRS technical commission II symposium, Vienna, AustriaGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.3D GIS Research Lab, Faculty of Geoinformation Science and Real EstateUniversiti Teknologi Malaysia (UTM)Johor BahruMalaysia

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