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Towards Semantic 3D City Modeling and Visual Explorations

  • Qing Zhu
  • Junqiao ZhaoEmail author
  • Zhiqiang Du
  • Yeting Zhang
  • Weiping Xu
  • Xiao Xie
  • Yulin Ding
  • Fei Wang
  • Tingsong Wang
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

In recent years, the integration of semantics into 3D city models has become a consensus. The CityGML standard laid the foundation for the storage and application of semantics, which boosts the progress of semantic 3D city modeling. This paper reports an extended semantic model based on CityGML and its visual applications under the content of a three-dimensional GIS project of China. Firstly, concepts Room, Corridor and Stair are derived from concept Space which represents the similar concept of Room in CityGML. These concepts will benefit the application of indoor navigation. Geological model is also supported by this model, which enables the underground analysis. Secondly, a semi-automatic data integration tool is developed. The types of semantic concept are defined based on the Technical Specification for Three-Dimensional City Modeling of China which leads to an adaptive way to assign semantics into pure geometry. In order to better visualize the models enriched by semantics, two fundamental techniques, data reduction and selective representation are then introduced. It shows that semantics could not only help improve the performance of exploration tasks but also enhance the efficiency of spatial cognition. Finally, two exploration cases are presented, one is indoor navigation, the semantic model is used to extract the geometric path and a semantics enhanced navigation routine is used, which greatly enriches the connotation of ordinary navigation applications; the other is a unified profiler, in order to fill up the cross-section correctly, semantics are incorporated, which help ensure the topological and semantic consistency.

Keywords

Thematic Model Semantic Model Semantic Concept Visual Exploration Industry Foundation Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The research for this paper was funded by the National Basic Research Program of China (No. 2010CB731800), National Natural Science Foundation of China (40871212 and 40701144) as well as the National High Technology Research and Development Program of China (No. 2008AA121600).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Qing Zhu
    • 1
  • Junqiao Zhao
    • 1
    Email author
  • Zhiqiang Du
    • 1
  • Yeting Zhang
    • 1
  • Weiping Xu
    • 1
  • Xiao Xie
    • 1
  • Yulin Ding
    • 1
  • Fei Wang
    • 1
  • Tingsong Wang
    • 1
  1. 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanPeople’s Republic of China

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