Building modeling from multi-source geodata

Liang Chien Chen*, Tee-Ann Teo, Fuan Tsai, Jiann Yeou Rau, Chieh Tsung Chen, Jia Xun Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The objective of this investigation is to establish building models from different data sources as well as different strategies. Three major works are included in this research: (1) building modeling, (2) texture mapping, and (3) visualization of a city model. In building modeling, we establish a 3-D Builder system to integrate various data sources, i.e. large scale topographic map, aerial images and Lidar point clouds, for building reconstruction. This system provides both datadriven and model-driven functions for different types of building. The system also includes texture mapping to generate photo-realistic building models. A 3-D Texturer that performs texture mapping from different data sources such as, ground-based camera and ground-based video are also developed. In order to visualize the city model, a 3-D Viewer with the techniques of level of detail is created to browse and query the 3-D objects. The test area, which is about 5000ha, is located in Taipei city. An area of 100ha is selected to generate photo-realistic models.

Original languageEnglish
Title of host publication30th Asian Conference on Remote Sensing 2009, ACRS 2009
Pages366-371
Number of pages6
StatePublished - 1 Dec 2009
Event30th Asian Conference on Remote Sensing 2009, ACRS 2009 - Beijing, China
Duration: 18 Oct 200923 Oct 2009

Publication series

Name30th Asian Conference on Remote Sensing 2009, ACRS 2009
Volume1

Conference

Conference30th Asian Conference on Remote Sensing 2009, ACRS 2009
CountryChina
CityBeijing
Period18/10/0923/10/09

Keywords

  • Building model
  • Cyber city
  • Texture mapping
  • Visualization

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  • Cite this

    Chen, L. C., Teo, T-A., Tsai, F., Rau, J. Y., Chen, C. T., & Chen, J. X. (2009). Building modeling from multi-source geodata. In 30th Asian Conference on Remote Sensing 2009, ACRS 2009 (pp. 366-371). (30th Asian Conference on Remote Sensing 2009, ACRS 2009; Vol. 1).