Change detection of building models from multi-source geodata

Liang Chien Chen*, Chih Yuan Huang, Tee-Ann Teo, Chao Yuan Lo

*Corresponding author for this work

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

Abstract

Three dimensional building models provide valuable spatial information for decision support. It is preferable to maintain a building database by firstly detecting the changes followed by a reconstruction procedure. Change detection is traditionally done using multi-temporal images through spectral analyses. Those images provide two-dimensional spectral information without including shape in the third dimension. As the availability and quality of emerging LIDAR systems that make the acquisition of shape information convenient, we use new LIDAR point clouds and aerial photos to detect changes for building models. The proposed scheme comprises data pre-processing and change detection on building areas. The validation for determination of change types shows that the results can reach 86% overall accuracy for those buildings with complicated roof structure. To provide comprehensive observations, those unreliable results are scrutinized.

Original languageEnglish
Title of host publication29th Asian Conference on Remote Sensing 2008, ACRS 2008
Pages938-943
Number of pages6
StatePublished - 1 Dec 2008
Event29th Asian Conference on Remote Sensing 2008, ACRS 2008 - Colombo, Sri Lanka
Duration: 10 Nov 200814 Nov 2008

Publication series

Name29th Asian Conference on Remote Sensing 2008, ACRS 2008
Volume2

Conference

Conference29th Asian Conference on Remote Sensing 2008, ACRS 2008
CountrySri Lanka
CityColombo
Period10/11/0814/11/08

Keywords

  • Building model
  • Change detection
  • Image
  • LIDAR
  • Multi-temporal

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  • Cite this

    Chen, L. C., Huang, C. Y., Teo, T-A., & Lo, C. Y. (2008). Change detection of building models from multi-source geodata. In 29th Asian Conference on Remote Sensing 2008, ACRS 2008 (pp. 938-943). (29th Asian Conference on Remote Sensing 2008, ACRS 2008; Vol. 2).