Automatic change detection of Urban areas using LIDAR data

Kyoung Ah Choi, Im Pyeong Lee

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Change detection has been recognized as one of the most important steps to update city models. In this study, we thus propose a method to detect urban changes from two sets of LIDAR data acquired at different times. The main processes in the proposed method are (1) detecting change areas through subtraction between two DSMs generated from the LIDAR sets, (2) organizing the LIDAR points within the detected areas into surface patches, (3) classifying the class of each patch such as ground, vegetation, and building, and (4) determining the kinds of changes based on the properties and classes of the patches. The results which were obtained from the application of the proposed method to real data were verified as appropriate using the reference data manually acquired from the visual inspection of the orthoimages of the same area. The probability of success in change detection is assessed to 97% on an average. In conclusion, the proposed method is evaluated as a reliable, and efficient approach to change detection and thus the update of city model.

Original languageEnglish
Pages (from-to)341-350
Number of pages10
JournalJournal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
Issue number4
StatePublished - 31 Aug 2008


  • Change detection
  • City model
  • Classification
  • LIDAR data
  • Segmentation
  • Urban areas


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