Volume XLII-3/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 421-427, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-421-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 421-427, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-421-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  06 Mar 2018

06 Mar 2018

OBJECT-ORIENTED CLASSIFICATION OF LIDAR DATA FOR POST-EARTHQUAKE DAMAGE DETECTION

H. Rastiveis, N. Khodaverdi zahraee, and A. Jouybari H. Rastiveis et al.
  • School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Keywords: LiDAR, Earthquake, Classification, Object Oriented Image Analysis, Buildings Damage Detection

Abstract. The collapse of buildings during the earthquake is a major cause of human casualties. Furthermore, the threat of earthquakes will increase with growing urbanization and millions of people will be vulnerable to earthquakes. Therefore, building damage detection has gained increasing attention from the scientific community. The advent of Light Detection And Ranging (LiDAR) technique makes it possible to detect and assess building damage in the aftermath of earthquake disasters using this data. The purpose of this paper is to propose and implement an object-based approach for mapping destructed buildings after an earthquake using LiDAR data. For this purpose, first, multi-resolution segmentation of post-event LiDAR data is done after building extraction from pre-event building vector map. Then obtained image objects from post-event LiDAR data is located on the pre-event satellite image. After that, appropriate features, which make a better difference between damage and undamaged buildings, are calculated for all the image objects on both data. Finally, appropriate training samples are selected and imported into the object-based support vector machine (SVM) classification technique for detecting the building damage areas. The proposed method was tested on the data set after the 2010 earthquake of Port-au-Prince, Haiti. Quantitative evaluation of results shows the overall accuracy of 92 % by this method.