Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 929-936, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-929-2018
© 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, 929-936, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-929-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

BUILDING DAMAGE EXTRACTION TRIGGERED BY EARTHQUAKE USING THE UAV IMAGERY

S. Li1 and H. Tang2,3 S. Li and H. Tang
  • 1School of Resource and Environmental Science, Hebei Normal University, 050024, Shijiazhuang, China
  • 2State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, 100875, Beijing, China
  • 3Key Laboratory of Environment Change and Natural Disaster, Ministry of Education, Beijing Normal University, 100875, Beijing, China

Keywords: Building Damage Information, Earthquake Disasters, Image Fusion, Point Clouds, UAV Imagery

Abstract. When extracting building damage information, we can only determine whether the building is collapsed using the post-earthquake satellite images. Even the satellite images have the sub-meter resolution, the identification of slightly damaged buildings is still a challenge. As the complementary data to satellite images, the UAV images have unique advantages, such as stronger flexibility and higher resolution. In this paper, according to the spectral feature of UAV images and the morphological feature of the reconstructed point clouds, the building damage was classified into four levels: basically intact buildings, slightly damaged buildings, partially collapsed buildings and totally collapsed buildings, and give the rules of damage grades. In particular, the slightly damaged buildings are determined using the detected roof-holes. In order to verify the approach, we conduct experimental simulations in the cases of Wenchuan and Ya’an earthquakes. By analyzing the post-earthquake UAV images of the two earthquakes, the building damage was classified into four levels, and the quantitative statistics of the damaged buildings is given in the experiments.