期刊
AUTOMATION IN CONSTRUCTION
卷 109, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.autcon.2019.102994
关键词
Regional seismic damage assessment; GIS; UAV; Convolutional neural network
资金
- Intellectual Innovation Program of Shenzhen Science and Technology Innovation Committee [JCYJ20180305123919731]
- National Key Research and Development Program [2018YFC1504401]
- National Natural Science Foundation of China [51708361]
- Natural Science Foundation of SZU [2017064]
A rapid assessment of the seismic damage to buildings can facilitate improved emergency response and timely relief in earthquake-prone areas. In this study, an automated building seismic damage assessment method using an unmanned aerial vehicle (UAV) and a convolutional neural network (CNN) is introduced. The method consists of three parts: (1) data preparation, (2) building image segmentation, and (3) CNN-based building seismic damage assessment. First, a three-dimensional (3D) building model, aerial images, and camera data are used for the following simulation. Next, a building image segmentation method is proposed using the 3D building model as georeference, through which multi-view segmented building images can be obtained. Subsequently, a CNN model based on VGGNet is adopted to assess the seismic damage of each building. The CNN model is fine-tuned based on manually tagged building images obtained from the Internet. Finally, a case study of the old Beichuan town is used to demonstrate the effectiveness of the proposed method. The damage distribution of the area is obtained with an accuracy of 89.39%.
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