A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network
Abstract 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%.
Highlights An automated seismic damage assessment framework is proposed for regional buildings. A building identification method is proposed using 3D building models as georeference. The CNN is adopted to assess the seismic damage of buildings based on aerial images. The predicted building damage can be linked to the GIS data for risk management.
Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network
Abstract 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%.
Highlights An automated seismic damage assessment framework is proposed for regional buildings. A building identification method is proposed using 3D building models as georeference. The CNN is adopted to assess the seismic damage of buildings based on aerial images. The predicted building damage can be linked to the GIS data for risk management.
Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network
Xiong, Chen (author) / Li, Qiangsheng (author) / Lu, Xinzheng (author)
2019-10-15
Article (Journal)
Electronic Resource
English
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