Abstract
Large-scale optical sensing and precise, rapid assessment of seismic building damage in urban communities are increasingly demanded in disaster prevention and reduction. The common method is to train a convolutional neural network (CNN) in a pixel-level semantic segmentation approach and does not fully consider the characteristics of the assessment objectives. This study developed a machine-learning-derived two-stage method for post-earthquake building location and damage assessment considering the data characteristics of satellite remote sensing (SRS) optical images with dense distribution, small size, and imbalanced numbers. It included a modified You Only Look Once (YOLOv4) object detection module and a support vector machine (SVM) based classification module. In the primary step, the multiscale features were successfully extracted and fused from SRS images of densely distributed buildings by optimizing the YOLOv4 model toward the network structures, training hyperparameters, and anchor boxes. The fusion improved multi-channel features, optimization of network structure and hyperparameters have significantly enhanced the average location accuracy of post-earthquake buildings. Thereafter, three statistics (i.e., the angular second moment, dissimilarity, and inverse difference moment) were further discovered to effectively extract the characteristic value for earthquake damage from located buildings in SRS optical images based on the gray level co-occurrence matrix. They were used as the texture features to distinguish damage intensities of buildings, using the SVM model. The investigated dataset included 386 pre- and post-earthquake SRS optical images of the 2017 Mexico City earthquake, with a resolution of 1024 × 1024 pixels. Results show that the average location accuracy of post-earthquake buildings exceeds 95.7% and that the binary classification accuracy for damage assessment reaches 97.1%. The proposed two-stage method was validated by its extremely high precision in respect of densely distributed small buildings, indicating the promising potential of computer vision in large-scale disaster prevention and reduction using SRS datasets.
Highlights
IntroductionDamage assessment of buildings plays a critical role in seismic damage surveys and emergency management of urban areas after an earthquake disaster
The performance of deep convolutional neural network (CNN) is highly dependent on the hyperparameter selection and network structure
In reference to previously reported research, the pre-trained weights obtained from the large dataset should be capable of extracting the low-level and intermediate features of images [50]
Summary
Damage assessment of buildings plays a critical role in seismic damage surveys and emergency management of urban areas after an earthquake disaster. YOLOv4 holds promising potential for detecting post-earthquake buildings owing to its high precision and rapid speed. The feature layer with 32 times downsampling has rich feature dimensions and a large receptive field, but the feature scale is compressed seriously, which is more suitable for object detection with large size. The eight times downsampling is appropriate for small objects, obtaining a larger feature scale, a smaller feature dimension, and a receptive field. The 16 times downsampling located in the middle of the two layers prefers to detect the medium objects
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