After a destructive earthquake, rapid evaluation of damaged buildings information is crucial for effective disaster relief efforts. Traditional data collecting techniques are often slow and insufficient to meet the urgent demands of earthquake response. To address this issue, this study introduces an improved algorithm derived from the you only look once version 8 (yolov8) model, tailored for the identification of damaged building components post-earthquake. In this study, the information extraction section of the backbone of YOLOv8 is improved. The Parallel Attention Mechanism Model (PAM) is introduced to improve the model's ability to deal with complex scenarios. Apart from that, the SimSPPF structure is introduced to optimize the feature pyramid layer, which can increase the speed. The results show the effectiveness of the improved YOLOv8 algorithm in identifying the damaged constructs of earthquake-damaged buildings. Average accuracy improved by 3.2% compared to the original model. The method can provide a valuable reference for the development of automatic analysis methods for earthquake information.
It is important to access the disaster distribution for earthquake disaster assessment and emergency command. Quick access to post-seismic disaster information is necessary for emergency command. Based on the post-earthquake landslide image dataset, a residual network model is used to identify disaster information on landslide image data using migration learning techniques. The research results show that the use of deep learning methods can better analyse landslide images, with recognition accuracy reaching over 93%, and can effectively extract disaster information from the images, providing technical support for the automatic analysis of emergency disaster data.
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