Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Earthquake Disaster Response and Management Based on Intelligent Detection System
Earthquakes are a highly hazardous natural disaster. Remote sensing is a type of image data primarily based on large-scale image data, which can assist rescue personnel in disaster assessment and rescue work. At present, earthquake damage monitoring based on multi-temporal remote sensing images before and after earthquakes and post earthquake building damage monitoring are important means of building damage monitoring in current remote sensing images. Due to the difficulty in quickly obtaining pre earthquake data, it is particularly important to use high-resolution remote sensing images only after earthquakes for damage assessment. This article proposed an intelligent detection system based on deep learning, aiming to improve the efficiency of earthquake disaster response and management. The study selected image data before and after the Kumamoto earthquake in Japan, constructed a building damage dataset, and trained and validated a building damage identification model. The experimental results showed that in the fourth round, the loss value dropped to 0.18 and the training time was also reduced. This method can effectively identify the damage to buildings after earthquakes, providing strong support for earthquake disaster response and management.
Earthquake Disaster Response and Management Based on Intelligent Detection System
Earthquakes are a highly hazardous natural disaster. Remote sensing is a type of image data primarily based on large-scale image data, which can assist rescue personnel in disaster assessment and rescue work. At present, earthquake damage monitoring based on multi-temporal remote sensing images before and after earthquakes and post earthquake building damage monitoring are important means of building damage monitoring in current remote sensing images. Due to the difficulty in quickly obtaining pre earthquake data, it is particularly important to use high-resolution remote sensing images only after earthquakes for damage assessment. This article proposed an intelligent detection system based on deep learning, aiming to improve the efficiency of earthquake disaster response and management. The study selected image data before and after the Kumamoto earthquake in Japan, constructed a building damage dataset, and trained and validated a building damage identification model. The experimental results showed that in the fourth round, the loss value dropped to 0.18 and the training time was also reduced. This method can effectively identify the damage to buildings after earthquakes, providing strong support for earthquake disaster response and management.
Earthquake Disaster Response and Management Based on Intelligent Detection System
Sustain. Civil Infrastruct.
Al-Turjman, Fadi (Herausgeber:in) / Lin, Zhidan (Autor:in)
International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things ; 2024 ; Türkiye
27.12.2024
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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