Effective Plans during Earthquake Emergencies for Medical Treatment

Abstract

The collapse of buildings caused by earthquakes can lead to a large loss of life and property. Rapid assessment of building damage with remote sensing image data can support emergency rescues. However, current studies indicate that only a limited sample set can usually be obtained from remote sensing images immediately following an earthquake. Consequently, the difficulty in preparing sufficient training samples constrains the generalization of the model in the identification of earthquake-damaged buildings. To produce a deep learning network model with strong generalization, this study adjusted Convolution Neural Network (CNN) models for extracting earthquake detection, damaged building information and compared their performance. A sample dataset of damaged buildings, earthquake detection, was constructed by using multiple disaster images retrieved from our dataset. These results provide a solution to the rapid extraction of earthquake-damaged building information based on a deep learning network model.