Research on Fine Estimation of People Trapped after Earthquake on Single Building Level Based on Multi-Source Data

Abstract

Risk assessments of people who are trapped are an important basis for scientific and effective emergency rescue after an earthquake. Currently, most models are based on the kilometer grid scale or community scale that gauge the population and extent of the earthquake burial under distinct intensities. The estimation results of the methods are on coarse scales; therefore, the methods cannot meet the requirements of rapid rescue after an earthquake. In response to the above statements, this study uses multi-source data to propose a way to estimate the number and distribution of people trapped under the scale of single buildings. Firstly, we use pre-earthquake optical high spatial resolution remote sensing images for building detection, and then we combine them with multi-source data for population distribution simulation. Secondly, indoor ratio assessment models are constructed by analyzing human behavior. Then, aerial remote sensing images are used for building seismic damage level detection. Finally, based on these three factors, a single building crush burial estimation model is constructed to obtain the number and distribution of personnel trapped. In this paper, the reliability of the proposed workflow is demonstrated by the casualty results in experiments conducted in the nearby Moxi town after the Luding 6.8 magnitude earthquake on 5 September 2022. For future natural disaster events, this method can provide reliable information support and decision references for earthquake emergency rescue.