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Following the earthquake, prompt evaluation of the distribution of coseismic landslides and estimation of potential disaster losses are crucial for emergency response and resettlement planning. The Luding earthquake of 2022 offers a valuable opportunity to conduct a rapid assessment of coseismic landslides using various models. In this study, we utilize the Logistic Regression (LR)-based Xu2019 model, a new-generation model developed in China, alongside the Newmark model to perform the rapid hazard assessment of coseismic landslides. Assessing the accuracy and applicability of these two models based on the coseismic landslides from the Luding earthquake, we find that within intensity area of IX, the high probability area identified by the Newmark model aligns closely with the actual distribution of landslides. However, the Newmark model’s prediction is overestimated in the intensity area of VIII. For the Xu2019 model, the prediction results are in good agreement with the distribution of actual landslides. Most landslides are located in high probability areas, such as Detuo town, Wandong, and Xingfu villages, indicating that the model has a higher prediction accuracy. Overall, two models have good practical utility in emergency hazard assessment of coseismic landslides. However, the Newmark model requires multi-input parameters and the assignment of these parameters will increase the uncertainty and subjectivity in the practical application of the modeling assessment.

Seismic disaster risk assessment results are an important way to reduce disaster losses and casualties. In this article, a seismic disaster risk assessment method is constructed based on the lethal level, and a quantitative assessment of the earthquake disaster risk in Guangzhou is carried out. There are four typical building types in this study area: brick-wood structures, brick-concrete structures, reinforced concrete structures, and civil structures. Typical building structural components (vertical load-bearing component, ring beam, mortar of infilled wall, roof or floor structure, foundation, height of building, nonstructural component, anti-seismic joint or other anti-seismic measures) are selected to build the lethal level index system of buildings. On the basis of the field survey, a lethal level of 431 survey points is obtained. The average lethal level in urban areas is 0.3476, and in rural areas is 0.4198, the overall lethal level of each township is 0.389, and the errors between the urban and rural areas are 0.0414 and 0.0308, and the fitting results coefficient of determination (R2) values are 0.6803 and 0.6714, respectively, the results is good. Furthermore, considering the seismic hazard (peak ground acceleration, PGA), population exposure and lethal level, the seismic disaster risk distribution of the Guangzhou area is determined. Under different intensities, the risk of death has shown obvious characteristics, as the intensity increases, the number of possible deaths increases rapidly, ranging from a few to several thousand, the higher risk areas are relatively fixed, generally mainly located in the central city, the quantitative assessment results of disaster risk also have obvious directivity. The areas with the highest risk are located in most areas of Tianhe District, Liwan District and Haizhu District, the highest grid death result is more than 1; the lethal level of Baiyun District and Huangpu District is low, but the death risk is also higher, generally between 0.5 and 1, in rural areas in the southern and northern regions of Guangzhou, the risk is relatively low, generally between 0 and 0.5. The distribution map of seismic disaster risk can provide solid scientific and technological support for the government to investigate potential risk points.

After earthquake, the correct and efficient earthquake emergency evacuation of pedestrian is crucial for reducing casualties. Based on the research on the quantitative of panic coefficient and the calibration of initial desired speed, this research has constructed the revised social force model which could accurately simulate the post-earthquake evacuation process of the crowed. Using the revised social force model, the effects of classroom layout, seismic intensity, the number of safeties exits and the number of students in the classroom on the evacuation efficiency are further studied. And then, according to the research results, the classroom layout has been further optimized to improve the students’ earthquake emergency evacuation efficiency. These research results are scientific bases for the targeted earthquake emergency safety education and the design of classroom layout.

This article focuses on the rapid assessment of possible numbers of casualties after an earthquake. We use data on earthquake casualties to study and verify the relationship between casualties and building types, building anti-lethal levels, seismic intensity and other influencing factors. A rapid assessment method for casualties based on buildings’ anti-lethal level is proposed. The method first uses the anti-lethal level and proportions of various types of buildings to obtain the overall anti-lethal level of the study area. Second, according to historical casualty data, the casualty rate of each intensity in the study area is obtained. On this basis, a rapid assessment matrix of casualties is constructed based on the overall anti-lethal level of the study area and the casualty rate of each intensity. Finally, we make a rapid assessment of casualties based on a matrix model. Through the verification of 7 selected historical earthquakes, we find that the data on casualties obtained based on this method are close to the actual number of casualties. The resulting casualty parameters are described against the overall casualty data from several historical earthquakes in China, and the error rates of earthquakes in Lancang-Gengma, Yushu, Lushan Min county-Zhang county and Changning are 4.1%, 25.27%, 12.75%, 2% and 7.69%, respectively. The error is relatively small, within 50%, and a more accurate evaluation result is obtained. Our findings illustrate the effectiveness and practicability of a rapid assessment method for casualties based on anti-lethal levels.

Building damage after seismic disasters is one of the most vital factors threatening people's lives. The seismic vulnerability of buildings over large scales at the regional or country level is a key parameter for the mitigation of seismic disaster risk and rapid assessment of casualties after seismic events. To acquire the seismic vulnerability of buildings over large areas, a machine learning method based on mid-resolution satellite optical images is proposed. Taking field-investigated building vulnerability at the satellite pixel scale as a reference, the 15 most correlated features are calculated based on VIIRS nighttime light, MODIS vegetation index and surface reflectance, and texture data from Landsat-8 OLI surface reflectance products. Taking Yancheng, Jiangsu Province, China, as the study area, where 401 pixel-level seismic vulnerabilities (PLSVs) of the building environment are acquired based on field investigations, support vector regression (SVR) and random forest (RF) models are proposed using the 15 features calculated from satellite optical images. The results show that the proposed method can be used to estimate the PLSV with a root mean square error of approximately 0.1, with the PLSV normalized between 0 and 1. The machine learning model proposed in this study has a better accuracy for PLSV estimation than spatial interpolation.

Identifying building function type (BFT) is vital for many studies and applications, such as urban planning, disaster risk assessment and management, and traffic control. Traditional remote sensing methods are commonly used for land use/cover classification, but they have some limitations in BFT identification. Considering that the dynamic variations of social sensing mobile signaling (MS) data at diurnal and daily scales are directly related to BFT, in this paper, we propose a method to infer BFT using MS data obtained from mobile devices. First, based on the different patterns of population dynamics within different building types, we propose a BFT classification scheme with five categories: residential (R), working (W), entertainment (E), visiting (V), and hospital (H). Then, a random forest (RF) classification model is constructed based on two days (one workday and one weekend) of MS data with a temporal resolution of one hour to identify the BFT. According to the cross-validation method, the overall classification accuracy is 84.89%, and the Kappa coefficient is 0.78. Applying the MS data-constructed RF model to the central areas of Beijing Dongcheng and Xicheng Districts, the overall detection rate is 97.35%. In addition, to verify the feasibility of the MS data, the Sentinel-2 (S2) remote sensing data are used for comparison, with a classification accuracy of 73.33%. The better performance of the MS method shows its excellent potential for BFT identification, as the spatial and temporal population dynamics reviewed based on MS data are more correlated with BFT than geometric or spectral features in remote sensing images. This is an innovative attempt to identify BFT with MS data, and such a method compensates for the scarcity of BFT studies driven by population dynamics. Overall, in this study, we show the feasibility of using time series MS data to identify BFT and we provide a new path for building function mapping at large scales.

Introduction: Buildings that collapse or are damaged by earthquakes are responsible for the majority of earthquake-related casualties. High-precision building data are the key to improving the accuracy of risk assessments of earthquake disaster loss. Many countries and regions have also proposed varying regional building exposure models, but most of these models are still based on administrative-level (city or county) statistical data; furthermore, they cannot accurately reflect the differences among buildings in different towns or villages. Methods: Although field investigation-based "township to township" methods can obtain more accurate building inventory data, considering costs and timeliness, remote sensing and other diverse data should be combined to acquire building data. Based on the field survey data of three cities in shanxi Province, combined with Global Human Settlement Layer (GHSL) data, this study is conducted on building inventory data. Data regarding the proportion of each building type and corresponding lethality level in each township are obtained based on the classification of building height, and the overall lethality level at the building level and township level is calculated on this basis. Results: The fitting results between the calculated results and the field survey results are good, the error is within 0.15, and the fitting R2 values of Xian, Baoji and Ankang are 0.6552, 0.5788 and 0.5937, respectively. Therefore, an earthquake disaster loss risk assessment is conducted based on the building level. Discussion: The findings indicate that the risk of casualties caused by the same building type can vary by city. Generally, the areas with high disaster loss risk in the three cities are distributed mainly in urban areas; the disaster loss risk in the newly built areas of each city is relatively low. According to the quantitative assessment results for each city, Xi'an has the highest loss risk, while Baoji and Ankang have the same loss risk. Based on the method constructed in this paper, we can realize the quantitative assessment of earthquake disaster loss risk at the building level to better target pre-earthquake emergency preparation and post-earthquake auxiliary decision-making. [ABSTRACT FROM AUTHOR]