Search

The accuracy of rapid earthquake assessment and the emergency assessment system for earthquake-induced damages could be substantially enhanced if the casualties triggered by earthquake-induced geological disasters, such as landslides, are subjected to comprehensive scientific evaluation. However, no credible solution for this purpose has been formulated yet. This study suggests a three-step rapid assessment method designed for earthquake-induced landslide casualties based on the GIS and an associated logistic regression model, as follows: (1) Partition of the region to be evaluated as a 1 km × 1 km grid in the GIS, with assignment of a certain amount of population to each of the grid cells as its population attribute. (2) Calculation of the death rate for each grid cell based upon its earthquake-induced landslide susceptibility attribute using the logistic regression model. (3) The earthquake-induced landslide casualties are first determined for each of the kilometer grid cells, and then for the entire region under evaluation. The proposed method was implemented to test the assessment of earthquake-induced landslide casualties in three earthquake-stricken regions. The study reveals the feasibility of the extensibility and applicability of the proposed rapid assessment method for earthquake-induced landslide casualties, and its suitability for similar assessments and calculations of other regions.

Considering the significant influence of clouds on thermal infrared (TIR) data sea surface temperature (SST) retrieval, this study focuses on the reduction of cloud influence on Moderate Resolution Imaging Spectroradiometer (MODIS) longwave SST retrieval. First, the quality level (QL) 3 MODIS SSTs are classified into cloudy or clear-sky pixels based on MODIS cloud mask products. The cloudy SST pixels, flagged as confident cloudy or probably cloudy in the cloud masks, are further classified into three groups according to their associated daytime cloud-top and optical properties. Taking the SSTs measured by 11 buoys over one year as the reference data, those three groups of cloudy SSTs are significantly underestimated, with biases of -33.45 °C, -6.35 °C, and -4.72 °C. The QL 3 clear-sky SSTs are identified as probably and confident clear in the cloud masks and are not influenced by clouds, with a bias of nearly 0 °C. Then, three support vector regression (SVR) models are individually proposed for the three groups of cloudy SSTs. Taking the cloud-top and optical parameters as inputs of the proposed SVR models, we can obtain SSTs with a bias of 0 °C and a root-mean-square error (RMSE) of less than 1.6 °C for the three groups of cloudy SSTs. The method proposed in this study shows the potential for TIR SST estimation under some cloudy conditions using satellite remote sensing cloud products. Considering the RMSE of 0.4 °C in operational sea surface temperature and sea ice analysis, further study is needed before actual applying the method proposed in this study.

Strengthening the study of trapped personnel distribution in an earthquake not only ensures the efficiency and orderliness of earthquake rescue but also maximizes the survival probability of trapped people and reduces casualties. Through distinguishing the use functions of buildings, this study constructed an assessment model of people trapped in a collapsed single building due to an earthquake and evaluated the risk of individuals trapped in Xichang area, China. The results showed that the risk level of people trapped in collapsed buildings in Xichang area is high. Under the influence of seismic intensity IХ, the proportion of people trapped in collapsed buildings has reached approximately 1–2% of the total regional population. The risk level of trapped people in the daytime and at nighttime caused by the same earthquake scenario is significantly different. The number of potentially trapped people at night is nearly two times greater than that during the day. The low seismic performance of buildings is the most important factor causing the high risk of people trapped. The use function of buildings also has an important effect on the risk assessment of the trapped population distribution. Some suggestions are provided for the local risk reduction of people trapped in earthquakes.

Dynamic monitoring of building environments is essential for observing rural land changes and socio-economic development, especially in agricultural countries, such as China. Rapid and accurate building extraction and floor area estimation at the village level are vital for the overall planning of rural development and intensive land use and the “beautiful countryside” construction policy in China. Traditional in situ field surveys are an effective way to collect building information but are time-consuming and labor-intensive. Moreover, rural buildings are usually covered by vegetation and trees, leading to incomplete boundaries. This paper proposes a comprehensive method to perform village-level homestead area estimation by combining unmanned aerial vehicle (UAV) photogrammetry and deep learning technology. First, to tackle the problem of complex surface feature scenes in remote sensing images, we proposed a novel Efficient Deep-wise Spatial Attention Network (EDSANet), which uses dual attention extraction and attention feature refinement to aggregate multi-level semantics and enhance the accuracy of building extraction, especially for high-spatial-resolution imagery. Qualitative and quantitative experiments were conducted with the newly built dataset (named the rural Weinan building dataset) with different deep learning networks to examine the performance of the EDSANet model in the task of rural building extraction. Then, the number of floors of each building was estimated using the normalized digital surface model (nDSM) generated from UAV oblique photogrammetry. The floor area of the entire village was rapidly calculated by multiplying the area of each building in the village by the number of floors. The case study was conducted in Helan village, Shannxi province, China. The results show that the overall accuracy of the building extraction from UAV images with the EDSANet model was 0.939 and that the precision reached 0.949. The buildings in Helan village primarily have two stories, and their total floor area is 3.1 × 105 m2. The field survey results verified that the accuracy of the nDSM model was 0.94; the RMSE was 0.243. The proposed workflow and experimental results highlight the potential of UAV oblique photogrammetry and deep learning for rapid and efficient village-level building extraction and floor area estimation in China, as well as worldwide.

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.

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.

After an earthquake, the goal of emergency rescue work is to minimize the number of casualties and save lives. The most important initial task is the rapid assessment of the number of earthquake casualties. At present, the rapid assessment of casualties relies primarily on census data rather than real-time population data in an earthquake area. Features of the census data, such as their low accuracy and time lag, can easily cause large errors in the assessment result; thus, the assessment result cannot always reflect the actual situation in an earthquake area. In this paper, we use phone signal data to construct a population density model and estimate the real-time population in a seismic area. The results show that the estimated population is consistent with the actual data in the study area. Finally, we obtain a real-time population distribution map of the study area. The real-time distribution of the population is consistent with the actual situation based on the economic and social development of the study area. The population in an urban area is relatively dense, whereas that in a rural area is less dense. The results show that phone signal data can play a useful role in estimating the real-time population when an earthquake occurs and can be used to support earthquake emergency rescue work.

To rapidly identify earthquake damage areas after earthquakes occur, a method based on Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL) images with nearly constant c...

China is one of the most earthquake-prone countries in the world. The highest-priority mission after an earthquake is to rapidly save lives, and to minimize the loss of life. Rapid judgment of the trapped personnel location is the important basis to identify the emergency supply demands and carry out the search and rescue work after the earthquake. Through analyzing the main influencing factors, we constructed an assessment model of people trapped in collapsed buildings caused by the earthquakes. The accuracy of the estimation results from the model was then tested against the actual investigation data in 2014 Ludian earthquake-hit area. Results showed that, the trapped personnel distribution assessed by this model is generally concordant with that obtained by the actual survey in Ludian earthquake. The grid-based assessment of people trapped in earthquakes can meet the requirements of key search and rescue zone identification and rescue forces allocation in the early stage of earthquake emergency. Although there were some limitations in the study, it offers a simple and rapid approach for assessing the trapped people losses based on basic empirical data. The approach can be further improved to provide more information and suggestions for earthquake emergency search and rescue.

Grey microstructure of microbanding in thin sections of stalagmite T9501 from Shihua Cave, Beijing was studied. The types of grey and the morphologic features of organic material were classified. The signification of microbanding and annual layer was discussed. The standard distinguishing the false annual layer and the multi-year layer is set up, which provides the theoretic fundaments for paleoclimatic study.