Disaster Scene Research Articles

A disaster scene simulation system in 3D for oil transmission stations: Design and implementation

The disaster scene in three-dimensional (3D) plays a crucial role in disaster emergency management and risk communication of oil transmission stations. However, existing research for the disaster scene mainly focuses on reproducing the disaster environment and rarely predicts the damage state of the disaster-affected object. This paper proposes an object-oriented modeling method that utilizes a multilevel decomposition pattern for disaster scenes. This method combines earthquake vulnerability assessment with 3D visualization technology to predict and characterize the damage state of critical infrastructure in oil transmission stations. To enhance earthquake risk perception, a simulation system is designed and developed, which allows for the construction of virtual scenes and quick simulation of disaster scenes in 3D. The case application shows that the system improves the 3D modeling efficiency of disaster scenes and enhances public awareness of earthquake risks. The simulation system can provide technical support for seismic mitigation planning and emergency management decision-making at oil transmission stations and has good application prospects.

Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review

The occurrence of any kind of natural disaster will eventually lead to the loss of life and property. Countries where such disasters occur make every effort to monitor such disasters and aid as quickly as possible. However, in some cases, a rescue cannot be sent because no information is available to initiate any type of rescue operation. This is usually because common disaster management systems (DMS) use on board or ground networks to route information from the disaster scene to rescue headquarters (HQ), which in most cases cannot provide the information efficiently. One effective approach is to use satellites in conjunction with existing air-to-ground systems. This study provides a comprehensive and systematic overview of the complexities of the space-air-ground integrated network (SAGIN) in disaster management applications, including different architectures and protocols. The main rationale behind this review is to provide an extensive analysis of existing disaster management systems that are making use of SAGIN. This paper also presents the taxonomy for disaster management systems and challenges. Moreover, this research work also highlights open research issues and challenges for any type of disaster scenario. Our results indicate that several challenges are faced by disaster management systems such as hardware-based challenges, network-based characteristics and communication protocols related challenges, availability and accuracy of imagery data, and security and privacy issues.

First Responder Virtual Reality Simulator to train and assess emergency personnel for mass casualty response.

As mass casualty incidents continue to escalate in the United States, we must improve frontline responder performance to increase the odds of victim survival. In this article, we describe the First Responder Virtual Reality Simulator, a high-fidelity, fully immersive, automated, programmable virtual reality (VR) simulation designed to train frontline responders to treat and triage victims of mass casualty incidents. First responder trainees don a wireless VR head-mounted display linked to a compatible desktop computer. Trainees see and hear autonomous, interactive victims who are programmed to simulate individuals with injuries consistent with an explosion in an underground space. Armed with a virtual medical kit, responders are tasked with triaging and treating the victims on the scene. The VR environment can be made more challenging by increasing the environmental chaos, adding patients, or increasing the acuity of patient injuries. The VR platform tracks and records their performance as they navigate the disaster scene. Output from the system provides feedback to participants on their performance. Eventually, we hope that the First Responder system will serve both as an effective replacement for expensive conventional training methods as well as a safe and efficient platform for research on current triage protocols.

Effects of Conformity Bias Caused by Evacuation Order on Risk Evaluation

We have found that various cognitive biases would interfere with evacuation behavior during a disaster, and this have caused many casualties. In this study, we focused on the conformity bias, one of the cognitive biases, which is introduced by evacuation order sentences with information about other residences’ behavior, to facilitate evacuation behavior. In the experiment, we presented the evacuation order sentences, and pictures which depict the scene of disaster, or normal situation. Participants evaluated the degree of risk for the picture, and degree of necessity of evacuation from the scene. Our results demonstrated that the conformity bias would facilitate evacuation especially for the low-risk situation. In addition, we found that judgments of the need for evacuation can be predicted by the rating of the degree of danger, disaster experience, sense of crisis about disasters, and the other-personality-orientations of the participants.

A Deep Learning Semantic Segmentation Method for Landslide Scene Based on Transformer Architecture

Semantic segmentation technology based on deep learning has developed rapidly. It is widely used in remote sensing image recognition, but is rarely used in natural disaster scenes, especially in landslide disasters. After a landslide disaster occurs, it is necessary to quickly carry out rescue and ecological restoration work, using satellite data or aerial photography data to quickly analyze the landslide area. However, the precise location and area estimation of the landslide area is still a difficult problem. Therefore, we propose a deep learning semantic segmentation method based on Encoder-Decoder architecture for landslide recognition, called the Separable Channel Attention Network (SCANet). The SCANet consists of a Poolformer encoder and a Separable Channel Attention Feature Pyramid Network (SCA-FPN) decoder. Firstly, the Poolformer can extract global semantic information at different levels with the help of transformer architecture, and it greatly reduces computational complexity of the network by using pooling operations instead of a self-attention mechanism. Secondly, the SCA-FPN we designed can fuse multi-scale semantic information and complete pixel-level prediction of remote sensing images. Without bells and whistles, our proposed SCANet outperformed the mainstream semantic segmentation networks with fewer model parameters on our self-built landslide dataset. The mIoU scores of SCANet are 1.95% higher than ResNet50-Unet, especially.

Research on the construction scheme of the emergency communication link in electric power system without public network signals

Recently, natural disasters have occurred frequently which usually cause great damage to communication links. Emergency communication technology is the key technology to connecting the emergency site and the command center under the condition of communication system collapse, and it plays an extremely important role in the process of emergency rescue. Based on the disaster scene of the electric power system, this paper fully considers the extreme conditions of power outages, network disconnection, and traffic interruption at the emergency site, and proposes an emergency communication link construction scheme. After the pilot test, the scheme proposed in this paper can quickly restore the network signal coverage of the emergency site, and establish the communication link between the command center and the emergency site which can provide a reliable communication guarantee for power emergency rescue operations.

Delphi Study on the Management System and Occupational Environment Improvement of SCBA and Charging Rooms

The purpose of this study is to evaluate and suggest priorities for policy implementation to improve the management system and environment of self-contained breathing apparatus (SCBA) and charging rooms used by firefighters. The study included 15 experts and three interviews, and two modified Delphi surveys were conducted. Based on the results of previous research and expert interviews, the management system and environmental improvement plan for the SCBA and charging rooms were first derived. The improvement plan was presented in five areas, including resources, the SCBA and charging room management system, SCBA related work, SCBA charging room related work, and laws and regulations. The priority of policy implementation was derived by simultaneously analyzing the importance and practicality of the policy. Consequently, the first priority was to add contents to the manual for measures to prevent unwanted drinking of the Han River water and related symptoms for rescue workers during hardship rescue. The second priority was to specify the duty of decontamination in the disaster scene standard handling procedure and disaster scene return procedure stage. The next step was to investigate by conducting education on the risk of health effects of pollutants at disaster scenes. The results of this study can be utilized in the implementation of mid- to long-term fire policies for the systematic operation and establishment of a systematic management system for firefighters’ personal protective equipment, such as SCBA and charging rooms.

Three-dimensional virtual representation for the whole process of dam-break floods from a geospatial storytelling perspective

ABSTRACT The objective of disaster scenes is to share location-based risk information to a large audience in an effective and intuitive way. However, current studies on three-dimensional (3D) representation for dam-break floods have the following limitations: (1) they are lacking a reasonable logic to organize the whole process of dam-break floods, (2) they present information in a way that cannot be easily understood by laypersons. Geospatial storytelling helps to create exciting experiences and to explain complex relationships of geospatial phenomena. This article proposes a three-dimensional virtual representation method for the whole process of dam-break floods from a geospatial storytelling perspective. The creation of a storyline and a storytelling-oriented representation of dam-break floods are discussed in detail. Finally, a prototype system based on WebGL is developed to conduct an experiment analysis. The results of the experiment show that the proposed method can effectively support 3D representation of the spatiotemporal process of dam-break floods. Furthermore, the statistical results indicate that the storytelling is useful for assisting participants in understanding the occurrence and development of dam-break floods, and is applicable to the popularization of disaster science for the general public.

A Three-Dimensional Visualization and Optimization Method of Landslide Disaster Scenes Guided by Knowledge

The rapid acquisition of deposit volume information and dynamic modeling, as well as the visualization of disaster scenes, have great significance for the sharing of landslide information and the management of emergency rescue. However, existing methods have shortcomings, such as a long and costly deposit volume acquisition cycle, lack of knowledge and guidance, complex operations for scene modeling expression, and low scene rendering efficiency. Therefore, this paper focuses on the study of a three-dimensional visualization and optimization method for landslide disaster scenes guided by knowledge, and discusses key technologies such as the rapid acquisition of landslide deposit volume information based on three-dimensional reconstruction, the knowledge-guided dynamic modeling visualization of disaster scenes, and scene optimization considering visual significance. The prototype systems are developed and used in a case experiment and analysis. The experimental results show that the proposed method can quickly obtain the deposit volume, and the results are equivalent to ContextCapture, Metashape, and Pix4Dmapper software. The method realizes the dynamic visualization of the whole disaster process, provides rich information, achieves high readability, and improves the efficiency of scene rendering, with a stable average rendering frame rate of more than 80 frames/second.

Survival communication under uncertainty and complexity: A review

This study aims to propose the concept of survival communication in arbitrating the comprehension and interpretation of risk information during cascading disasters under uncertainty and complexity. A broad content review and synthesis were conducted based on the literature on notions of risk and crisis communication published before 2015. A concept of survival and communication was framed and discussed through the review of the literature and lessons from two cascading disasters. During cascading disasters, the public may be exposed to massive general risk information through various channels. The information often may not be on time or limited due to the cascading effect, including natural disasters with sequential events, damaged social infrastructure, insufficient information sources, and insufficient expertise knowledge. Accessing and understanding the risk information is key to supporting decision-making on time and disseminating information to protect people by themselves, particularly in a small-scale community. The public may need different information depending on a situation that could help deal with emergencies and decision making, support understanding and interpreting risk information by their language, and apply their knowledge to emergency activities. Based on that, this study proposes a concept of survival communication that is defined as a system that is organized for supporting multi-way communication for disaster risk and emergency information to protect individuals' lives during cascading disasters until the arrival of professional first responders at the disaster scene. Proposed survival communication could enhance disaster literacy on the basis of target-oriented risk information, experiences, wisdom, and local knowledge and reduce risks containing uncertainty and complexity of cascading disasters.

LIDAR-BASED MULTI-SENSOR MODULE DEVELOPMENT AND ASSESSMENT OF APPLICABILITY FOR DAMAGE INVESTIGATION OF INDOOR DISASTER

Abstract. As the risk of disaster scenes increases, the number of cases of acquiring disaster scenes information using unmanned robots is increasing. Because unmanned robots can be remotely controlled, sensors such as LiDAR and optical cameras are installed so that investigators can safely observe disaster scenes and acquire information. In particular, the information that can be acquired is different depending on the sensor characteristics, and a sensor module suitable for the purpose is being developed. Accordingly, the National Disaster Management research Institute(NDMI) also developed a investigation robot capable of acquiring information on disaster sites independently. Based on 3D point cloud data, we developed a multi-sensor module and SLAM algorithm customized to the investigation robot to collect quantitative information on the damage situation. To test the performance of the independently developed multi-sensor module, SLAM mapping was performed in a disaster building reproduced like a disaster scene, and various SLAM algorithms and distance comparison were performed. As a result, PackSLAM developed in this study showed the lowest error. In the future, to increase applicability at disaster sites, more precise experiments will be conducted by conducted by establishing a rough terrain environment.

SMALL UAVS-BASED DISASTER DAMAGE INVESTIGATION: FOCUSED ON FLOODING DISASTER AND CHEMICAL ACCIDENT

Abstract. Disasters that occur in recent years are linked to various factors and occur in a chain, and the scale is also increasing. In order to solve the more complex and serious disaster problem, it is necessary to expand the affect scope of the disaster and to acquire and manage information by integrating and operating various sensors. With the introduction of UAVs for disaster work, various studies such as investigation of the affected area, rescue of survivors, and establishment of emergency communication networks are being conducted. This is useful for strengthening the field of disaster management because it is very effective in terms of time and human resources. In addition, it is possible to acquire high-resolution images, and by using the 3D point cloud data and ortho-mosaic imagery generation, it is possible to produce a disaster scene close to the real world. Therefore, if UAV information can be linked to disaster types and characteristics prior to supporting disaster damage investigation, it is possible to establish a response method according to the disaster damage situation and help decision-making. In order to examine the applicability of UAV technology for actual disaster sites, this study conducted data collection and analysis of damaged areas targeting sites damaged by heavy rain and chemical accidents. In the heavy rain damage site, flooding damage occurred in the ginseng cultivation patches due to river overflow caused by the torrential rain, and the damaged area and lots were investigated. The site of the chemical accident was damaged by hydrochloric acid gas leaking into the atmosphere, and the surrounding forest, crops, and residential facilities were identified and the affected area was calculated. As a result of investigating the damage from heavy rain and chemical accidents using UAV, it was possible to quickly collect data on a wide disaster site. In addition, it was easy to calculate the damaged area based on ortho-imagery, and the limitations of spatial analysis were reduced, so that it could be used more specifically and efficiently to the disaster site.

An integrated framework for managing fire resilience of metro station system: Identification, assessment, and optimization

As a sociotechnical infrastructure system constituting equipment and facilities, operational staff, and passengers, the metro station system (MSS) is under fire threat from varying causes. Although fires occupy a high portion of all hazards occurring in the MSS, how the MSS in operation can systematically cope with fires has drawn scant attention. To improve MSSs’ poor performance across the fire management cycle, the concept of fire resilience is proposed based on the system resilience theory. The disaster scene analysis, TOSE approach, and modified TOPSIS method are combined to identify fire resilience indexes. Then, a Bayesian network is developed to assess fire resilience and reveal critical causal chain in fire scenes. Furthermore, sensitivity analysis and dynamic Bayesian network with critical importance analysis are adopted to formulate optimization strategies for different periods of operating life. The resulting framework is applied to Nanjing MSS, providing operational staff and decision makers with practical tools to engage in long-term resilient operation of MSS against fires within a clear manageable scope. The results indicate passengers’ escape skills and safety behaviors, security screening operations, equipment inspection and maintenance, and rescue service access are the prime factors resulting in low fire resilience; meanwhile, economic resource allocation should be prioritized for optimization initially, but optimization priorities should be transferred to the less controllable passengers’ escape skills and aging firefighting equipment as operating life increases. The integration of identification, assessment, and optimization methods can also be flexibly embedded into various infrastructure systems’ operation management processes to optimize disaster resilience continuously.

Designing a Model for Human Resource Optimization in the Red Crescent Society of Yazd

INTRODUCTION: Various studies have demonstrated that rescuers enjoy higher levels of self-efficacy and quality of life, as compared to ordinary people. Moreover, the constant and sometimes prolonged presence of Red Crescent Society rescuers at the scene of disasters makes them more prone to the emotional impact of crises, in comparison with ordinary rescuers. METHODS: The present study was conducted based on a causal-comparative (ex-post facto) design. The statistical population included 200 cases, and the sample size was equal to 120 (60 rescuers and 60 non-rescuers who were selected via the random sampling method). Scherrer self-efficacy questionnaire (1983), quality of work life questionnaire (1973), and Coopersmith self-confidence questionnaire (1967) were used to collect data, and data analysis was performed by analysis of variance. FINDINGS: As evidenced by the obtained results, the highest and lowest percentages of rescuers were in the age group of 20-30 (51.92%) and under 20 years (5.77%), respectively. In terms of education, the highest and lowest percentages of rescuers had a bachelor’s degree (61.7%) and high school education (1.7%). Furthermore, the highest and lowest percentages of non-rescuers were permanent (63.73%) and contract employees (1.7%), respectively. CONCLUSION: The results pointed out that rescuers enjoyed higher levels of self-confidence, quality of work life, and self-efficacy, as compared to non-rescuers.

Comparison of Rescue Workers in the Kermanshah Earthquake and Non-rescuers in the Red Crescent Society in Terms of Self-efficacy, Quality of Work Life, and Self-confidence

INTRODUCTION: Various studies have demonstrated that rescuers enjoy higher levels of self-efficacy and quality of life, as compared to ordinary people. Moreover, the constant and sometimes prolonged presence of Red Crescent Society rescuers at the scene of disasters makes them more prone to the emotional impact of crises, in comparison with ordinary rescuers. METHODS: The present study was conducted based on a causal-comparative (ex-post facto) design. The statistical population included 200 cases, and the sample size was equal to 120 (60 rescuers and 60 non-rescuers who were selected via the random sampling method). Scherrer self-efficacy questionnaire (1983), quality of work life questionnaire (1973), and Coopersmith self-confidence questionnaire (1967) were used to collect data, and data analysis was performed by analysis of variance. FINDINGS: As evidenced by the obtained results, the highest and lowest percentages of rescuers were in the age group of 20-30 (51.92%) and under 20 years (5.77%), respectively. In terms of education, the highest and lowest percentages of rescuers had a bachelor’s degree (61.7%) and high school education (1.7%). Furthermore, the highest and lowest percentages of non-rescuers were permanent (63.73%) and contract employees (1.7%), respectively. CONCLUSION: The results pointed out that rescuers enjoyed higher levels of self-confidence, quality of work life, and self-efficacy, as compared to non-rescuers.

A gaze‐based interaction method for large‐scale and large‐space disaster scenes within mobile virtual reality

AbstractA three‐dimensional (3D) visualization of disaster scenes based on mobile virtual reality (VR) can improve the application scenarios and emergency service capabilities of traditional 3D visualization of disaster scenes. Because a smartphone needs to be placed into a mobile head‐mounted display, conventional touch scene interaction cannot be used by mobile VR, and the user's gaze usually serves as the default scene interaction method. However, the existing gaze‐based interaction methods for mobile VR scenes are passive scene interaction methods and cannot meet the basic interaction requirement for actively roaming through and exploring large‐scale and large‐space disaster scenes. Therefore, this study focuses on gaze‐based mobile VR interactions to satisfy the various interaction requirements of large‐scale and large‐space disaster scenes. First, a dynamic user interface (UI) generation method for gaze interaction in large‐scale and large‐space disaster scenes is proposed to solve the problem of the active exploration of mobile VR disaster scenes. Second, disaster scene exploration and disaster information query methods based on a dynamic UI and gaze are proposed. Finally, using a flood disaster as an example, a prototype system and associated experiments are discussed. As indicated by the experimental results, the gaze‐based mobile VR interaction methods addressed in this study can effectively support users in actively roaming through and exploring large‐scale and large‐space disaster scenes, disaster simulation analysis, and the interactive querying of disaster information within mobile VR, making the effective interaction of mobile VR disaster scenes possible.

What do emergency services and authorities need from society to better handle disasters?

Over the last 20 years, disaster events have been increasing with 3.25 billion people affected. The public are not only affected by these incidents but also, they are the first on the disaster scene. To fully utilize the public's potential and define their role in assisting in disaster management, we need to hear the voice of the main responsible for handling a disaster, the authorities, and emergency personnel. Therefore, this paper aims to identify and prioritize their needs through conducting a survey and interviews with members of authorities and emergency organizations in several European Union countries, namely; France, Israel, Italy, Norway, Romania, Spain, and Sweden. The highest-ranked needs identified are getting credible information as soon as possible from the disaster scene as well as following authorities' recommendations. Additionally, this study identifies some barriers associated with the engagement of society in disaster management. An example of such a barrier is that citizens could hurt themselves or others and hamper the intervention efforts through their interference. Despite the barriers, the emergency organizations are in favor of engaging the society in the response and recovery phases. The identified needs and barriers help to define procedures and policies that can improve the engagement of citizens and consequently, social resilience.

Deep Learning With Weak Supervision for Disaster Scene Description in Low-Altitude Imagery

Pictures or videos captured from a low-altitude aircraft or an unmanned aerial vehicle are a fast and cost-effective way to survey the affected scene for the quick and precise assessment of a catastrophic event’s impacts and damages. Using advanced techniques, such as deep learning, it is now possible to automate the description of disaster scenes and identify features in captured images or recorded videos to gain situational awareness. However, building a large-scale, high-quality dataset with annotated disaster-related features for supervised model training is time-consuming and costly. In this article, we propose a weakly supervised approach to train a deep neural network on low-altitude imagery with highly imbalanced and noisy crowd-sourced labels. We further make use of the rich spatiotemporal data obtained from the pictures and its sequence information to enhance the model’s performance during training via label propagation. Our approach achieves the highest score among all the submitted runs in the TRECVID2020 Disaster Scene Description and Indexing (DSDI) Challenge, indicating its superior capabilities in retrieving disaster-related video clips compared to other proposed methods.

Noncontact Detection of Cardiopulmonary Activities of Trapped Humans in Rescue Relief Events

Equipment for the correct identification of living objects entrapped under heavy debris is generally purpose-built, is costly, must be operated by highly trained professionals and is not readily available in a catastrophic event. A more readily available solution for improving the time-to-rescue ratio and logistics issues can be provided with smartphones which, equipped with software to find signs of life, are readily available at any disaster scene. This paper examines whether cardiac and pulmonary-related activities of living objects can provide acceptably accurate readings from a non-contact detection method. Laboratory experiments were conducted with Doppler radar at a 2.4GHz frequency spectrum similar to smartphone-like devices, with empirical results demonstrating that human vital signs can be clearly identified when using smartphones for non-contact detection of living objects entrapped under debris. Experiments also simulated the psychogenic tremors likely to be experienced by individuals while operating the sensor-equipped devices under crisis conditions. The results show a clear relationship between the wavelength of pulmonary and blood vessel activities and the distance between the trapped human and the sensor in various conditions. The article also reports the design of a pseudo learning algorithm for model-based anomaly detection in time series to detect vital signs during normal and abnormal ventilation based on cardiopulmonary clinical records and datasets. This work significantly contributes to the existing body of research on timely rescue during disaster events.

Consciousness Detection on Injured Simulated Patients Using Manual and Automatic Classification via Visible and Infrared Imaging.

In a disaster scene, triage is a key principle for effectively rescuing injured people according to severity level. One main parameter of the used triage algorithm is the patient’s consciousness. Unmanned aerial vehicles (UAV) have been investigated toward (semi-)automatic triage. In addition to vital parameters, such as heart and respiratory rate, UAVs should detect victims’ mobility and consciousness from the video data. This paper presents an algorithm combining deep learning with image processing techniques to detect human bodies for further (un)consciousness classification. The algorithm was tested in a 20-subject group in an outside environment with static (RGB and thermal) cameras where participants performed different limb movements in different body positions and angles between the cameras and the bodies’ longitudinal axis. The results verified that the algorithm performed better in RGB. For the most probable case of 0 degrees, RGB data obtained the following results: Mathews correlation coefficient (MMC) of 0.943, F1-score of 0.951, and precision-recall area under curve AUC (PRC) score of 0.968. For the thermal data, the MMC was 0.913, F1-score averaged 0.923, and AUC (PRC) was 0.960. Overall, the algorithm may be promising along with others for a complete contactless triage assessment in disaster events during day and night.