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Accurate monitoring of mining three-dimensional deformation plays an important role in analyzing the subsidence mechanism and assessing potential geological hazards in the mining area. This paper combines TerraSAR-X and RADARSAT-2 heterogeneous SAR images employing MSBAS technology to obtain the three-dimensional time-series of deformation of the 132 610 working face in the Fengfeng mine from December 2015 to February 2016. The monitoring results show that the maximum surface subsidence and the subsidence rate during the monitoring period were –243 mm and 4.42 mm/day, and the maximum east-west horizontal movement was –80~30 mm. The surface subsidence basin is in the active stage, which is prone to pose a threat to the safety of the surrounding structures. Comparison with the leveling data shows that the root-mean-square deviation of the vertical deformation obtained by MSBAS technology is 10.7 mm, and the accuracy can reach the centimeter level. Meanwhile, based on the vertical deformation obtained by MSBAS technology, combined with the proportionality relationship between horizontal movement and tilt, the north-south horizontal movement was estimated as –80~70 mm, which provided a solution for obtaining the north-south horizontal movement of the mining area by InSAR. Analysis of the three-dimensional time-series of the surface deformation at the main sections shows that the surface deformation features during the mining process of the 132610 working face match well with the general model of mining subsidence, but the working face inclination angle has an influence on the surface deformation. [ABSTRACT FROM AUTHOR]

After the occurrence of various types of disasters, including natural disasters and man-made damage, aid workers need accurate and timely data, such as the damage status of buildings, in order to take effective measures for rescue. So as to solve this problem, this paper researches and designs a building damage classification system based on machine learning. The damage assessment system consists of two network models (building extraction network and damage classification network). This article analyzes and designs the structure of each network model, and discusses the principles related to computer vision in machine learning. Buildings in satellite images are segmented through Siamese Convolutional Neural Network, the BottleNeck Module and Feature Pyramid Network are used in the damage classification assessment network to detect damage to buildings in sub-temporal remote sensing images. Subsequently, the model was trained and tested on different disaster events on the xBD dataset. The results show that the building damage detection system based on Siamese-CNN achieves good detection accuracy, and the system has the advantages of simple operation, good timeliness and low resource consumption, and can well meet the needs of disaster assessment. [ABSTRACT FROM AUTHOR]

With the continuous development of the oblique photography technique, it has been used more and more widely in the field of geological disasters. It can quickly obtain the three-dimensional (3D) real scene model of dangerous mountainous areas under the premise of ensuring the safety of personnel while restoring the real geographic information as much as possible. However, geological disaster areas are often accompanied by many adverse factors such as cliffs and dense vegetation. Based on this, the paper introduced the flight line design of oblique photogrammetry, analyzed the multi-platform data fusion processing, studied the multi-period data dynamic evaluation technology and proposed the application methods of data acquisition, early warning, disaster assessment and decision management suitable for geological disaster identification through the analysis of actual cases, which will help geologists to plan and control geological work more scientifically and rationally, improve work efficiency and reduce the potential personnel safety hazards in the process of geological survey, to offer technical support to the application of oblique photogrammetry in geological disaster identification and decision making and provide the scientific basis for personal and property safety protection and later-stage geological disaster management in disaster areas. [ABSTRACT FROM AUTHOR]

Disasters can devastate individuals and their properties, highlighting the importance of risk assessment to promote safety. Recently, deep learning techniques have shown the potential in identifying hazardous situations during disasters. Recognizing potentially dangerous objects in indoor environments can be essential for assisting individuals in responding appropriately to emergencies. In this article, we present an indoor-risk analysis framework for disasters based on deep learning. Our framework utilizes modern deep learning techniques to calculate an indoor risk rating based on dangerous objects' sizes, enabling comprehensive risk assessment of indoor environments during disasters. To that end, we use (Mask R-CNN) to identify hazardous indoor objects in disaster situations with 94% accuracy. By incorporating object size information, our framework offers a more nuanced and detailed risk assessment than previous approaches. Our proposed system provides a valuable tool for promoting ongoing safety improvement and enhancing indoor safety during natural disasters. [ABSTRACT FROM AUTHOR]

Abstract Rapid building damage assessment following an earthquake is important for humanitarian relief and disaster emergency responses. In February 2023, two magnitude-7.8 earthquakes struck Turkey in quick succession, impacting over 30 major cities across nearly 300 km. A quick and comprehensive understanding of the distribution of building damage is essential for efficiently deploying rescue forces during critical rescue periods. This article presents the training of a two-stage convolutional neural network called BDANet that integrated image features captured before and after the disaster to evaluate the extent of building damage in Islahiye. Based on high-resolution remote sensing data from WorldView2, BDANet used pre-disaster imagery to extract building outlines; the image features before and after the disaster were then combined to conduct building damage assessment. We optimized these results to improve the accuracy of building edges and analyzed the damage to each building, and used population distribution information to estimate the population count and urgency of rescue at different disaster levels. The results indicate that the building area in the Islahiye region was 156.92 ha, with an affected area of 26.60 ha. Severely damaged buildings accounted for 15.67% of the total building area in the affected areas. WorldPop population distribution data indicated approximately 253, 297, and 1,246 people in the collapsed, severely damaged, and lightly damaged areas, respectively. Accuracy verification showed that the BDANet model exhibited good performance in handling high-resolution images and can be used to directly assess building damage and provide rapid information for rescue operations in future disasters using model weights.

In the contexts of global climate change and the urbanization process, urban flooding poses significant challenges worldwide, necessitating effective rapid assessments to understand its impacts on various aspects of urban systems. This can be achieved through the collection and analysis of big data sources such as social media data. However, existing literature remains limited in terms of conducting a comprehensive disaster impact assessment leveraging social media data. This study employs mixed-methods research, a synergy of statistical analysis, machine learning algorithms, and geographical analysis to examine the impacts of urban flooding using the case of the 2020 Guangzhou rainstorm event. The result show that: (1) analyzing social media content enables monitoring of the development of disaster situations, with varied distributions of impact categories observed across different phases of the urban flood event; (2) a lexicon-based approach allows for tracking specific sentiment categories, revealing differential contributions to negative sentiments from various impact topics; (3) location information derived from social media texts can unveil the geographic distribution of impacted areas, and significant correlations are indicated between the waterlogging hotspots and four predisposing factors, namely precipitation, proportion of built-up surfaces, population density, and road density. Consequently, this study suggests that collecting and analyzing social media data is a reliable and feasible way of conducting rapid impact assessment for disasters. [ABSTRACT FROM AUTHOR]

Conducting landslide recognition research holds notable practical significance for disaster management. In response to the challenges posed by noise, information redundancy, and geometric distortions in single-orbit SAR imagery during landslide recognition, this study proposes a dual-polarization SAR image landslide recognition approach that combines ascending and descending time-series information while considering polarization channel details to enhance the accuracy of landslide identification. The results demonstrate notable improvements in landslide recognition accuracy using the ascending and descending fusion strategy compared to single-orbit data, with F1 scores increasing by 5.19% and 8.82% in Hokkaido and Papua New Guinea, respectively. Additionally, utilizing time-series imagery in Group 2 as opposed to using only pre- and post-event images in Group 4 leads to F1 score improvements of 6.94% and 9.23% in Hokkaido and Papua New Guinea, respectively, confirming the effectiveness of time-series information in enhancing landslide recognition accuracy. Furthermore, employing dual-polarization strategies in Group 4 relative to single-polarization Groups 5 and 6 results in peak F1 score increases of 7.46% and 12.07% in Hokkaido and Papua New Guinea, respectively, demonstrating the feasibility of dual-polarization strategies. However, due to limitations in Sentinel-1 imagery resolution and terrain complexities, omissions and false alarms may arise near landslide edges. The improvements achieved in this study hold critical implications for landslide disaster assessment and provide valuable insights for further enhancing landslide recognition capabilities. [ABSTRACT FROM AUTHOR]

Rapid building damage assessment following an earthquake is important for humanitarian relief and disaster emergency responses. In February 2023, two magnitude-7.8 earthquakes struck Turkey in quick succession, impacting over 30 major cities across nearly 300 km. A quick and comprehensive understanding of the distribution of building damage is essential for efficiently deploying rescue forces during critical rescue periods. This article presents the training of a two-stage convolutional neural network called BDANet that integrated image features captured before and after the disaster to evaluate the extent of building damage in Islahiye. Based on high-resolution remote sensing data from WorldView2, BDANet used pre-disaster imagery to extract building outlines; the image features before and after the disaster were then combined to conduct building damage assessment. We optimized these results to improve the accuracy of building edges and analyzed the damage to each building, and used population distribution information to estimate the population count and urgency of rescue at different disaster levels. The results indicate that the building area in the Islahiye region was 156.92 ha, with an affected area of 26.60 ha. Severely damaged buildings accounted for 15.67% of the total building area in the affected areas. WorldPop population distribution data indicated approximately 253, 297, and 1,246 people in the collapsed, severely damaged, and lightly damaged areas, respectively. Accuracy verification showed that the BDANet model exhibited good performance in handling high-resolution images and can be used to directly assess building damage and provide rapid information for rescue operations in future disasters using model weights. [ABSTRACT FROM AUTHOR]

This article estimates the economic impacts of wildfire damage on Korea's regional economies, developing an integrated disaster‐economic system for Korea. The system is composed of four modules: an interregional computable general equilibrium (ICGE) model for the eastern mountain area (EMA) and the rest of Korea, a Bayesian wildfire model, a transportation demand model, and a tourist expenditure model. The model has a hierarchical structure, with the ICGE model serving as a core module to link to three other modules. In the impact analysis of a wildfire, three external shocks are injected into the ICGE model: (1) the wildfire damaged area derived from the Bayesian wildfire model, (2) changes in travel times among cities and counties derived from the transportation demand model, and (3) variations in visitors' expenditures derived from the tourist expenditure model. The simulation shows that the gross regional product (GRP) of the EMA would decrease by 0.25% to 0.55% without climate change and by 0.51%–1.23% with climate change. This article contributes to the development of quantitative linkages between macro and micro spatial models in a bottom‐up system for the impact analysis of disasters, integrating a regional economic model with a place‐based disaster model and the demands of tourism and transportation. [ABSTRACT FROM AUTHOR]

Objectives: The Turkey-Syria earthquake occurred on February 6th, 2023, and caused heavy casualties and property losses. This paper used high-resolution night-time light images to assess the damage of the affected cities. Methods: A multivariate power function regression model was used for intercalibration between sustainable development goals satellite-1(SDGSAT-1) and Yangwang-1 night-time light images. And the disaster assessment was carried out in the cities of Adiyaman and Antakya-Defne in Turkey. Results: The results show that the power facilities of Adiyaman and Antakya-Defne were severely damaged in the earthquake. The light loss rate of more than half of the building area exceeded 80%, and the overall light loss rate of the building area exceeded 70%. Additionally, the light loss rate in the east of Adiyaman was higher than that in the west, and the light loss rate in the downtown area of Antakya-Defne was higher than that in the suburbs. Conclusions: The high-resolution night-time light images can effectively assess the impact caused by natural disasters, and the assessment results can also help local government departments guide emergency relief and reconstruction work after the earthquake. [ABSTRACT FROM AUTHOR]

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Pine wilt disease (PWD) is one of the most concerning diseases in forestry and poses a considerable threat to forests. Since the deep learning approach can interpret the raw images acquired by UAVs, it provides an effective means for forest health detection. However, the fact that only PWD can be detected but not the degree of infection can be evaluated hinders forest management, so it is necessary to establish an effective method to accurately detect PWD and extract regions infected by PWD. Therefore, a Mask R-CNN-based PWD detection and extraction algorithm is proposed in this paper. Firstly, the extraction of image features is improved by using the advanced ConvNeXt network. Then, it is proposed to change the original multi-scale structure to PA-FPN and normalize it by using GN and WS methods, which effectively enhances the data exchange between the bottom and top layers under low Batch-size training. Finally, a branch is added to the Mask module to improve the ability to extract objects using fusion. In addition, a PWD region extraction module is proposed in this paper for evaluating the damage caused by PWD. The experimental results show that the improved method proposed in this paper can achieve 91.9% recognition precision, 90.2% mapping precision, and 89.3% recognition rate of the affected regions on the PWD dataset. It can effectively identify the distribution of diseased pine trees and calculate the damage proportion in a relatively accurate way to facilitate the management of forests. [ABSTRACT FROM AUTHOR]

Floods are the most frequent natural disasters, occurring almost every year around the globe. To mitigate the damage caused by a flood, it is important to timely assess the magnitude of the damage and efficiently conduct rescue operations, deploy security personnel and allocate resources to the affected areas. To efficiently respond to the natural disaster, it is very crucial to swiftly obtain accurate information, which is hard to obtain during a post-flood crisis. Generally, high resolution satellite images are predominantly used to obtain post-disaster information. Recently, deep learning models have achieved superior performance in extracting high-level semantic information from satellite images. However, due to the loss of multi-scale and global contextual features, existing deep learning models still face challenges in extracting complete and uninterrupted results. In this work, we proposed a novel deep learning semantic segmentation model that reduces the loss of multi-scale features and enhances global context awareness. Generally, the proposed framework consists of three modules, encoder, decoder and bridge, combined in a popular U-shaped scheme. The encoder and decoder modules of the framework introduce Res-inception units to obtain reliable multi-scale features and employ a bridge module (between the encoder and decoder) to capture global context. To demonstrate the effectiveness of the proposed framework, we perform an evaluation using a publicly available challenging dataset, FloodNet. Furthermore, we compare the performance of the proposed framework with other reference methods. We compare the proposed framework with recent reference models. Quantitative and qualitative results show that the proposed framework outperforms other reference models by an obvious margin. [ABSTRACT FROM AUTHOR]

In the contexts of global climate change and the urbanization process, urban flooding poses significant challenges worldwide, necessitating effective rapid assessments to understand its impacts on various aspects of urban systems. This can be achieved through the collection and analysis of big data sources such as social media data. However, existing literature remains limited in terms of conducting a comprehensive disaster impact assessment leveraging social media data. This study employs mixed-methods research, a synergy of statistical analysis, machine learning algorithms, and geographical analysis to examine the impacts of urban flooding using the case of the 2020 Guangzhou rainstorm event. The result show that: (1) analyzing social media content enables monitoring of the development of disaster situations, with varied distributions of impact categories observed across different phases of the urban flood event; (2) a lexicon-based approach allows for tracking specific sentiment categories, revealing differential contributions to negative sentiments from various impact topics; (3) location information derived from social media texts can unveil the geographic distribution of impacted areas, and significant correlations are indicated between the waterlogging hotspots and four predisposing factors, namely precipitation, proportion of built-up surfaces, population density, and road density. Consequently, this study suggests that collecting and analyzing social media data is a reliable and feasible way of conducting rapid impact assessment for disasters.

Conducting landslide recognition research holds notable practical significance for disaster management. In response to the challenges posed by noise, information redundancy, and geometric distortions in single-orbit SAR imagery during landslide recognition, this study proposes a dual-polarization SAR image landslide recognition approach that combines ascending and descending time-series information while considering polarization channel details to enhance the accuracy of landslide identification. The results demonstrate notable improvements in landslide recognition accuracy using the ascending and descending fusion strategy compared to single-orbit data, with F1 scores increasing by 5.19% and 8.82% in Hokkaido and Papua New Guinea, respectively. Additionally, utilizing time-series imagery in Group 2 as opposed to using only pre- and post-event images in Group 4 leads to F1 score improvements of 6.94% and 9.23% in Hokkaido and Papua New Guinea, respectively, confirming the effectiveness of time-series information in enhancing landslide recognition accuracy. Furthermore, employing dual-polarization strategies in Group 4 relative to single-polarization Groups 5 and 6 results in peak F1 score increases of 7.46% and 12.07% in Hokkaido and Papua New Guinea, respectively, demonstrating the feasibility of dual-polarization strategies. However, due to limitations in Sentinel-1 imagery resolution and terrain complexities, omissions and false alarms may arise near landslide edges. The improvements achieved in this study hold critical implications for landslide disaster assessment and provide valuable insights for further enhancing landslide recognition capabilities.

• Urban floods induce not only natural geological disasters but also social accidents. • These disaster events have cascade, superposition, and amplification effects. • Social media data can provide detailed and valid information about disaster events. • Event evolutionary graphs can use social media data to construct disaster networks. • The disaster event network's propagation efficiency decreased by 37–62% and 44%. Increasingly frequent flood disasters have caused great losses in recent years. Urban floods induce not only natural geological disasters but also social accidents. These disaster events, called urban flood cascade compound disaster events (UFCCDEs) in this study, have significant cascade, superposition, and amplification effects. However, conventional data sources and processing methods make it difficult to analyze the detailed course of disaster events caused by urban floods, thereby hindering the vulnerability assessment of UFCCDE networks (UFCCDENs). Herein, we propose a framework considering the interactions between disaster events caused by urban floods for rapidly and comprehensively assessing the vulnerability of the UFCCDEN. First, social media data (Sina Weibo) are processed to analyze the spatio-temporal distribution of UFCCDEs and construct a UFCCDEN based on an event evolutionary graph. Second, complex network theory is applied to evaluate the importance of disaster events and the vulnerability of disaster causal chains in the constructed UFCCDEN. Finally, the global efficiency of the network is calculated to assess the propagation efficiency of the UFCCDEN before and after implementing disaster mitigation strategies based on the assessment results to demonstrate the performance of the assessment framework. The coastal megacity Guangzhou was selected as an example. The results showed that, social media data can provide detailed and valid information about UFCCDEs, which can be used to construct the UFCCDEN based on the event evolutionary graph. Waterlogging is found to be the most important disaster event in the UFCCDEN. Furthermore, power facilities, drainage facilities, and roads should be given top priority in the prevention and mitigation of urban floods because of their significant cascading amplification effects. The proposed framework can make the propagation efficiency of the UFCCDEN markedly decrease by 37–62% and 44%, based on the assessment results of disaster events and causal chains, respectively. [ABSTRACT FROM AUTHOR]

Using data from 62 tropical cyclones (TCs) that made landfall in Guangdong Province (China) between 2000 and 2019, we selected six indices—minimum central pressure, maximum wind speed, maximum rainstorm ratio, cumulative surface rainfall, tropical cyclone (TC) track length, lifetime—and constructed a projection pursuit dynamic cluster (PPDC) model to assess TC damage risk. Although a single index may provide correct information on the intensity of certain types of damage, a comprehensive damage risk assessment cannot be obtained from individual indices alone. The PPDC model is a stable tool for TC damage risk assessment, especially in terms of economic loss, agricultural disaster area and disaster-affected population. Model validation improved the correlation of each of the indices. Output from the PPDC model for disaster-affected population and agricultural disaster-affected area also improved after model validation. We examined the limitations of the single indices using data from three TCs. Output from the PPDC model can closely reflect the intensity of the damage caused by the cyclones. Projection pursuit dynamic clustering is a new objective method for TC damage risk assessment, which can provide the scientific basis to support disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) is one of the most concerning diseases in forestry and poses a considerable threat to forests. Since the deep learning approach can interpret the raw images acquired by UAVs, it provides an effective means for forest health detection. However, the fact that only PWD can be detected but not the degree of infection can be evaluated hinders forest management, so it is necessary to establish an effective method to accurately detect PWD and extract regions infected by PWD. Therefore, a Mask R-CNN-based PWD detection and extraction algorithm is proposed in this paper. Firstly, the extraction of image features is improved by using the advanced ConvNeXt network. Then, it is proposed to change the original multi-scale structure to PA-FPN and normalize it by using GN and WS methods, which effectively enhances the data exchange between the bottom and top layers under low Batch-size training. Finally, a branch is added to the Mask module to improve the ability to extract objects using fusion. In addition, a PWD region extraction module is proposed in this paper for evaluating the damage caused by PWD. The experimental results show that the improved method proposed in this paper can achieve 91.9% recognition precision, 90.2% mapping precision, and 89.3% recognition rate of the affected regions on the PWD dataset. It can effectively identify the distribution of diseased pine trees and calculate the damage proportion in a relatively accurate way to facilitate the management of forests.

Floods are the most frequent natural disasters, occurring almost every year around the globe. To mitigate the damage caused by a flood, it is important to timely assess the magnitude of the damage and efficiently conduct rescue operations, deploy security personnel and allocate resources to the affected areas. To efficiently respond to the natural disaster, it is very crucial to swiftly obtain accurate information, which is hard to obtain during a post-flood crisis. Generally, high resolution satellite images are predominantly used to obtain post-disaster information. Recently, deep learning models have achieved superior performance in extracting high-level semantic information from satellite images. However, due to the loss of multi-scale and global contextual features, existing deep learning models still face challenges in extracting complete and uninterrupted results. In this work, we proposed a novel deep learning semantic segmentation model that reduces the loss of multi-scale features and enhances global context awareness. Generally, the proposed framework consists of three modules, encoder, decoder and bridge, combined in a popular U-shaped scheme. The encoder and decoder modules of the framework introduce Res-inception units to obtain reliable multi-scale features and employ a bridge module (between the encoder and decoder) to capture global context. To demonstrate the effectiveness of the proposed framework, we perform an evaluation using a publicly available challenging dataset, FloodNet. Furthermore, we compare the performance of the proposed framework with other reference methods. We compare the proposed framework with recent reference models. Quantitative and qualitative results show that the proposed framework outperforms other reference models by an obvious margin.

Multiple hazards, which threaten people's lives and property, are the main concern for engineers in preventing dangers to buildings. In particular, densely populated areas, such as capital cities and tall buildings, are exposed to higher risks owing to multiple hazards. To rapidly evaluate the disaster assessment of tall buildings, the Federal Emergency Management Agency (FEMA) in the U.S. proposed the integrated rapid visual screening system (IRVS). However, the IRVS system only considers U.S. conditions. Therefore, a Korean-oriented rapid visual screening system should be developed because the number of buildings over 200 m in Korea ranked fourth in the world with the highest density (500 people per square kilometer) among the top five countries based on the Council on Tall Buildings and Urban Habitat's (CTBUH) database. This study describes a Korea-rapid visual screening (K-RVS) system that focuses on the structural safety of tall buildings in Korea. The K-RVS system was modified based on the IRVS, considering the Korean design standard (KDS) and Korean conditions. With the weight value for each characteristic, the scores for each hazard and final scores combined from the scores by multi-hazard can be obtained to conduct a disaster assessment of tall buildings subjected to multiple hazards. [ABSTRACT FROM AUTHOR]

Qinghai Forest Park is a large state-owned forest area in China. Due to frequent thunder and lightning weather, lightning strikes are prone to occur in this area, triggering large-scale forest fires, and causing great economic losses and ecological disasters to the country. In order to solve the problem of lightning damage that has plagued the area for a long time, and reduce the losses caused by lightning and lightning strikes, it is necessary to build an effective lightning protection and fire prevention system. This article designs a lightning monitoring and positioning system for Qinghai Forest Park. The system has the function of lightning monitoring and positioning which can provide information for lightning protection in forest areas. In addition, this article analyzes the lightning disaster in Qinghai Forest Park and provides a reference for protection work. [ABSTRACT FROM AUTHOR]

Typhoon Lekima (2019) struck Zhejiang Province on 10 August 2019 as a supertyphoon, which severely impacted Zhejiang Province. The typhoon killed 45 people and left three others missing, and the total economic loss reached 40.71 billion yuan. This paper reports a postdisaster survey that focuses on the storm precipitation, flooding, landslides, and weather services associated with Typhoon Lekima (2019) along the southeastern coastline of Zhejiang Province. The survey was conducted by a joint survey team from the Shanghai Typhoon Institute and local meteorological bureaus from 26 to 28 August, 2019, approximately two weeks after the disaster. Based on this survey and subsequent analyses of the results, we hope to develop countermeasures to prevent future tragedies. [ABSTRACT FROM AUTHOR]

The landslide-induced surge of river type reservoir in the mountainous area has become one of the most important disasters affecting the waterborne transportation and the safety of human life and property in the reservoir area. Based on the field observation of landslide and landslide-induced surge in the Three Gorges Reservoir (TGR) area and relevant research results, this paper develops the basic principles for selecting the risk factors of landslide-induced surge, analyses the risk factors of landslide-induced surge, and gives the main and secondary risk factors and their characteristics. According to the characteristics of landslide-induced surge disaster, the hierarchical structure, judgment matrix, and risk calculation model suitable for landslide-induced surge disaster are established by using analytic hierarchy process method, and the classification standard of the action index of each main evaluation factor is determined. This well-developed evaluation method is then applied to evaluate the risk of Hongyanzi landslide-induced surge disaster in the TGR area. The evaluation result is high risk, which is consistent with the actual disaster situation.

Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally. [ABSTRACT FROM AUTHOR]

Natural and man-made hazards may disrupt and affect negatively agricultural sector. 0Damage and loss caused by disasters which are likely to become more frequent and severe due to Climate Change effects, need to be assessed, because precise sector-specific disaster losses and damage data are a vital indication for disaster risk reduction and policy-making. This paper responds to the necessity of creation a unified methodology for the damage and loss assessment in Agriculture Sector in Georgia. The paper introduces a comprehensive formula for damage assessment in agriculture sector. Assessment formulas has been designed to be compliant to the Sendai Framework requirements while enforcing the assessment of damages in agriculture with a multi-temporal dimension. The proposed methodology differs from other well know methodologies (ECLAC- DaLA, FAO and UNSDR methodologies) by excluding certain aspects and adding relevant variables (e.g. yield per hectare by region, irrigation and varieties; production capacity until full recovery of perennial crops etc. has been considered). Collected data can be used to calculate volume of compensation in agriculture sector and improve compensation procedures. As well as, it will enable relevant government agencies, private insurance companies, and research institutes to develop (calibrate) damage and loss assessment models. [ABSTRACT FROM AUTHOR]

We collect the isoseismal data of 488 earthquakes in western China and 182 earthquakes in eastern China after 1900. The least square method is used to establish two models of the seismic intensity attenuation relationship partitions in China, based on the major and minor axis ellipse models, and the area and the major axis radius, respectively. The two models are applied to the calculation of the intensity circle of the earthquake events with a magnitude MS ​≥ ​5.0 from 2008 to 2019, and the actual intensity is compared with the model intensity value as an indicator to verify the consistency between the actual intensity and the value from the empirical statistical model. Three real earthquake results are selected to calculate the major and minor axis and area of the ellipse using the two intensity attenuation relationship models. After comparison, we summarize the deviation characteristics of the intensity value, and put forward corresponding improvement suggestions.

Multiple hazards, which threaten people’s lives and property, are the main concern for engineers in preventing dangers to buildings. In particular, densely populated areas, such as capital cities and tall buildings, are exposed to higher risks owing to multiple hazards. To rapidly evaluate the disaster assessment of tall buildings, the Federal Emergency Management Agency (FEMA) in the U.S. proposed the integrated rapid visual screening system (IRVS). However, the IRVS system only considers U.S. conditions. Therefore, a Korean-oriented rapid visual screening system should be developed because the number of buildings over 200 m in Korea ranked fourth in the world with the highest density (500 people per square kilometer) among the top five countries based on the Council on Tall Buildings and Urban Habitat’s (CTBUH) database. This study describes a Korea-rapid visual screening (K-RVS) system that focuses on the structural safety of tall buildings in Korea. The K-RVS system was modified based on the IRVS, considering the Korean design standard (KDS) and Korean conditions. With the weight value for each characteristic, the scores for each hazard and final scores combined from the scores by multi-hazard can be obtained to conduct a disaster assessment of tall buildings subjected to multiple hazards.

The landslide-induced surge of river type reservoir in the mountainous area has become one of the most important disasters affecting the waterborne transportation and the safety of human life and property in the reservoir area. Based on the field observation of landslide and landslide-induced surge in the Three Gorges Reservoir (TGR) area and relevant research results, this paper develops the basic principles for selecting the risk factors of landslide-induced surge, analyses the risk factors of landslide-induced surge, and gives the main and secondary risk factors and their characteristics. According to the characteristics of landslide-induced surge disaster, the hierarchical structure, judgment matrix, and risk calculation model suitable for landslide-induced surge disaster are established by using analytic hierarchy process method, and the classification standard of the action index of each main evaluation factor is determined. This well-developed evaluation method is then applied to evaluate the risk of Hongyanzi landslide-induced surge disaster in the TGR area. The evaluation result is high risk, which is consistent with the actual disaster situation. [ABSTRACT FROM AUTHOR]

This paper examines the vulnerability of households to disasters, using an asset vulnerability framework to represent livelihoods. Such frameworks are widely employed to analyse household poverty and focus on living conditions and well‐being rather than money‐metric measures of consumption and income. The conceptualisation of household vulnerability is a challenge in current studies on coping with disasters. The paper considers whether a capital assets framework is useful in identifying and assessing household vulnerability in the context of the Wenchuan earthquake in China in 2008. The framework has five categories of assets (financial, human, natural, physical, and social capital) and attempts to measure the resilience and vulnerability of households. When applied to a major disaster, asset‐based methods face the problem of heterogeneity of the population, such as with regard to livelihood type or residence. Moreover, the effect of external interventions, such as the provision of relief assistance, must be taken into account. [ABSTRACT FROM AUTHOR]

This article estimates the economic impacts of wildfire damage on Korea's regional economies, developing an integrated disaster-economic system for Korea. The system is composed of four modules: an interregional computable general equilibrium (ICGE) model for the eastern mountain area (EMA) and the rest of Korea, a Bayesian wildfire model, a transportation demand model, and a tourist expenditure model. The model has a hierarchical structure, with the ICGE model serving as a core module to link to three other modules. In the impact analysis of a wildfire, three external shocks are injected into the ICGE model: (1) the wildfire damaged area derived from the Bayesian wildfire model, (2) changes in travel times among cities and counties derived from the transportation demand model, and (3) variations in visitors' expenditures derived from the tourist expenditure model. The simulation shows that the gross regional product (GRP) of the EMA would decrease by 0.25% to 0.55% without climate change and by 0.51%-1.23% with climate change. This article contributes to the development of quantitative linkages between macro and micro spatial models in a bottom-up system for the impact analysis of disasters, integrating a regional economic model with a place-based disaster model and the demands of tourism and transportation., (© 2023 Society for Risk Analysis.)

City is an important foundation for human development. Natural disasters, whose impact on cities was influenced by disaster risk, urban vulnerability and resistance, have always posed serious threats to human development. It is important to identify the key factors in urban disaster preventing and urban securing to reduce the losses of disasters and ensure the urban safety. However, it is difficult to assess the urban safety comprehensively, as the characteristics of disasters and cities varied a lot, resulting in the limited efficiency of urban disaster prevention and management. As a result, we built an index system to improve the urban safety assessment method. By taking six types of urban disasters (urban flood and waterlogging, drought, low temperature, sandstorm, earthquake, and rockfall, landslide and debris flow disasters) into consideration, based on the open access data and the convenient methods to calculate weights and indices, this evaluation system is of applicability and simplicity, and it can overcome the low efficiency shortcoming of the current assessment methods. The index system was then applied to 596 cities in the mainland of China to validate its effectiveness and accuracy and understand the situation of urban safety of China. The interaction mechanisms among the three characteristics of urban disasters (disaster risk, urban vulnerability and resistance) were analysed qualitatively and quantitatively, and the 'compensate effect' between urban vulnerability and resistance was found. According to the result of the urban safety assessment, the safety situations of most cities in China were not satisfactory, but there was great potential to improve them. Since the major natural disasters and safety degrees varied in different cities, these Chinese mainland cities were classified into six different regions based on our results, and the key points of disaster management in each region were pointed out, in order to help the government to promote Chinese cities' safety. [ABSTRACT FROM AUTHOR]

Abstract Social media, with its ability to record human activities, has gained increasing attention from various fields. In this study, we developed a framework to assess disaster impacts with social media data, and examined the potential of information extraction with social media messages from Weibo platform to inform disaster response and recovery in China, using the case of 2016 Wuhan rainstorm and flood disaster. Temporal evolution of social media activities was investigated to track the process of the disaster, and further compared with observed precipitation data. Moreover, major impacts of the disaster were assessed through word frequency analysis of impact-related topics. Finally, place-related information were extracted to map the hotspots of the disaster. The results indicate that temporal variation of social media activities was consistent with the rainstorm process and significant positive correlation was found between social media activities and precipitation intensity. Word frequency analysis of impact-related topics revealed that transportation and daily life were most affected, meanwhile impacts on people's emotion and psychological activities were also notable. For hot spots affected by the disaster, more flooded sites were found in central urban districts, which are generally residential/industrial area or roads and other traffic-related places. This study demonstrated the utility of social media for disaster assessment, however ensuring the accuracy of online information and expanding the application for all phases of disaster management still pose substantial challenges for future research. [ABSTRACT FROM AUTHOR]

In order to estimate the post-earthquake losses in Mainland China, this paper develops a set of economic loss estimation models for Mainland China based on fixed asset data and the damaging information compiled in the Mainland China composite damaging earthquake catalog (MCCDE-CAT). Considering the differences in seismicity and economic development levels across China, these estimation models are developed separately for different sub-regions. Before performing regression analysis, the Human development index (HDI) is used to normalize the economic loss ratio from earthquake occurrence to the present. The robustness and effectiveness of the loss estimation models are verified by separating the loss information in MCCDE-CAT into sample and test data. According to the loss estimation models developed and the normal distribution characteristics of the residuals between modeled and recorded loss data, the probability of different ranges of predicted earthquake economic loss can be determined. Finally, the 2021 Ms 6.4 Yangbi earthquake is taken as an example to demonstrate the application of our developed loss estimation model. The good consistency between the economic loss estimated based on post-earthquake investigations and the predicted economic loss range by our model further indicates the applicability of the model in rapid post-earthquake loss estimation. In the future, the economic loss estimation method will provide scientific guidance in post-earthquake government reconstruction decision-making and international humanitarian assistance. This method is also applicable to disaster loss estimation in any country and region in the world. [ABSTRACT FROM AUTHOR]

The timely and accurate recognition of damage to buildings after destructive disasters is one of the most important post-event responses. Due to the complex and dangerous situations in affected areas, field surveys of post-disaster conditions are not always feasible. The use of satellite imagery for disaster assessment can overcome this problem. However, the textural and contextual features of post-event satellite images vary with disaster types, which makes it difficult to use models that have been developed for a specific disaster type to detect damaged buildings following other types of disasters. Therefore, it is hard to use a single model to effectively and automatically recognize post-disaster building damage for a broad range of disaster types. Therefore, in this paper, we introduce a building damage detection network (BDD-Net) composed of a novel end-to-end remote sensing pixel-classification deep convolutional neural network. BDD-Net was developed to automatically classify every pixel of a post-disaster image into one of non-damaged building, damaged building, or background classes. Pre- and post-disaster images were provided as input for the network to increase semantic information, and a hybrid loss function that combines dice loss and focal loss was used to optimize the network. Publicly available data were utilized to train and test the model, which makes the presented method readily repeatable and comparable. The protocol was tested on images for five disaster types, namely flood, earthquake, volcanic eruption, hurricane, and wildfire. The results show that the proposed method is consistently effective for recognizing buildings damaged by different disasters and in different areas.

For improved prevention and reduction of marine disasters, China’s marine authorities and emergency response agencies require a solution that provides risk assessment, early warning, and decision-making support. This paper proposes a comprehensive approach to disaster assessment that involves automated long-term operation, a spatial information visualization method and systematic integration. The proposed approach provides functions for numerical ocean models with forecast results, automated processing of massive data, multiple disaster/element coupled assessment, and multidimensional display and expression. With regard to storm surge disasters, the approach proposed in this paper adopts a four-tier structure and the functions of each tier are described separately. The original data are comprised of a combination of statistical analysis data and real-time data obtained from the unstructured grid Finite Volume Community Ocean Model. Automated data processing methods and assessment theories incorporating an indicator system and weighted parameters are used for the assessment. By applying 2D/3D visualization technology, assessment results are displayed via several modes for ease of operation and comprehension. The validity of the approach was verified by applying it to Typhoon Hato (No. 1713). Compared with the results of the post-disaster investigation, the assessment results of the proposed approach proved the reliability of the system.

In 2016, Hurricane Matthew devastated many parts of the Caribbean, in particular the country of Haiti. More than 500 people died and the damage was estimated at 1.9 billion USD. At the time, the Humanitarian OpenStreetMap Team (HOT) activated their network of volunteers to create base maps of areas affected by the hurricane, in particular coastal communities in the path of the storm. To help improve HOT's information workflow for disaster response, one strand of the Crowd4Sat project, which was funded by the European Space Agency, focussed on examining where the Picture Pile Tool, an application for rapid image interpretation and classification, could potentially contribute. Satellite images obtained from the time that Hurricane Matthew occurred were used to simulate a situation post-event, where the aim was to demonstrate how Picture Pile could be used to create a map of building damage. The aim of this paper is to present the Picture Pile tool and show the results from this simulation, which produced a crowdsourced map of damaged buildings for a selected area of Haiti in 1 week (but with increased confidence in the results over a 3 week period). A quality assessment of the results showed that the volunteers agreed with experts and the majority of individual classifications around 92% of the time, indicating that the crowd performed well in this task. The next stage will involve optimizing the workflow for the use of Picture Pile in future natural disaster situations. [ABSTRACT FROM AUTHOR]

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This paper investigates how social images and change detection techniques can be applied to identify the damage caused by natural disasters for disaster assessment. We propose a multi-step image matching-based framework (MSIM), which takes advantages of fast clustering, near duplicate image detection and robust boundary matching for the change detection in disasters. We first model the social images by exploiting their tags and locations. After that, we propose a recursive 2-means algorithm over the new data model, and refine the near duplicate detection by local interest point-based matching over image pairs in neighboring clusters. Finally, we propose a novel boundary representation model called relative position annulus (RPA), which is robust to boundary rotation, location shift and editing operations. A new RPA matching method is proposed by extending dynamic time wrapping (DTW) from time to position annulus. We have done extensive experiments to evaluate the high effectiveness and efficiency of our approach. [ABSTRACT FROM AUTHOR]

Accurate building damage assessment is essential in providing decision support for disaster relief and reconstruction. Polarimetric synthetic aperture radar (PolSAR) has become one of the most effective means of building damage assessment, due to its all-day/all-weather ability and richer backscatter information of targets. However, intact buildings that are not parallel to the SAR flight pass (termed oriented buildings) and collapsed buildings share similar scattering mechanisms, both of which are dominated by volume scattering. This characteristic always leads to misjudgments between assessments of collapsed buildings and oriented buildings from PolSAR data. Because the collapsed buildings and the intact buildings (whether oriented or parallel buildings) have different textures, a novel building damage assessment method is proposed in this study to address this problem by introducing texture parameters of statistical models. First, the logarithms of the estimated texture parameters of different statistical models are taken as a new texture feature to describe the collapse of the buildings. Second, the collapsed buildings and intact buildings are distinguished using an appropriate threshold. Then, the building blocks are classified into three levels based on the building block collapse rate. Moreover, this paper also discusses the capability for performing damage assessment using texture parameters from different statistical models or using different estimators. The RADARSAT-2 and ALOS-1 PolSAR images are used to present and analyze the performance of the proposed method. The results show that using the texture parameters avoids the problem of confusing collapsed and oriented buildings and improves the assessment accuracy. The results assessed by using the K / G 0 distribution texture parameters estimated based on the second moment obtain the highest extraction accuracies. For the RADARSAT-2 and ALOS-1 data, the overall accuracy (OA) for these three types of buildings is 73.39% and 68.45%, respectively. [ABSTRACT FROM AUTHOR]