Search Results (252)

Debris flows pose significant risks to mountainous regions, and quick, accurate volume estimation is crucial for hazard assessment and post-disaster response. Traditional volume estimation methods, such as ground surveys and aerial photogrammetry, are often limited by cost, accessibility, and timeliness. While remote sensing offers wide coverage, existing optical and Synthetic Aperture Radar (SAR)-based techniques face challenges in direct volume estimation due to resolution constraints and rapid terrain changes. This study proposes a Super-Resolution Shape from Shading (SRSFS) approach enhanced by a Non-local Piecewise-smooth albedo Constraint (NPC), hereafter referred to as NPC SRSFS, to estimate debris-flow erosion volume using single high-resolution optical satellite imagery. By integrating publicly available global Digital Elevation Model (DEM) data as prior terrain reference, the method enables accurate post-disaster topography reconstruction from a single optical image, thereby reducing reliance on stereo imagery. The NPC constraint improves the robustness of albedo estimation under heterogeneous surface conditions, enhancing depth recovery accuracy. The methodology is evaluated using Gaofen-6 satellite imagery, with quantitative comparisons to aerial Light Detection and Ranging (LiDAR) data. Results show that the proposed method achieves reliable terrain reconstruction and erosion volume estimates, with accuracy comparable to airborne LiDAR. This study demonstrates the potential of NPC SRSFS as a rapid, cost-effective alternative for post-disaster debris-flow assessment. Full article

This study investigates the challenges and opportunities of balancing cultural preservation, tourism investment, and community resilience in historic Syrian cities during the post-war recovery period. The Syrian conflict has imposed considerable harm upon the nation’s cultural heritage, encompassing UNESCO World Heritage sites, thereby interrupting not only the urban infrastructure but also local economies and social networks. Utilizing a comprehensive methodology that includes a literature review, stakeholder interviews, and local surveys, this research investigates the potential for aligning cultural preservation with tourism investment to promote sustainable economic revitalization while simultaneously enhancing social cohesion and community resilience. The results underscore the significance of inclusive governance, participatory planning, and capacity enhancement to guarantee that post-conflict urban redevelopment fosters enduring environmental, social, and cultural sustainability. By framing the Syrian case within the broader context of global urban sustainability and resilience discourse, the study offers valuable insights for policymakers, urban planners, and heritage managers working in post-conflict or post-disaster environments worldwide. In the end, the study highlights that the revitalization of historic cities transcends being a simple technical or economic endeavor; it is a complex process of re-establishing identity, strengthening communities, and fostering sustainable, resilient urban futures. Full article

Natural disasters can cause severe damage to critical infrastructure such as road networks, significantly delaying rescue and recovery efforts. Conventional road damage assessments rely heavily on manual inspection, which is labor-intensive, time-consuming, and infeasible in large-scale disaster-affected areas. This study aims to propose a deep learning-based framework to automatically detect and quantitatively assess road damage using high-resolution pre- and post-disaster satellite imagery. To achieve this, the study systematically compares three distinct change detection approaches: single-timeframe overlay, difference-based segmentation, and Siamese feature fusion. Experimental results, validated over multiple runs, show the difference-based model achieved the highest overall F1-score (0.594 ± 0.025), surpassing the overlay and Siamese models by approximately 127.6% and 27.5%, respectively. However, a key finding of this study is that even this best-performing model is constrained by a low detection recall (0.445 ± 0.051) for the ‘damaged road’ class. This reveals that severe class imbalance is a fundamental hurdle in this domain for which standard training strategies are insufficient. This study establishes a crucial benchmark for the field, highlighting that future research must focus on methods that directly address class imbalance to improve detection recall. Despite its quantified limitations, the proposed framework enables the visualization of damage density maps, supporting emergency response strategies such as prioritizing road restoration and accessibility planning in disaster-stricken areas. Full article

Accurate multi-class semantic segmentation of disaster-affected areas is essential for rapid response and effective recovery planning. We present CSDNet, a context-aware segmentation model tailored to disaster scene scenarios, designed to improve segmentation of both large-scale disaster zones and small, underrepresented classes. The architecture combines a lightweight transformer module for global context modeling with depthwise separable convolutions (DWSCs) to enhance efficiency without compromising representational capacity. Additionally, we introduce a detection-guided feature fusion mechanism that integrates outputs from auxiliary detection tasks to mitigate class imbalance and improve discrimination of visually similar categories. Extensive experiments on several public datasets demonstrate that our model significantly improves segmentation of both man-made infrastructure and natural damage-related features, offering a robust and efficient solution for post-disaster analysis. Full article

Natural and man-made disasters are occurring more frequently, making household emergency preparedness essential for an effective response. Enrollees of the World Trade Center Health Registry have been found to be less prepared than the US national average despite their prior disaster exposure. The purpose of this study was to evaluate and compare the effectiveness of two interventions—a mailed brochure and a structured phone call—for increasing emergency preparedness knowledge among this population. We conducted a two-arm parallel group trial between February 2019 and August 2020. Participants were Registry enrollees who completed the Wave 4 Registry (2015–2016) survey, whose primary language was English or Spanish, who lived in New York City, and who did not report being a rescue and recovery worker affiliated with FDNY or NYPD. Enrollees were randomized to receive either a brochure by mail summarizing the components of emergency preparedness or a 15 min phone call describing the same. The primary outcome measure was the number of “yes” responses to the ten-item CDC CASPER emergency preparedness questionnaire, measured at baseline and post-intervention. Enrollees were sequentially alternatively assigned to either the brochure or phone call groups. In total, 705 enrollees were assigned to the brochure (n = 353) or phone call (n = 352) groups, and a total of 702 enrollees were analyzed. The Incident Rate Ratio (IRR) for the effect of time was 1.17 (95% CI = (1.14, 1.20)) and for intervention was 1.00 (95% CI = (0.95, 1.05)) Both the brochure and phone call interventions improved knowledge of emergency preparedness from baseline to post-intervention assessment, and to the same extent. Full article

Critical transportation networks in developing countries often lack structural robustness and functional redundancy due to insufficient planning and preparedness. These deficiencies increase vulnerability to disruptions and impede effective post-disaster response and recovery. Understanding how such networks perform under stress is essential to improving resilience in hazard-prone urban environments. This paper presents an integrated predictive methodology for assessing the operational resilience of urban transportation networks under extreme events, specifically tailored to data-scarce and high-risk contexts. By combining Geographic Information Systems (GISs) with complex network theory, the framework captures both spatial and topological dependencies. The methodology is applied to Beirut, the capital of Lebanon, a densely populated and disaster-prone Mediterranean city, through scenario-based simulations that account for interdependent stressors such as traffic dynamics, structural fragility, and geophysical hazards. Results reveal that the network exhibits low redundancy and high sensitivity to even minor disruptions, leading to rapid performance degradation. These findings indicate that the network should be classified as highly vulnerable. The study offers a robust framework for assessing infrastructure resilience and supporting evidence-based decision-making in critical urban network management. Full article

Urban flooding poses escalating threats to socioeconomic stability and human safety, exacerbated by urbanization and climate change. While urban flood resilience (UFR) has emerged as a critical framework for flood risk management, existing studies often overlook the systemic integration of post-disaster recovery capacity and multidimensional interactions in UFR assessment. This study develops a novel hazard–vulnerability–exposure–defense capacity–recovery capacity (HVEDR) framework to address research gaps. We employ a hybrid game theory combined weight method (GTCWM)-TOPSIS approach to evaluate UFR in China’s Taihu Lake Basin (TLB), a region highly vulnerable to monsoon- and typhoon-driven floods. Spanning 1999–2020, the analysis reveals three key insights: (1) weight allocation via GTCWM identifies defense capacity (0.224) and hazard (0.224) as dominant dimensions, with drainage pipeline density (0.091), flood-season precipitation (0.087), and medical capacity (0.085) ranking as the top three weighted indicators; (2) temporal trends show an overall upward trajectory in UFR, interrupted by a sharp decline in 2011 due to extreme hazard events, with Shanghai and Hangzhou exhibiting the highest UFR levels, contrasting Zhenjiang’s persistently low UFR; (3) spatial patterns reveal stronger UFR in southern and eastern areas and weaker resilience in northern and western regions. The proposed HVEDR framework and findings provide valuable insights for UFR assessments in other flood-prone basins and regions globally. Full article

Housing recovery represents a critical component of disaster recovery, and accurately forecasting household relocation decisions is essential for guiding effective post-disaster reconstruction policies. This study explores the use of machine learning algorithms to improve the prediction of household relocation in the aftermath of disasters. Leveraging data from 1304 completed interviews conducted as part of the Displaced New Orleans Residents Survey (DNORS) following Hurricane Katrina, we evaluate the performance of Logistic Regression (LR), Random Forest (RF), and Weighted Support Vector Machine (WSVM) models. Results indicate that WSVM significantly outperforms LR and RF, particularly in identifying the minority class of relocated households, achieving the highest F1 score. Key predictors of relocation include homeownership, extent of housing damage, and race. By integrating variable importance rankings and partial dependence plots, the study also enhances interpretability of machine learning outputs. These findings underscore the value of advanced predictive models in disaster recovery planning, particularly in geographically vulnerable regions like New Orleans where accurate relocation forecasting can guide more effective policy interventions. Full article

Background and Objectives: On 6 February 2023, two catastrophic earthquakes struck southeastern Türkiye, affecting over 13 million individuals and causing widespread destruction. While the physical damage was immediate, the psychological consequences—particularly posttraumatic stress disorder (PTSD) and depression—have proven long-lasting. This study aimed to evaluate the severity and course of PTSD symptoms among survivors and to examine the effectiveness of a telepsychiatry-based mental health intervention in a post-disaster setting. Materials and Methods: This naturalistic, observational study included 153 adult participants from the affected regions who underwent at least two telepsychiatry sessions between the first and sixth month post-disaster. Initial screening was conducted using the General Health Questionnaire (GHQ-12), and individuals scoring ≥ 13 were further assessed with the PTSD Checklist—Civilian Version (PCL-C) and the Beck Depression Inventory (BDI). Follow-up evaluations and pharmacological or psychoeducational interventions were offered as clinically indicated. Results: At the one-month follow-up, 94.4% of participants met the threshold for PTSD symptoms (PCL-C > 22) and 77.6% had severe depressive symptoms (BDI > 30). By the sixth month, PTSD symptoms had significantly decreased (mean PCL-C score reduced from 42.47 ± 12.22 to 33.02 ± 12.23, p < 0.001). Greater symptom reduction was associated with higher educational attainment and perceived social support, while prior trauma predicted poorer outcomes. Depression severity emerged as the strongest predictor of chronic PTSD. Conclusions: This study highlights the psychological burden following the 2023 earthquakes in Türkiye and demonstrates the feasibility and potential effectiveness of telepsychiatry in disaster mental health care. Integrating digital mental health services into disaster response systems may help reach vulnerable populations and improve long-term psychological recovery. Full article

This article explores the notion of “gender empowerment” in relation to feminist claims around collectivity and the real lives of young women and non-binary people who grew up in post-Katrina New Orleans. Drawing on participants’ narratives, the article calls into question the assumption that collectivity and isolation are diametrically opposed experiences. Instead, it offers a more nuanced view of “alone space” as forced aloneness—not as inherently negative or disconnected, but as a vital and generative terrain through which participants navigated recovery, identity, and empowerment. The findings suggest that meaningful collective action and participation often emerged not despite but through moments of solitude that allowed for reflection on individual passions, desires, and agency. In this way, individualist approaches were intricately linked to collectivity. Participants carved out unique spaces for change that were both personal and social, finding that their most powerful engagements with collectivity were often rooted in the growth fostered during periods of isolation. These journeys were nonlinear and fraught with complexity, marked by feelings of insecurity and powerlessness, particularly around decision-making and identity formation in the wake of disaster. Yet, within the altered landscape of post-Katrina New Orleans, the experience of aloneness became an unexpected catalyst for empowerment, offering routes back into collective life on renewed and self-defined terms. Full article

Unmanned aerial vehicles (UAVs), owing to their flexible coverage expansion and dynamic adjustment capabilities, hold significant application potential across various fields. With the emergence of urban low-altitude air traffic dominated by UAVs, the integrated aviation information network combining UAVs and manned aircraft has evolved into a complex space–air–ground integrated Internet of Things (IoT) system. The application of 5G/6G network technologies, such as cloud computing, network function virtualization (NFV), and edge computing, has enhanced the flexibility of air traffic services based on service function chains (SFCs), while simultaneously expanding the network attack surface. Compared to traditional networks, the aviation information network integrating UAVs exhibits greater heterogeneity and demands higher service reliability. To address the failure issues of SFCs under attack, this study proposes an efficient SFC recovery method for recovery rate optimization (ERRRO) based on virtual network functions (VNFs) migration technology. The method first determines the recovery order of failed SFCs according to their recovery costs, prioritizing the restoration of SFCs with the lowest costs. Next, the migration priorities of the failed VNFs are ranked based on their neighborhood certainty, with the VNFs exhibiting the highest neighborhood certainty being migrated first. Finally, the destination nodes for migrating the failed VNFs are determined by comprehensively considering attributes such as the instantiated SFC paths, delay of physical platforms, and residual resources. Experiments demonstrate that the ERRRO performs well under networks with varying resource redundancy and different types of attacks. Compared to methods reported in the literature, the ERRRO achieves superior performance in terms of the SFC recovery rate and delay. Full article

While previous studies have explored the use of digital technologies in cultural heritage site reconstruction, limited attention has been given to systems that simultaneously support cultural restoration and psychological healing. This study investigates how multimodal, deep learning–assisted digital technologies can aid displaced populations by enabling both digital reconstruction and trauma relief within virtual environments. A demonstrative virtual reconstruction workflow was developed using the Great Mosque of Aleppo in Damascus as a case study. High-precision three-dimensional models were generated using Neural Radiance Fields, while Stable Diffusion was applied for texture style transfer and localized structural refinement. To enhance immersion, Vector Quantized Variational Autoencoder–based audio reconstruction was used to embed personalized ambient soundscapes into the virtual space. To evaluate the system’s effectiveness, interviews, tests, and surveys were conducted with 20 refugees aged 18–50 years, using the Impact of Event Scale-Revised and the System Usability Scale as assessment tools. The results showed that the proposed approach improved the quality of digital heritage reconstruction and contributed to psychological well-being, offering a novel framework for integrating cultural memory and emotional support in post-disaster contexts. This research provides theoretical and practical insights for future efforts in combining cultural preservation and psychosocial recovery. Full article

Natural disasters such as hurricanes, earthquakes, or tsunamis can significantly affect the image of tourist destinations and the intention to visit them. However, research on the effects of natural disasters and their impact in destinations in Mexico is an under-researched topic. Moreover, attitudes and behaviors of solidarity are important for recovery of destinations after natural disasters. Therefore, the aim of this study was to examine how people’s perceived risk and solidarity attitudes affect the image and intention to visit destinations after natural disasters in the country. Through a structured questionnaire (n = 228), the risk perception, solidarity attitudes, destination image, and intention to visit were measured to assess interest in visiting the emblematic destination of Acapulco, Mexico, which was devastated by Hurricane Otis (category 5) in October 2023. The results show that risk perception does not affect destination image and solidarity attitudes, but it does affect the intention to visit the destination (β = −0.120). The main findings of this study establish the strong influence of solidarity attitudes on the image (β = 0.611) of the destination and the intention to visit (β = 0.581). The results state that destination image had a mediating effect (β = 0.240) on solidarity attitudes and intention to visit post-disaster destinations. Therefore, destination image has a fundamental effect on the formation of attitudes of solidarity for the recovery of destinations after a natural disaster. Solidarity attitudes are of great importance for the destination’s recovery after natural disasters. It is important to prioritize marketing campaigns that recognize these actions of solidarity, on the part of destination management organizations (DMOs) and local governments. Full article

Flood hazards caused by intense short-term precipitation have led to significant social and economic losses and pose serious threats to human life and property. Accurate disaster risk assessment plays a critical role in verifying disaster statistics and supporting disaster recovery and reconstruction processes. In this study, a novel Large-Scale Flood Risk Assessment Model (LS-FRAM) is proposed, incorporating the dimensions of hazard, exposure, vulnerability, and coping capacity. Multi-source heterogeneous data are utilized for evaluating the flood risks. Soil erosion modeling is incorporated into the assessment framework to better understand the interactions between flood intensity and land surface degradation. An index system comprising 12 secondary indicators is constructed and screened using Pearson correlation analysis to minimize redundancy. Subsequently, the Analytic Hierarchy Process (AHP) is utilized to determine the weights of the primary-level indicators, while the entropy weight method, Fuzzy Analytic Hierarchy Process (FAHP), and an integrated weighting approach are combined to calculate the weights of the secondary-level indicators. This model addresses the complexity of large-scale flood risk assessment and management by incorporating multiple perspectives and leveraging diverse data sources. The experimental results demonstrate that the flood risk assessment model, utilizing multi-source data, achieves an overall accuracy of 88.49%. Specifically, the proportions of areas classified as high and very high flood risk are 54.11% in Henan, 31.74% in Shaanxi, and 18.2% in Shanxi. These results provide valuable scientific support for enhancing flood control, disaster relief capabilities, and risk management in the middle and lower reaches of the Yellow River. Furthermore, they can furnish the necessary data support for post-disaster reconstruction efforts in impacted areas. Full article

Vegetation restoration in seismically active regions involves complex interactions between geological hazards and ecological processes. Understanding the spatiotemporal patterns of vegetation recovery is critical for assessing disaster evolution, evaluating mitigation effectiveness, and guiding ecological resilience planning. This study investigates post-earthquake vegetation dynamics in the Chutou Gully watershed, located in the 12 May 2008 Wenchuan earthquake zone, using NDVI data from 2000 to 2022. Results reveal a sharp decline in vegetation cover following the earthquake, followed by a steady recovery trend, with NDVI values projected to return to pre-earthquake levels by 2030. Degradation was concentrated in debris flow channels, while more stable adjacent slopes exhibited stronger recovery. Over time, the area of poorly restored vegetation significantly declined, indicating increased ecosystem resilience. The findings highlight the need for site-specific ecological restoration strategies tailored to localized recovery conditions. This study provides valuable insights for disaster mitigation agencies, ecological planners, and local governments working in mountainous hazard-prone regions, and contributes to the long-term sustainability of ecosystems in disaster-prone areas. Full article