Search Results (1,891)

Tropical cyclones rank among the most destructive natural hazards globally, posing significant threats to coastal ecosystems and communities. Mangrove forests, renowned for their ecological importance and coastal protection services, are vulnerable to these disturbances, suffering structural damage, habitat loss, and disruption of vital ecosystem functions. Conventional field-based assessment methods often fall short in capturing the rapid and widespread impacts of cyclones, particularly in remote or cloud-obscured regions. This review aims to provide a comprehensive synthesis of remote sensing applications for monitoring cyclone-induced impacts on mangrove and coastal ecosystems worldwide. Through a systematic literature review of 74 peer-reviewed articles from 1990 to 2025, the study evaluates the utility of optical sensors, radar systems, and multi-sensor platforms in assessing inundation, vegetation damage, and ecosystem service loss. Key methodological advances such as time-series analysis, machine learning, and UAV-based validation are highlighted, alongside critical gaps including limited geographic coverage, weak validation practices, and minimal socio-economic integration. Notably, 75.4% of reviewed studies are concentrated in Asia, with Bangladesh and India alone accounting for 44.6% of the total literature, underscoring a pronounced geographic bias. The findings underscore the need for robust, near-real-time monitoring frameworks that combine satellite technologies with ground data and community engagement. Ultimately, the review advocates for an integrated, multi-sensor, and participatory approach to cyclone resilience, offering valuable insights for future research, disaster response planning, and sustainable mangrove management. Full article

Rapid post-disaster building damage assessment requires recognizing explicit structural failures and interpreting implicit situational cues in visually complex scenes. Whereas conventional automated methods are often confined to detecting explicit damage patterns, human perception naturally integrates both types of information into a holistic risk judgment. This study presents an exploratory investigation into the neural signatures underlying this integrated judgment process using electroencephalography. A modified paradigm was employed to probe the cognitive dynamics of risk evaluation in participants with civil engineering backgrounds. Although participants were instructed only to identify damaged buildings without explicit severity grading, event-related potential analysis revealed systematic, graded neural responses that scaled with damage severity. This suggests that the brain encodes damage-related information not as a binary state but as a continuous spectrum of perceived risk, implicitly processing severity, even in the absence of explicit instructions. Furthermore, single-trial analysis demonstrated that time-domain features contain robust discriminative information, verifying the feasibility of decoding these latent judgments from brain activity. These findings provide a physiological basis for developing future cognition-informed algorithms and human-in-the-loop frameworks, bridging the semantic gap to enhance the reliability of automated disaster assessment. Full article

Despite the well-documented acceleration of climate-related economic losses in Europe, existing research has largely treated these damages as isolated stochastic events rather than as structurally embedded fiscal risks. This gap leaves EU fiscal governance frameworks inadequately prepared for the persistent, spatially concentrated, and temporally dependent nature of such losses. This study addresses this gap by investigating the systemic transformation of climate-related economic risks within the European Union, arguing that climate losses have evolved from unpredictable stochastic shocks into a persistent, structural burden on the European economy. Adopting a comprehensive multi-methodological approach, the research quantifies this transition by integrating spatial concentration metrics (HHI), advanced time-series modelling (OLS, ARIMA, ETS), and anomaly detection techniques to analyse loss patterns across the EU-27 from 1980 to 2023. The empirical results demonstrate three critical systemic dimensions: (1) a statistically significant upward shift in the baseline of economic damages; (2) a high geographical concentration of losses, with Germany, Italy, and France consistently bearing the largest share of climate-driven fiscal pressure; and (3) the emergence of volatility clustering, indicating that climate risks are becoming increasingly non-linear and embedded in the macroeconomic environment. The study contributes to the literature by reframing climate-related economic losses as a systemic fiscal phenomenon requiring structural governance reform, rather than ad hoc disaster response. The findings suggest that existing reactive policy frameworks are insufficient to address the scale of these structural risks. Consequently, the paper advocates for a paradigm shift in EU climate policy—moving toward anticipatory fiscal instruments, harmonised resilience financing, and monitoring systems designed to mitigate systemic volatility and cross-country economic asymmetry rather than merely responding to isolated disaster events. Full article

Land subsidence has emerged as a critical geohazard affecting major urban centers worldwide, particularly in coastal and deltaic regions where intensive groundwater extraction and rapid urbanization are prevalent. It is estimated that subsidence threatens more than 1.6 billion people globally, with reported subsidence rates exceeding 100 mm/year in several rapidly urbanizing cities and cumulative ground lowering exceeding 10 m in extreme cases such as Mexico City. This review provides a comprehensive synthesis of the hydrogeological drivers, impacts, and sustainable mitigation pathways of land subsidence based on a systematic literature review of 167 peer-reviewed studies following the PRISMA framework and bibliometric network analysis. The findings confirm that groundwater extraction is the dominant driver, causing pore pressure decline and irreversible consolidation of compressible aquitards, while geological conditions, recharge imbalance, and climate variability strongly influence subsidence magnitude and persistence. The consequences are severe and multidimensional, including increased flood risk, infrastructure damage, groundwater storage loss, ecosystem degradation, and significant socio-economic impacts. Global case studies from major subsiding cities demonstrate that subsidence often contributes more to relative sea-level rise and urban flood vulnerability than climate-driven ocean rise alone. Mitigation strategies, including groundwater regulation, managed aquifer recharge, water-sensitive urban design, geotechnical stabilization, and satellite-based monitoring, have shown effectiveness but remain limited when implemented independently. This study proposes an integrated management framework combining continuous monitoring, hydrogeological assessment, sustainable groundwater management, engineering and nature-based solutions, and governance integration. The findings highlight that early intervention, groundwater sustainability, and coordinated policy actions are essential to reduce subsidence and enhance long-term urban resilience. These insights support the achievement of Sustainable Development Goal 11 (Sustainable Cities and Communities), particularly in strengthening disaster risk reduction and climate resilience in subsidence-prone urban areas. Full article

Oil spills are severe environmental disasters that cause long-lasting damage to marine ecosystems and impose significant economic costs, underscoring the urgent need for efficient detection and monitoring technologies. Conventional field-based observation methods, while valuable, are constrained by limited spatial coverage, high costs, and labor-intensive processes, making them impractical for large-scale or rapid-response applications. To overcome these challenges, satellite remote sensing has been used as an effective alternative for oil spill monitoring. In particular, the advent of Google Earth Engine (GEE), a cloud-based geospatial platform, has transformed oil spill research by enabling scalable management and analysis of large satellite remote sensing datasets. This review synthesizes studies employing GEE for oil spill detection, across marine environments and interconnected aquatic systems, focusing on methodologies based on optical imagery and synthetic aperture radar data and approaches that integrate machine learning techniques. The analysis underscores that GEE enhances oil spill monitoring by facilitating rapid data processing, supporting reproducible workflows, and expanding access to multi-source satellite data. Furthermore, this review highlights the necessity of incorporating very-high-resolution satellite data and achieving tighter integration of external deep learning framework within GEE to improve detection accuracy and the operational applicability in complex marine and coastal contexts. Full article

The work presented herein proposes DisasterReliefGPT, a multimodal AI system for automation in the areas of crisis communication and post-disaster assessment. The system integrates three tightly coupled components: a vision module called DisasterOCS for structural damage detection in satellite images, a Large Vision–Language Model (LVLM) for enhanced visual understanding and contextual reasoning, and a Large Language Model (LLM) to produce detailed, clear assessment reports. DisasterOCS relies on a ResNet34-based encoder with partial weight sharing and event-specific decoders, coupled with a custom MultiCrossEntropyDiceLoss function for multi-class segmentation on pre- and post-disaster image pairs. On the benchmark xBD dataset, the developed system reaches a high score of 78.8% in identifying F1-damage, making correct identifications of destroyed buildings with 81.3% precision, while undamaged structures are found with a very high value of 90.7%. From a combination of these components, emergency responders can immediately provide reliable and readable assessments of damage that can be used to directly support urgent decision-making. Full article

Slow-moving landslides can trigger severe disasters when activated by earthquakes, torrential rains, or typhoons. Early detection is crucial for mitigating loss of life and property damage. Interferometric Synthetic Aperture Radar (InSAR) technology is among the most effective techniques for detecting slow-moving landslides, though its accuracy can be further improved through integration with optical imagery and Digital Elevation Models (DEM). Current machine learning approaches that combine InSAR and optical data suffer from limited efficiency, poor transferability, and challenges in regional-scale application. To address these limitations, this study proposes a multimodal dual-path network that integrates InSAR products with textural information from optical imagery to detect slow-moving landslides. One path processes InSAR deformation rates and topographic factors, while the other incorporates texture information and auxiliary data. Together, these paths extract semantic information from high-dimensional spatial features and condense it into low-dimensional representations. A pyramid pooling module is employed to capture multi-scale features during low-level semantic extraction. For feature fusion, a rate-constrained adaptive module is introduced to enhance the contribution of deformation rates to slow-moving landslides. According to the results, the proposed method improves the F1-score for landslide detection by 6% compared to using InSAR products alone. These results provide reliable technical support for regional landslide inventory compilation and disaster management, as well as new insights for regional-scale surveys in slow-moving landslide-prone areas. Full article

Floods are among the most devastating natural hazards, intensified by climate change and rapid urbanization. This study introduces a novel deep learning framework, Transfer Learning-Enhanced FEDformer (TLE-FEDformer), designed for accurate and temporally consistent flood inundation mapping. The framework integrates pre-trained Xception backbones for robust multi-sensor feature extraction from Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery, a cross-modal fusion module to align heterogeneous modalities, and the Frequency Enhanced Decomposed Transformer (FEDformer) for efficient frequency-domain temporal modeling. This architecture effectively captures long-range dependencies and flood dynamics including onset, peak, duration, and recession, while addressing challenges such as cloud contamination, speckle noise, and limited labeled data. Comprehensive experiments demonstrate superior performance, achieving an overall accuracy of 98.12%, an F1-score of 98.55%, and an Intersection over Union (IoU) of 97.38%, outperforming baselines including Convolutional Neural Networks, Capsule Networks, and transfer learning alone. Ablation studies validate the contributions of each component, while sensitivity analyses confirm robustness across hyperparameters. Uncertainty quantification via Monte Carlo dropout highlights high confidence in core flooded regions. Preliminary generalization tests on independent events yield IoU > 94%, indicating strong transferability. TLE-FEDformer advances operational flood monitoring by providing reliable, scalable, and temporally consistent mapping from multi-sensor remote sensing data. This approach offers significant potential for real-time disaster response, early warning systems, and damage assessment in flood-prone regions worldwide. Full article

The 2023 Kahramanmaraş earthquakes caused unprecedented structural damage across South-Eastern Türkiye, highlighting the critical need for rapid post-disaster assessment and understanding the root causes of failure in reinforced concrete (RC) structures. This study provides a comprehensive comparative analysis of 207 RC buildings located in Adıyaman, Hatay, and Kahramanmaraş. A novel methodological approach was employed by integrating post-earthquake field observations with pre-earthquake digital data obtained via Google Street View to identify structural irregularities and damage patterns. The investigated buildings were classified based on their damage levels, with 11.1% categorized as heavily damaged, 34.3% as to-be-demolished, and 54.6% as collapsed. Significant structural irregularities, including soft stories (ranging from 64.9% to 82.7%), heavy overhangs, and vertical discontinuities, were found to be the primary drivers of severe damage. Furthermore, pounding and short-column effects were identified as the most prevalent damage types across all three provinces. The results demonstrate that pre-existing structural irregularities significantly exacerbated the seismic vulnerability of the RC building stock. This research emphasizes the importance of stringent adherence to design codes and suggests that integrating digital imagery into post-disaster surveys can significantly enhance the accuracy of damage classification for future earthquake resilience. Full article

Hurricanes have intensified and become more persistent under a changing climate, increasing the risk of infrastructure damage and property loss in coastal regions, threatening their sustainability. This study examines how hurricane intensity and persistence influence infrastructure loss, contributing to a more comprehensive understanding of climate-related risks. Using data from the National Oceanic and Atmospheric Administration (NOAA) Storm Events Database from 1996 to 2024, we develop a series of machine learning models to predict property losses based on storm characteristics and contextual vulnerability factors. Narrative-based text analysis and time-series feature engineering were applied to extract meteorological and temporal attributes, while regression and ensemble models were used for predictive evaluation. Results show that storm intensity alone explains only a small portion of loss variance, with persistence influencing damage primarily through rainfall and hydrological effects. The findings highlight that vulnerability, exposure, and cumulative risk dynamics are essential for accurate long-term prediction and for assessing infrastructure sustainability. Overall, the study demonstrates that combining machine learning techniques with climate and vulnerability data can inform future research on infrastructure sustainability. The quantified vulnerability-versus-intensity breakdown presented here can support post-disaster resource allocation, insurance risk modeling, and the prioritization of infrastructure maintenance in hurricane-prone regions. Full article

Landslides are characterized by strong suddenness and a wide range of damage; accurate prediction of their susceptibility is an important prerequisite for regional risk prevention and control. To address the difficulties in acquiring landslide inventories in complex terrain areas and the insufficient interpretability of existing prediction models, this study proposes a landslide susceptibility assessment (LSA) framework that integrates automated sample detection and interpretability analysis. The proposed framework is applied to Moxi Town, a typical alpine valley area in Sichuan Province, China. A Mask R-CNN instance segmentation model was introduced to achieve automated detection of landslide samples, resulting in a high-quality inventory containing 923 landslides. Based on the spatial relationships between the landslide inventory and influencing factors, a convolutional neural network (CNN) landslide susceptibility assessment model incorporating Shapley Additive exPlanations (SHAP) interpretability analysis was constructed. The CNN model was further compared with random forest (RF) and extreme gradient boosting (XGBoost) machine learning models. The results show that the AUC value of the CNN model has increased by 4.3% and 3.2% compared with the RF and XGBoost models, respectively, and it significantly reduces the pretzel effect of landslide susceptibility mapping (LSM). The results validate the reliability of the proposed framework, which can provide technical support for landslide disaster prevention and monitoring. Full article

This study evaluates the tracking performance of structural damages in disaster environments by combining YOLOv8 detection with the BoT-SORT tracker. Cracks and exposed rebar, characterized by fine and irregular structures, showed high sensitivity to viewpoint changes, with camera motion compensation (CMC) improving $IoU$ by +19.63% and +20.23%. For exposed rebar, the joint use of CMC and re-identification (Re-ID) further increased $IDF1$ by +37.73%, emphasizing the effectiveness of appearance-based matching. In contrast, delamination and concrete debris, with stable morphology and clear boundaries, exhibited limited benefits from CMC, improving $IoU$ by +11.17% and +3.28%. Analysis of $MOTA$, $IDF1$, and $HOTA$ confirms that fine-grained damages require motion- and appearance-based strategies, while stable types maintain high performance through detection consistency. These results highlight the importance of tailored tracking strategies for enhancing disaster-response robots and structural monitoring systems. Full article

Natural disasters disrupt maritime operations, yet their environmental consequences remain underexplored. This study quantifies CO₂ emission changes following the February 2023 İskenderun Bay earthquakes (7.6 Mwg and 7.5 Mwg) using AIS-derived port visit data and graph neural network modeling. Analyzing 25,837 port visits across a 36-month period (January 2022–December 2024), we compared emissions during baseline (pre-earthquake), acute disruption (February–June 2023), and recovery phases. Results revealed a statistically significant 35.9% increase in per-visit CO₂ emissions during the acute phase (t = 11.79, p < 0.001, Cohen’s d = 0.27), driven by extended port visit durations (from 77.87 to 105.82 h). Counterfactual analysis estimated 27,574 tonnes of excess CO₂ emissions directly attributable to earthquake disruption. Network analysis showed 23.8% reduction in edge density during the acute phase. The graph neural network (GNN) emission prediction model achieved R² = 0.985 (baseline) and R² = 0.997 (recovery) in predicting emission patterns, while acute phase showed predictability collapse (R² = −1.591). These findings demonstrate that seismic events generate sustainability-relevant externalities beyond immediate physical damage, and that quantifying disruption-driven excess emissions supports sustainability-oriented port resilience planning and more robust maritime emission accounting (e.g., under the EU MRV framework). Full article

Sea-level rise (SLR) and coastal flooding are among the most pressing climate-related challenges facing coastal regions worldwide, and their impacts are further intensified by rapid urbanization. These processes pose serious socioeconomic and environmental risks, including increased flood exposure, threats to public health, and damage to critical infrastructure. In Saudi Arabia, more than 3100 km² of coastal land lies at elevations of 1 m or lower; however, reliable assessments of future sea-level rise and its potential impacts remain limited, creating significant uncertainty for long-term planning. This study addresses this knowledge gap by identifying areas vulnerable to sea-level rise and coastal flooding through the development of inundation maps for the Dammam Metropolitan Area (DMA) as a case study, while also outlining potential adaptation measures. Using satellite imagery and geospatial datasets, changes in the DMA shoreline between 2014 and 2024 were analyzed, and sea-level rise scenarios were simulated based on projections from the Intergovernmental Panel on Climate Change (IPCC). The results indicate that under a 0.6 m sea-level rise scenario, flooding would be limited to a small area of approximately 0.2 km² in the Half-Moon residential district. In contrast, a 1.1 m sea-level rise scenario reveals a substantial increase in risk, with nearly 83 km² of the DMA potentially exposed to coastal flooding. Based on these findings, targeted disaster management and adaptation strategies are recommended for areas most vulnerable to sea-level rise. The study highlights the need for policies regulating coastal reclamation and other climate-sensitive developments to minimize future flood risks. It supports the United Nations Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action) by enhancing urban flood risk assessment and improving understanding of climate-driven sea-level rise impacts. Full article

Landslides occurring on reservoir banks in steep, high-gradient canyon areas pose a significant risk of surge disasters when they slide into the water. This can endanger the lives and property of downstream residents and damage coastal infrastructure. Therefore, researching the formation mechanisms, disaster evolution, and risk assessment of the landslide-surge disaster chain in such areas is essential. This paper takes the Laowuchang landslide in the Shuibuya Reservoir area of the Qingjiang River, China, as its research object. Using GeoStudio 2018 software, it evaluates the landslide’s stability under varying reservoir water levels and rainfall conditions. For potential unstable scenarios identified, a full-chain numerical simulation of the landslide–tsunami disaster was conducted based on the Tsunami Squares method, with a focus on analyzing the wave characteristics during generation, propagation, and run-up processes. Furthermore, the paper assesses the risk of landslide–tsunami disasters in the Laowuchang landslide area. The research findings indicate that: (1) Under the long-term continuous river incision, limestone of the Triassic Daye Formation slides along weak interlayers, inducing large-scale collapses. Subsequently, part of the landslide mass is transported by water, while most accumulates in the near-shore area of the Qingjiang River, ultimately shaping the present morphology of the landslide. (2) The Laowuchang landslide is stable under static water levels of 375 m and 400 m, with corresponding safety factors of 1.137 and 1.167, respectively. Under combined static water level and heavy rainfall conditions, the slope stability decreases significantly, with safety factors of 1.034 and 1.064, respectively. Under reservoir drawdown conditions, the slope tends to be unstable, with a safety factor of 1.047. (3) Numerical simulation results indicate that if the Laowuchang landslide fails into water by the speed of 12 m/s and with a volume of 2 million m³, the maximum initial wave height can reach 15.9 m. The tsunami’s affected range spans 10 km upstream and downstream from the landslide mass, with four houses and one substation within a 2 km up and downstream falling into high-risk areas. If abnormal increases in landslide displacement occur, relocation and risk avoidance measures should be implemented. The findings of this study provide a scientific basis for the prevention and response to landslide–tsunami disasters in similar high and steep canyon terrains. Full article