Search Results (2,106)

Land cover classification (LCC) is a fundamental task in remote sensing, which enables effective environmental monitoring, agricultural planning, and disaster management. The existing approaches often rely on fine-tuning pre-trained models, which are not specifically designed for LCC, which lead to suboptimal performance in complex scenarios. To address these limitations, we propose the Cross-Module Attention Land Cover Network (CALCNet), a novel architecture developed from scratch. CALCNet follows a contracting and restoration backbone, where the contracting path extracts progressively abstract semantic features while reducing spatial resolution, and the restoration path recovers fine-grained spatial details through upsampling and skip connections. In addition, CALCNet integrates a cross-module attention mechanism that combines spatial attention and multi-scale feature selection to enhance feature representation. Furthermore, we applied a differential evolution-based neuron pruning strategy to create a compressed CALCNet variant, which retains high classification performance while reducing computational cost. The CALCNet is evaluated on four benchmark LCC datasets, AID, UCMerced_LandUse, NWPU_RESISC45, and EuroSAT, demonstrating strong performance across all benchmarks. Specifically, the model achieves classification accuracies of 98.09%, 99.47%, 99.19%, and 99.19%, respectively. The compressed CALCNet variant reduces computational cost to 78.55 million floating point operations (FLOPs) with a model size of 43 MB, while achieving improved inference speeds (38.32 frames/sec on CPU and 118.3 frames/sec on GPU), representing approximately 45–50% reduction in FLOPs and model storage. These results highlight that CALCNet is both highly accurate and computationally efficient, making it well suited for real-world LCC applications. Full article

In goafs formed by underground mineral resource extraction, the remaining pillars are often subjected to uniaxial loading at different loading rates, and their mechanical responses and failure mechanisms directly affect the long-term stability of the goafs. This study uses rock-like materials to conduct uniaxial compression tests at loading rates ranging from 0.001 mm/min to 0.05 mm/min, combined with acoustic emission (AE) monitoring, to systematically investigate the effects of loading rate on the mechanical properties, energy distribution, constitutive model, and AE characteristics of the material. The results show that an increase in loading rate significantly enhances the stiffness and strength of the material, promotes a transition in failure mode from a shear–tension composite to tension-dominated, intensifies brittle characteristics, and simultaneously inhibits full crack development and fragments generation. In terms of energy evolution, an increased loading rate enhances the pre-peak total strain energy and elastic strain energy storage but reduces the efficiency of energy dissipation, leading to an intensified mismatch between energy storage and dissipation capacities at peak stress. A damage variable induced by loading rate was proposed, and a damage constitutive model considering the loading rate was established, with the theoretical curves showing good agreement with the experimental data. AE characteristic analysis further reveals that an increase in loading rate causes the crack type to transition from shear-dominated to tension-dominated, and the fluctuating increase in the b-value reflects a reduction in pre-peak fracture scale and a decrease in the degree of material fragmentation. The research findings are expected to deepen the understanding of the damage and failure mechanisms of rock materials under different loading rates, thereby laying a research foundation for the stability assessment of goaf pillars and disaster warning. Full article

Malawi lies within the southern segment of the East African Rift System and is exposed to infrequent but potentially damaging earthquakes. While recent advances in fault mapping, seismic monitoring, and hazard modelling have substantially improved scientific understanding of earthquake hazard in the Malawi Rift Zone, the practical reduction in seismic risk remains limited. This Perspective paper argues that earthquake resilience in Malawi is constrained less by scientific uncertainty than by challenges in integrating existing hazard knowledge into governance, planning, and preparedness. Drawing exclusively on published geological, geophysical, engineering, and policy literature, the paper synthesises evidence on seismic hazard, historical earthquake impacts, institutional preparedness, and barriers to the operational use of scientific risk assessments. An integrated, multi-pillar framework is proposed to support improved coordination between science, governance, infrastructure practice, and community preparedness. The framework is conceptual in nature and is intended to inform policy dialogue, prioritisation, and future empirical research rather than to provide a validated operational model. While grounded in the Malawian context, the insights presented are relevant to other low-income, rift-hosted regions facing similar challenges in translating earthquake science into effective disaster risk reduction. Full article

The accurate and rapid prediction of hazard gas dispersion fields in urban environments is essential for guiding emergency sensor deployment and enabling real-time risk assessment. However, the computational cost associated with Computational Fluid Dynamics (CFD) simulations hinders their use as real-time forward models, while simplified Gaussian plume models lack the fidelity to resolve building obstruction effects. This study proposes a morphology-guided conditional Generative Adversarial Network (cGAN) framework designed to achieve real-time gas dispersion field modeling in urban environments with complex building configurations. The urban area is discretized into 50 × 50 m grid cells, each characterized by six morphological parameters describing building geometry. K-means clustering categorizes these cells into distinct morphological types. High-fidelity dispersion datasets are then generated for each type using Lattice Boltzmann Method (LBM) simulations. Each sample encodes building geometry, release location, wind speed, and time as multi-channel input images, with the corresponding gas dispersion concentration field is recorded as the output. Two cGAN architectures, Image-to-Image Translation (Pix2Pix) and its high-resolution variant (Pix2PixHD), are employed to learn the mapping from input features to dispersion fields. Model performance is evaluated using four complementary metrics: Fraction within a Factor of Two (FAC2) for prediction accuracy, Normalized Root Mean Square Error (NRMSE) for precision, Fractional Bias (FB) for systematic error, and Structural Similarity Index (SSIM) for spatial pattern fidelity. A case study is conducted across a 1176 km² urban district in China. The results demonstrate that under varying wind speeds (0.5–1.5 m/s) and temporal scales (5–60 s), and across five morphological categories, the Pix2PixHD-based model achieves 92.5% prediction accuracy and reproduces 97.6% of the spatial patterns. The proposed framework accelerates computation by approximately 18,000 times compared to traditional CFD, reducing inference time to under 0.1 s per scenario. This sub-second capability enables real-time concentration field estimation for emergency management, and provides a physically informed, computationally feasible forward model that can potentially support sensor-based gas source localization and detection network planning in complex urban environments. Full article

With the increasing functional and geometric complexity of modern steel buildings, irregular beam-to-column joints are becoming increasingly common in engineering practice, while their seismic performance and force transfer mechanisms remain insufficiently understood. Based on previous full-scale cyclic loading tests on unequal-depth steel beam (UDSB) and staggered steel beam (SSB) joints incorporating inclined internal diaphragms, this study presents numerical simulations and parametric analyses of irregular steel beam to concrete-filled steel tube (CFST) column joints. Three-dimensional nonlinear finite element models were developed using ABAQUS and validated against experimental results. The strengthening effects of internal diaphragms and concrete infill were then comparatively investigated. The results indicate that internal diaphragms increase the initial stiffness and load-carrying capacity of the joints to approximately 2.0–2.3 times and 1.16–1.8 times, respectively, compared with joints without diaphragms, whereas concrete infill provides smaller enhancements of about 1.3 times in stiffness and 1.2–1.3 times in strength. In addition, the hysteretic response of joints without diaphragms shows good agreement with the post-fracture behavior observed in the experiments, validating the diaphragm fracture mechanism. A parametric study further demonstrates that, under cyclic loading, the beam depth ratio, staggered floor ratio, column wall thickness, column width, diaphragm thickness, and diaphragm opening diameter have significant influences on joint strength and stress distribution, while the effect of axial load ratio is relatively minor. Finally, a strength prediction method applicable to inclined-diaphragm UDSB and SSB joints is proposed, and corresponding fitted expressions are derived based on the parametric results. The findings provide useful guidance for the seismic design of irregular steel beam–CFST column joints incorporating internal diaphragms. Full article

Vehicle-to-grid (V2G) technology has emerged as a key enabler for coupling large-scale electric vehicle (EV) deployment with the operation of smart energy systems. By allowing bidirectional power and information exchange between EVs and the grid, V2G transforms EVs from passive loads into distributed energy resources capable of supporting grid flexibility, reliability, and renewable energy integration. However, the practical realization of V2G remains challenged by technical complexity, system coordination, user participation, and regulatory constraints. This paper presents a comprehensive review of V2G integration from a system-level perspective. Rather than focusing solely on individual technologies, the review examines how V2G is embedded within smart energy systems, emphasizing the interactions among EVs, aggregators, grid operators, energy markets, and end users. Key enabling technologies, including bidirectional charging, aggregation mechanisms, communication frameworks, and data-driven control strategies, are discussed in relation to their system-level roles and limitations. The impacts of V2G on grid operation, energy management, and market participation are analyzed, with particular attention to reliability, battery lifetime, and user trust. Furthermore, this review identifies critical open issues that hinder large-scale deployment, spanning infrastructure readiness, standardization, economic incentives, and cybersecurity. Emerging application scenarios, such as building-integrated V2G, fleet-based services, and artificial intelligence (AI) supported coordination, are also discussed to illustrate potential evolution pathways. By synthesizing technological developments with system-level impacts and unresolved challenges, this paper aims to provide a structured reference for researchers, system planners, and policymakers seeking to advance the integration of V2G into future smart energy systems. Full article

To address the critical challenges of geological hazards, such as water and mud inrush, encountered during the construction of deep-buried tunnels in China, this study investigates the hydraulic properties of remolded mud-infill materials. A multi-scale approach, integrating indoor variable-head permeability tests with scanning electron microscopy (SEM), was employed to characterize the evolutionary patterns of the permeability coefficient (k). Specifically, the research evaluates the independent influences of moisture content, dry density, and confining pressure, alongside the synergistic coupling between dry density and hydration state. The results demonstrate the following: Under independent variable conditions, k exhibits a monotonic decline with increasing dry density and confining pressure while showing a positive correlation with moisture content, with the sensitivity varying significantly across different parameter regimes; under coupled effects, the permeability in both low- and high-moisture ranges manifests a distinct “increase–decrease–increase” fluctuation as dry density rises, reaching a local peak at 2.20 g/cm³. Notably, a relative minimum k (6.12 × 10⁻⁷ cm/s) is achieved at the optimum moisture content (5.8%); micro-mechanistic analysis reveals that low-moisture samples are characterized by randomized angular particles and well-developed interconnected macropore networks, facilitating higher k values. Conversely, high-moisture samples exhibit preferential plate-like stacking dominated by occluded micropores, resulting in a substantial reduction in hydraulic conductivity. This study elucidates the multi-factor coupling mechanism governing the seepage behavior of remolded mud, providing essential theoretical benchmarks for the prediction and mitigation of water–mud outburst disasters in deep underground engineering, thereby ensuring the structural stability and operational safety of tunnel projects. Full article

We developed two distinct automatic packet reporting system (APRS) payload designs to establish a reliable backup communication system for disaster risk reduction and management. The payloads are designed to perform a significant key operation, primarily APRS digital repeating (DP), enabling continuous communication access even in areas where conventional ground-based infrastructure is damaged by natural disasters through the relay of APRS packets to extend communication coverage. A detailed framework is designed using the standard amateur packet radio (AX.25 protocol). It specifies the structure of APRS data frames and packets, which are used to transmit alerts, emergency status updates, and text messages. This structure ensures that important information is transmitted reliably and effectively during an emergency. The designs for the APRS payloads share a common overall operating system architecture but differ in their very high frequency transceiver modules used for the amateur radio (Radiometrix BiM1H very high frequency (VHF) Narrowband Transceiver and Dorji DRA818V VHF Band Voice Transceiver Module). Full article

Accurate monitoring of water spatiotemporal dynamics is critical for hydrological process analysis and climate impact assessment. While remote sensing enables effective water monitoring, public satellite imagery is limited by mixed-pixel effects that hinder small river detection, and high-resolution commercial data suffers from low temporal frequency and restricted coverage. To address these limitations, this study proposes a deep learning-based super-resolution (SR) framework for multispectral remote sensing imagery. This paper constructs a matched dataset for GF2 and Sentinel-2 imagery and develops an Attention Enhanced Super Resolution Generative Adversarial Network (AESRGAN). By integrating attention mechanisms and a spectral-structural loss design, the network is optimized to adapt to the characteristics of multispectral remote sensing imagery. Experimental results demonstrate that AESRGAN achieves strong reconstruction performance, with a Peak Signal-to-Noise Ratio (PSNR) of 33.83 dB and a Structural Similarity Index Measure (SSIM) of 0.882. Water extraction based on the reconstructed imagery using the U-Net++ model achieved an overall accuracy of 0.97 and a Kappa coefficient of 0.92. In addition, the reconstructed imagery improved the estimation accuracy of river length, width, and area by 0.34%, 3.28%, and 8.51%, respectively. The proposed framework provides an effective solution for multi-source remote sensing data fusion and high-precision surface water monitoring, offering new potential for long-term hydrological observation using medium-resolution satellite imagery. Full article

Prior studies have shown that socio-economic and structural risks can be correlated with earthquake effects. The quantification of these effects was used to formulate robust disaster risk reduction (DRR) strategies and building codes. This is more pronounced in countries with complex tectonic settings, such as the Philippines, where strong-to-major earthquakes can occur. Here, we report the evaluation of deterministic ground shaking (GS) intensity measurements for Camarines Norte, the Philippines, with the objective of assessing and mapping the susceptibility of communities to intense ground motion. GS intensities and peak ground acceleration (PGA) were computed using the Rapid Earthquake Damage Assessment System (REDAS) software developed by the Philippine Institute of Volcanology and Seismology (PHIVOLCS). The PGA was computed as a fraction of acceleration due to gravity, while GS used the PHIVOLCS Earthquake Intensity Scale (PEIS). Simulations were based on recorded earthquakes and mapped active faults near the province. Geographic information systems were used to stack and refine each simulation. Results showed that 13 earthquakes and 13 seismic source zones classified most of the province as PEIS VIII or higher, with the PGA maximum at 0.66 g. The results implied that the province is susceptible to very destructive to completely devastating ground shaking, and it is recommended to incorporate these results into DRR policymaking. Full article

Transmission towers, as critical infrastructure in power systems, are frequently threatened by multiple hazards such as strong winds and flood scour. Traditional structural health monitoring methods face limitations in data feedback timeliness and mechanical interpretation, making real-time condition awareness and early warning under disaster scenarios challenging. To address these issues, this paper proposes a digital twin framework for transmission tower structures, integrating Building Information Modeling (BIM), Internet of Things (IoT) technology, and the Finite Element Method (FEM) for structural health monitoring and visual warning under wind loads and flood scour effects. The framework achieves cross-platform collaboration through the FEM Open Application Programming Interface (OAPI) and Python scripts. In the physical domain, fluctuating wind loads are simulated based on the Davenport spectrum, flood scour depth is modeled using the HEC-18 formulation, and foundation constraint degradation is represented through nonlinear spring stiffness reduction. In the FEM domain, dynamic time-history analyses are conducted to obtain structural responses. In the BIM domain, a three-level warning mechanism based on stress change rate (ΔR) is established to achieve intuitive rendering and dynamic feedback of structural damage. A 44.4 m high latticed angle steel tower is employed as the case study for validation. Results demonstrate that the simulated wind spectrum closely matches the theoretical target spectrum, confirming the validity of the load input. A critical scour evolution threshold of 40% is identified, beyond which the first two natural frequencies exhibit nonlinear decay with a maximum reduction of 80.9%. Non-uniform scour induces significant load transfer, with axial forces at leeside nodes increasing from 27 kN to 54 kN. During the 0–60 s wind loading process, BIM visualization accurately captures the full stress evolution from the tower base to the upper structure, showing excellent agreement with FEM results. The proposed framework establishes a closed-loop interaction mechanism of “physical sensing–digital simulation–visual warning”, effectively enhancing the timeliness and interpretability of structural health monitoring for transmission towers under multiple hazards, providing an innovative approach for intelligent disaster prevention in power infrastructure. Full article

Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas, raising important sustainability concerns related to community safety, infrastructure continuity, social equity, and long-term planning capacity. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and capable of representing co-evolving behavioral and network processes under compound hazard conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. To support sustainable and equitable local planning, the framework prioritizes spatially resolved outputs, including neighborhood clearance time, isolation probability, accessibility loss, and shelter demand imbalance. By emphasizing modularity, configurability, and policy-relevant metrics, this review connects methodological advances in evacuation modeling to the broader sustainability goals of resilient infrastructure systems, inclusive disaster risk reduction, and locally informed emergency planning. Full article

Emergency Alert and Warning Systems (EAWSs) are essential components of sustainable disaster risk reduction, providing communities with timely information to prepare for and respond to impending hazards. In the Philippines, one of the world’s most disaster-prone countries, earthquakes, typhoons, and other natural hazards occur frequently. However, national statistics from 2018 indicated that only 40% of Filipinos considered themselves well prepared for disasters, while 31% reported being slightly prepared or not prepared at all. This study investigates the perceived effectiveness of EAWSs in enhancing disaster awareness and preparedness among Filipino residents. Guided by the Theory of Planned Behavior (TPB), the research develops an integrated framework to examine behavioral, technical, and perceptual factors influencing preparedness intentions. Data were collected from 200 respondents through a structured survey. Structural Equation Modeling (SEM) was employed to identify significant linear relationships among the constructs, while an Artificial Neural Network (ANN) analysis was subsequently applied to capture nonlinear patterns and rank the relative importance of key predictors. Unlike previous studies that rely solely on SEM or descriptive approaches, the combined SEM–ANN framework enables a more comprehensive understanding of both causal relationships and complex behavioral dynamics influencing disaster preparedness. The findings reveal that behavioral intention, system reliability, message clarity, and trust in EAWS substantially affect individuals’ preparedness behavior and risk mitigation actions. These results underscore the importance of strengthening EAWS design and communication strategies to support long-term disaster resilience. The study provides practical insights for national agencies, local governments, and policymakers on refining emergency communication systems and developing sustainable, evidence-based disaster preparedness initiatives. Full article

Vehicular infrared object detection is a key technology supporting autonomous driving systems to achieve all-weather environmental perception. However, infrared images inherently lack texture, resulting in blurred object contours. Additionally, deep network propagation severely erodes and loses feature information of distant tiny objects. To address the above issues, this study proposes a Full-Scale Feature Synergistic Perception Architecture for vehicular infrared object detection. This architecture first designs a Gradient-Informed Attention module, which initializes convolution kernels through physical gradient operators to inject geometric prior information into the network, enhancing the model’s perception capability of blurred object boundaries. Secondly, it constructs a Full-Scale Feature Pyramid containing a $P_2$ high-resolution feature layer to effectively recover the geometric detail features of distant tiny objects. Finally, it proposes a Scale-Aware Shared Head, which relies on a cross-scale parameter sharing mechanism to achieve extreme parameter compression, and simultaneously introduces deep semantic information to form strong constraints, suppressing noise interference in shallow features. Experimental results on the FLIR v2 and M3FD datasets show that the proposed architecture exhibits excellent detection performance. On FLIR v2, it raises mAP@50 to 64.06% (6.51% relative gain vs. YOLOv11) while maintaining 547 FPS inference speed, achieving an optimal accuracy–efficiency balance. Full article

This study bridges the existing gaps in quantifying risk and enhancing community defences by applying a cohesive five-pillar risk and resilience framework developed within the C2IMPRESS project. We assessed the anticipated impacts of various C2IMPRESS tools on community resilience across four European case study areas (CSAs): Egaleo (Greece), Mallorca (Spain), Ordu (Turkey), and the Centro Region (Portugal). Methodologically, a targeted survey asked CSA representatives to estimate the expected changes across 42 resilience indicators—encompassing social, institutional, economic, infrastructural, and environmental dimensions—following tool implementation. A public–private-civil partnership (PPCP) framework was also assessed across all sites to enable a comparative analysis. The results indicate that individual vulnerability and emergency preparedness are the most responsive dimensions, exhibiting significant projected improvements alongside institutional capacities and community trust. Conversely, the community economy emerged as the least flexible dimension, exhibiting minimal anticipated change. In conclusion, the C2IMPRESS framework effectively bridges disaster risk reduction and climate adaptation by integrating local knowledge into actionable interventions. However, while social and institutional resilience can be actively enhanced, improving economic resilience requires long-term structural adjustments beyond the scope of these localised tools. Full article