Search Results (691)

UAV small target detection in urban security, disaster monitoring, agricultural inspection, and other fields faces the challenge of increasing accuracy and real-time requirements. However, existing detection algorithms still have weak small target representation ability, extensive computational resource overhead, and poor deployment adaptability. Therefore, this paper proposes a lightweight algorithm, I-YOLOv11n, based on YOLOv11n, which is systematically improved in terms of both feature enhancement and structure compression. The RFCBAMConv module that combines deformable convolution and channel–spatial attention is designed to adjust the receptive field and strengthen the edge features dynamically. The multiscale pyramid of STCMSP context and the lightweight Transformer–DyHead hybrid detection head are designed by combining the multiscale hole feature pyramid (DFPC), which realizes the cross-scale semantic modeling and adaptive focusing of the target area. A collaborative lightweight strategy is proposed. Firstly, the semantic discrimination ability of the teacher model for small targets is transferred to guide and protect the subsequent compression process by integrating the mixed knowledge distillation of response alignment, feature imitation, and structure maintenance. Secondly, the LAMP–Taylor channel pruning mechanism is used to compress the model redundancy, mainly to protect the key channels sensitive to shallow small targets. Finally, K-means++ anchor frame optimization based on IoU distance is implemented to adapt the feature structure retained after pruning and the scale distribution of small targets of UAV. While significantly reducing the model size (parameter 3.87 M, calculation 14.7 GFLOPs), the detection accuracy of small targets is effectively maintained and improved. Experiments on VisDrone, AI-TOD, and SODA-A datasets show that the mAP@0.5 and mAP@0.5:0.95 of I-YOLOv11n are 7.1% and 4.9% higher than the benchmark model YOLOv11 n, respectively, while maintaining real-time processing capabilities, verifying its comprehensive advantages in accuracy, light weight, and deployment. Full article

The rainy season characteristics are directly modulated by atmospheric circulation and moisture transport dynamics. Focusing on the characteristics of the rainy season onset date (RSOD), this study aims to advance the understanding and prediction of climate change impacts on agricultural production and disaster mitigation strategies. Based on rainfall data from 66 meteorological stations in northeast China (NEC) from 1961 to 2020, this study determined the patterns of the RSOD in the region and established its mechanistic linkages with atmospheric circulation and water vapor transport mechanisms. This study identifies a climatic regime shift around 2000, with the RSOD transitioning from low to high interannual variability in NEC. Further analysis reveals a strong correlation between the RSOD and atmospheric circulation characteristics: cyclonic vorticity amplifies before the RSOD and dissipates afterward. Innovatively, this study reveals a significant transition in the water vapor transport paths during the early rainy season in NEC around 2000, shifting from eastern Mongolia–Sea of Japan to the northwestern Pacific region. Moreover, the advance or delay of the RSOD directly influences the water vapor transport intensity—an early (delayed) RSOD is associated with enhanced (weakened) water vapor transport. These findings provide a new perspective for predicting the RSOD in the context of climate change while providing critical theoretical underpinnings for optimizing agricultural strategies and enhancing disaster prevention protocols. Full article

Extreme precipitation events severely impact agriculture, reducing yields and land use efficiency. The spatiotemporal distribution of Total Column Water Vapor (TCWV), the primary gaseous form of water, directly influences sustainable agricultural management. This study, through multi-source data fusion, employs methods including the Mann–Kendall test, sliding change-point detection, wavelet transform, pixel-scale trend estimation, and linear regression to analyze the spatiotemporal dynamics of global TCWV from 1959 to 2023 and its impacts on agricultural systems, surpassing the limitations of single-method approaches. Results reveal a global TCWV increase of 0.0168 kg/m²/year from 1959–2023, with a pivotal shift in 2002 amplifying changes, notably in tropical regions (e.g., Amazon, Congo Basins, Southeast Asia) where cumulative increases exceeded 2 kg/m² since 2000, while mid-to-high latitudes remained stable and polar regions showed minimal content. These dynamics escalate weather risks, impacting sustainable agricultural management with irrigation and crop adaptation. To enhance prediction accuracy, we propose a novel hybrid model combining wavelet transform with LSTM, TCN, and GRU deep learning models, substantially improving multidimensional feature extraction and nonstationary trend capture. Comparative analysis shows that WT-TCN performs the best (MAE = 0.170, R² = 0.953), demonstrating its potential for addressing climate change uncertainties. These findings provide valuable applications for precision agriculture, sustainable water resource management, and disaster early warning. Full article

Floods stand as one of the most harmful natural disasters, which have become more dangerous because of climate change effects on urban structures and agricultural fields. This research presents a comprehensive flood mapping approach that combines multi-sensor satellite data with a machine learning method to evaluate the July 2021 flood in the Netherlands. The research developed 25 different feature scenarios through the combination of Sentinel-1, Landsat-8, and Radarsat-2 imagery data by using backscattering coefficients together with optical Normalized Difference Water Index (NDWI) and Hue, Saturation, and Value (HSV) images and Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture features. The Random Forest (RF) classifier was optimized before its application based on two different flood-prone regions, which included Zutphen’s urban area and Heijen’s agricultural land. Results demonstrated that the multi-sensor fusion scenarios (S18, S20, and S25) achieved the highest classification performance, with overall accuracy reaching 96.4% (Kappa = 0.906–0.949) in Zutphen and 87.5% (Kappa = 0.754–0.833) in Heijen. For the flood class F1 scores of all scenarios, they varied from 0.742 to 0.969 in Zutphen and from 0.626 to 0.969 in Heijen. Eventually, the addition of SAR texture metrics enhanced flood boundary identification throughout both urban and agricultural settings. Radarsat-2 provided limited benefits to the overall results, since Sentinel-1 and Landsat-8 data proved more effective despite being freely available. This study demonstrates that using SAR and optical features together with texture information creates a powerful and expandable flood mapping system, and RF classification performs well in diverse landscape settings. Full article

Wellington, New Zealand, is highly vulnerable to disaster-induced food security crises due to its geography and geological characteristics, which can disrupt transportation and isolate the city following disasters. Urban agriculture (UA) has been proposed as a potential alternative food source for post-disaster scenarios. This study examined the potential of urban agriculture for enhancing post-disaster food security by calculating vegetable self-sufficiency rates. Specifically, it evaluated the capacity of current Wellington’s community gardens to meet post-disaster vegetable demand in terms of both weight and nutrient content. Data collection employed mixed methods with questionnaires, on-site observations and mapping, and collecting high-resolution aerial imagery. Garden yields were estimated using self-reported data supported by literature benchmarks, while cultivated areas were quantified through on-site mapping and aerial imagery analysis. Six post-disaster food demand scenarios were used based on different target populations to develop an understanding of the range of potential produce yields. Weight-based results show that community gardens currently supply only 0.42% of the vegetable demand for residents living within a five-minute walk. This rate increased to 2.07% when specifically targeting only vulnerable populations, and up to 10.41% when focusing on gardeners’ own households. However, at the city-wide level, the current capacity of community gardens to provide enough produce to feed people remained limited. Nutrient-based self-sufficiency was lower than weight-based results; however, nutrient intake is particularly critical for vulnerable populations after disasters, underscoring the greater challenge of ensuring adequate nutrition through current urban food production. Beyond self-sufficiency, this study also addressed the role of UA in promoting food diversity and acceptability, as well as its social and psychological benefits based on the questionnaires and on-site observations. The findings indicate that community gardens contribute meaningfully to post-disaster food security for gardeners and nearby residents, particularly for vulnerable groups with elevated nutritional needs. Despite the current limited capacity of community gardens to provide enough produce to feed residents, findings suggest that Wellington could enhance post-disaster food self-reliance by diversifying UA types and optimizing land-use to increase food production during and after a disaster. Realizing this potential will require strategic interventions, including supportive policies, a conducive social environment, and diversification—such as the including private yards—all aimed at improving food access, availability, and nutritional quality during crises. The primary limitation of this study is the lack of comprehensive data on urban agriculture in Wellington and the wider New Zealand context. Addressing this data gap should be a key focus for future research to enable more robust assessments and evidence-based planning. Full article

Unmanned Aerial Vehicles (UAVs) have rapidly grown into different essential applications, including surveillance, disaster response, agriculture, and urban monitoring. However, for UAVS to steer safely and autonomously, the ability to detect obstacles and nearby aircraft remains crucial, especially under hard environmental conditions. This study comprehensively analyzes the recent landscape of obstacle and aircraft detection techniques tailored for UAVs acting in difficult scenarios such as fog, rain, smoke, low light, motion blur, and disorderly environments. It starts with a detailed discussion of key detection challenges and continues with an evaluation of different sensor types, from RGB and infrared cameras to LiDAR, radar, sonar, and event-based vision sensors. Both classical computer vision methods and deep learning-based detection techniques are examined in particular, highlighting their performance strengths and limitations under degraded sensing conditions. The paper additionally offers an overview of suitable UAV-specific datasets and the evaluation metrics generally used to evaluate detection systems. Finally, the paper examines open problems and coming research directions, emphasising the demand for lightweight, adaptive, and weather-resilient detection systems appropriate for real-time onboard processing. This study aims to guide students and engineers towards developing stronger and intelligent detection systems for next-generation UAV operations. Full article

The need for systems that forecast and respond proactively to meteorological disasters is growing amid climate variability. Although the early warning system in South Korea includes countermeasure information, it remains limited in terms of data recency, granularity, and regional adaptability. Additionally, its closed architecture hinders interoperability with external systems. This study aims to redesign the countermeasure function as an independent cloud-based platform grounded in the common standard terminology framework in South Korea. A multi-dimensional data model was developed using attributes such as crop type, cultivation characteristics, growth stage, disaster type, and risk level. The platform incorporates user-specific customization features and history tracking capabilities, and it is structured using a microservices architecture to ensure modularity and scalability. The proposed system enables real-time management and dissemination of localized countermeasure suggestions tailored to various user types, including central and local governments and farmers. This study offers a practical model for enhancing the precision and applicability of agrometeorological response information. It is expected to serve as a scalable reference platform for future integration with external agricultural information systems. Full article

Using LULC change detection analysis, it is possible to identify changes due to urbanization, deforestation, or a natural disaster in an area. As population growth and urbanization increase, real-time solutions for the effects of urbanization on land use are required to assess its implications for food security and livelihood. This study seeks to identify and quantify recent LULC changes in Limbe, Cameroon, and to measure rates of conversion between agricultural, forest, and urban lands between 1986 and 2020 using remote sensing and GIS. Also, there is a deficiency of research employing these data to evaluate the efficiency of LULC satellite data and a lack of awareness by local stakeholders regarding the impact on LULC change. The changes were identified in four classes utilizing maximum supervised classification in ENVI and ArcGIS environments. The classification result reveals that the 2020 image has the highest overall accuracy of 94.6 while the 2002 image has an overall accuracy of 89.2%. The overall gain for agriculture was approximately 4.6 km², urban had an overall gain of nearly 12.7 km², while the overall loss for forest was −16.9 km² during this period. Much of the land area previously occupied by forest is declining as pressures for urban areas and new settlements increase. This study’s findings have significant policy implications for sustainable land use and food security. It also provides a spatial method for monitoring LULC variations that can be used as a framework by stakeholders who are interested in environmentally conscious development and sustainable land use practices. Full article

This study presents a cost-effective, carbon-negative construction material for affordable housing, developed entirely from locally available agricultural wastes: rice husk ash, wood ash, and rice straw—materials often problematic to dispose of in many African regions. Rice husk ash provides high amorphous silica, acting as a strong pozzolanic agent. Wood ash contributes calcium oxide and alkalis to serve as a reactive binder, while rice straw functions as a lightweight organic filler, enhancing thermal insulation and indoor climate comfort. These materials undergo natural pozzolanic reactions with water, eliminating the need for Portland cement—a major global source of anthropogenic CO₂ emissions (~900 kg CO₂/ton cement). This process is inherently carbon-negative, not only avoiding emissions from cement production but also capturing atmospheric CO₂ during lime carbonation in the hardening phase. Field trials in Kenya confirmed the composite’s sufficient structural strength for low-cost housing, with added benefits including termite resistance and suitability for unskilled laborers. In a collaboration between the University of Tartu and Kenyatta University, a semi-automatic mixing and casting system was developed, enabling fast, low-labor construction of full-scale houses. This innovation aligns with Kenya’s Big Four development agenda and supports sustainable rural development, post-disaster reconstruction, and climate mitigation through scalable, eco-friendly building solutions. Full article

Accurate short-term weather forecasting plays a vital role in disaster response, agriculture, and energy management, where timely and reliable predictions are essential for decision-making. Graph neural networks (GNNs), known for their ability to model complex spatial structures and relational data, have achieved remarkable success in meteorological forecasting by effectively capturing spatial dependencies among distributed weather stations. However, most existing GNN-based approaches rely on pairwise station connections, limiting their capacity to represent higher-order spatial interactions. Moreover, their dependence on supervised learning makes them vulnerable to spatial heterogeneity and temporal non-stationarity. This paper introduces a novel spatial–temporal pretraining framework, Hypergraph-enhanced Meteorological Pretraining (HyMePre), which combines hypergraph neural networks with self-supervised learning to model high-order spatial dependencies and improve generalization across diverse climate regimes. HyMePre employs a two-stage masking strategy, applying spatial and temporal masking separately, to learn disentangled representations from unlabeled meteorological time series. During forecasting, dynamic hypergraphs group stations based on meteorological similarity, explicitly capturing high-order dependencies. Extensive experiments on large-scale reanalysis datasets show that HyMePre outperforms conventional GNN models in predicting temperature, humidity, and wind speed. The integration of pretraining and hypergraph modeling enhances robustness to noisy data and improves generalization to unseen climate patterns, offering a scalable and effective solution for operational weather forecasting. Full article

Unmanned Aerial Vehicle Remote Sensing (UAV-RS) has emerged as a transformative technology in high-resolution Earth observation, with widespread applications in precision agriculture, ecological monitoring, and disaster response. However, a systematic understanding of its scientific evolution and structural bottlenecks remains lacking. This study collected 4985 peer-reviewed articles from the Web of Science Core Collection and conducted a comprehensive scientometric analysis using CiteSpace v.6.2.R4, Origin 2022, and Excel. We examined publication trends, country/institutional collaboration networks, keyword co-occurrence clusters, and emerging research fronts. Results reveal an exponential growth in UAV-RS research since 2015, dominated by application-driven studies. Hotspots include vegetation indices, structure from motion modeling, and deep learning integration. However, foundational challenges—such as platform endurance, sensor coordination, and data standardization—remain underexplored. The global collaboration network exhibits a “strong hubs, weak bridges” pattern, limiting transnational knowledge integration. This review highlights the imbalance between surface-level innovation and deep technological maturity and calls for a paradigm shift from fragmented application responses to integrated systems development. Our findings provide strategic insights for researchers, policymakers, and funding agencies to guide the next stage of UAV-RS evolution. Full article

This research focuses on enhancing cooperative localization for heterogeneous mobile robots composed of a quadcopter and an unmanned ground vehicle. The study employs sensor fusion techniques, particularly the Extended Kalman Filter, to fuse data from various sensors, including GPSs, IMUs, and cameras. By integrating these sensors and optimizing fusion strategies, the research aims to improve the precision and reliability of cooperative localization in complex and dynamic environments. The primary objective is to develop a practical framework for cooperative localization that addresses the challenges posed by the differences in mobility and sensing capabilities among heterogeneous robots. Sensor fusion is used to compensate for the limitations of individual sensors, providing more accurate and robust localization results. Moreover, a comparative analysis of different sensor combinations and fusion strategies helps to identify the optimal configuration for each robot. This research focuses on the improvement of cooperative localization, path planning, and collaborative tasks for heterogeneous robots. The findings have broad applications in fields such as autonomous transportation, agricultural operation, and disaster response, where the cooperation of diverse robotic platforms is crucial for mission success. Full article

The present study was conducted in Dacope Upazila, a sub-district located within the Khulna District of the coastal region in Bangladesh. The research methods employed included the implementation of 350 household questionnaire surveys (HQSs), 12 focus group discussions (FGDs), and 20 key informant interviews (KIIs) to assess economic security status in disaster-vulnerable areas. The findings indicate that the economic well-being of the region is precarious due to a paucity of revenue sources and the occurrence of various calamitous events, induced risks, and vulnerabilities. To achieve long-term economic security for households, a considerable proportion of the population (approximately 22%) in the study areas is dependent on agricultural activities for their livelihoods. The study also revealed that approximately 22% of households in the study areas reported experiencing salinity intrusion. Furthermore, most of the households, around 68%, reported cyclones as their primary obstacle to building disaster-resilient communities. Consequently, the prevailing local and institutional strategies to ensure economic security were found to be inadequate and unsustainable in the study upazila. Therefore, the study resulted in the formulation of a conceptual framework intended to measure the contribution of economic security to the adaptability and sustainability of disaster-resilient communities in vulnerable coastal areas of Bangladesh. Full article

Understanding the time-lagged response of land subsidence to groundwater level fluctuations and subsurface strain variations is crucial for uncovering its underlying mechanisms and enhancing disaster early warning capabilities. This study focuses on Dangshan County, Anhui Province, China, and systematically analyzes the spatio-temporal evolution of land subsidence from 2018 to 2024. A total of 207 Sentinel-1 SAR images were first processed using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to generate high-resolution surface deformation time series. Subsequently, the seasonal-trend decomposition using the LOESS (STL) model was applied to extract annual cyclic deformation components from the InSAR-derived time series. To quantitatively assess the delayed response of land subsidence to groundwater level changes and subsurface strain evolution, time-lagged cross-correlation (TLCC) analysis was performed between surface deformation and both groundwater level data and distributed fiber-optic strain measurements within the 5–50 m depth interval. The strain data was collected using a borehole-based automated distributed fiber-optic sensing system. The results indicate that land subsidence is primarily concentrated in the urban core, with annual cyclic amplitudes ranging from 10 to 18 mm and peak values reaching 22 mm. The timing of surface rebound shows spatial variability, typically occurring in mid-February in residential areas and mid-May in agricultural zones. The analysis reveals that surface deformation lags behind groundwater fluctuations by approximately 2 to 3 months, depending on local hydrogeological conditions, while subsurface strain changes generally lead surface subsidence by about 3 months. These findings demonstrate the strong predictive potential of distributed fiber-optic sensing in capturing precursory deformation signals and underscore the importance of integrating InSAR, hydrological, and geotechnical data for advancing the understanding of subsidence mechanisms and improving monitoring and mitigation efforts. Full article

Background: Drones are rapidly establishing themselves as one of the most critical technologies. Robotics, automated machinery, intelligent manufacturing, and other high-impact technological research and applications bring up pressing ethical, social, legal, and political issues. Methods: The present research aims to present the results of a qualitative investigation that looked at perceptions of the growing socio-ethical conundrums surrounding the development of drone applications. Results: According to the obtained results, participants often share similar opinions about whether different drone applications are approved by the public, regardless of their level of experience. Perceptions of drone applications appear consistent across various levels of expertise. The most notable associations are with military objectives (73%), civil protection (61%), and passenger transit and medical purposes (56%). Applications that have received high approval include science (8.70), agriculture (8.78), and disaster management (8.87), most likely due to their obvious social benefits and reduced likelihood of ethical challenges. Conclusions: The study’s findings can help shape the debate on drone acceptability in particular contexts, inform future research on promoting value-sensitive development in society more broadly, and guide researchers and decision-makers on the use of drones, as people’s attitudes, understanding, and usage will undoubtedly impact future advancements in this technology. Full article