Search Results (9,560)

This study presents a novel top-down approach to quantify diffuse methane (CH₄) emissions at oil and gas well sites. It uses an unmanned aerial vehicle (UAV) equipped with a scanning–sampling tunable diode laser absorption spectroscopy (TDLAS) CH₄ measurement instrument. By integrating the top-down emission rate retrieval algorithm (TERRA) and adopting concentric circular sampling, the method aims to overcome the limitations of traditional ground-based measurements. The UAV system was deployed at 11 oil and gas sites in the Changqing Oilfield. The results show that the average CH₄ emission rate detected by the UAV is 1.425 kg/h (excluding non-detected samples), which is larger than the 1.061 kg/h obtained from ground-based onsite direct measurement. This discrepancy may be because the UAV’s scanning–sampling capability can cover a larger area, capturing scattered or hidden diffuse emission sources that might be missed by ground-based onsite direct measurement. The study demonstrates that the UAV-based approach with a scanning–sampling TDLAS CH₄ measurement instrument, integrated with the TERRA and concentric circular sampling, is effective in capturing diffuse CH₄ emissions at oil and gas well sites, providing a viable method for large-scale and efficient monitoring of such emissions. This approach could provide an effective pathway for large-scale, efficient, and cost-effective monitoring of methane emissions. Full article

Early prediction of common bean (Phaseolus vulgaris L.) yield is essential for improving productivity in tropical agricultural systems. In this study, we integrated canopy structural metrics obtained with the Tracing Radiation and Architecture of Canopies (TRAC) system, unmanned aerial vehicle (UAV)-based multispectral measurements (normalized difference vegetation index—NDVI, projected canopy area), and phenological variables collected from stages R6 to R8 under non-limiting nitrogen conditions. Exploratory analyses (correlation, variance inflation factors—VIF), dimensionality reduction (principal component analysis—PCA), and regularized regression (Elastic Net/LASSO), combined with bootstrap stability selection, were applied to identify a parsimonious subset of robust predictors. The final model, composed of six variables, explained approximately 72% of the variability in plant-level grain yield, with acceptable errors (RMSE ≈ 10.67 g; MAE ≈ 7.91 g). The results demonstrate that combining early vigor, radiation interception, and canopy architecture provides complementary information beyond simple spectral indices. This non-destructive framework delivers an efficient model for early yield estimation and supports site-specific management decisions in common bean with high spatial resolution. By enhancing input-use efficiency and reducing waste, this approach contributes to sustainable development and aligns with the global Sustainable Development Goals (SDGs) for climate-resilient agriculture. Full article

Vehicle-mounted multi-UAV (VM-UAV) systems offer enhanced flexibility and rapid deployment for large-scale remote sensing tasks such as disaster response and land surveys. However, maximizing their operational efficiency remains challenging, as it requires the simultaneous resolution of task scheduling and coverage path planning—an NP-hard problem. This study presents a novel multi-objective genetic algorithm (GA) framework that jointly optimizes routing and scheduling for cost-constrained, load-balanced multi-UAV remote sensing missions. To improve convergence speed and solution quality, we introduce two innovative operators: a Multi-Region Edge Recombination Crossover (MRECX) to preserve superior path segments from parents and an Adaptive Hybrid Mutation (AHM) mechanism that dynamically adjusts mutation strategies to balance exploration and exploitation. The algorithm minimizes total flight distance while equalizing workload distribution among UAVs. Extensive simulations and experiments demonstrate that the proposed GA significantly outperforms conventional GA, particle swarm optimization (PSO), ant colony optimization (ACO), and clustering-based planning methods in both solution quality and robustness. The practical applicability of our framework is further validated through two real-world case studies. The results confirm that the proposed approach delivers an effective and scalable solution for vehicle-mounted multi-UAV scheduling and path planning, enhancing operational efficiency in time-critical remote sensing applications. Full article

To enhance the battery endurance of unmanned aerial vehicles (UAVs), this article addresses key issues in traditional wireless power transfer (WPT) systems. These issues occur during constant current/constant voltage (CC/CV) switching, such as poor stability, high payload, power loss, and charging instability. Accordingly, a WPT system based on topology switching is proposed. First, a lightweight compensation topology based on LCC-Series compensated topology (LCC-S) is designed. A tuning capacitor is incorporated, and two switches regulate the switching of the compensation capacitor to realize CC/CV mode transition. Meanwhile, the impedance matrix model is built to find optimal compensation component values, maximizing energy transfer. To reduce sensitivity to misalignment, a “+” shaped compensation coil is added to the basic 2 × 2 square coil array. It improves magnetic field uniformity and suppresses flux leakage. Experimental results show that the system achieves stable load-independent output. Within horizontal offset [−150, 150] mm and diagonal offset [−150√2, 150√2] mm, it keeps output power over 150 W and efficiency over 70%, with strong anti-misalignment ability. This system effectively solves key challenges such as endurance bottlenecks, complex CC/CV switching, and weak anti-misalignment. It offers a reliable technical solution for efficient charging of autonomous UAVs. Full article

The pine wood nematode (PWN), one of the globally significant forest diseases, has driven the demand for precise detection methods. Recent advances in satellite remote sensing technology, particularly ultra-high-resolution optical imagery, have opened new avenues for identifying PWN-infected trees. In order to systematically evaluate the ability of ultra-high-resolution optical remote sensing and the influence of spatial and spectral resolution in detecting PWN-infected trees, this study utilized a U-Net network model to identify PWN-infected trees using three remote sensing datasets of the ultra-high-resolution multispectral imagery from Beijing 3 International Cooperative Remote Sensing Satellite (BJ3N), with a panchromatic band spatial resolution of 0.3 m and six multispectral bands at 1.2 m; the high-resolution multispectral imagery from the Beijing 3A satellite (BJ3A), with a panchromatic band resolution of 0.5 m and four multispectral bands at 2 m; and unmanned aerial vehicle (UAV) imagery with five multispectral bands at 0.07 m. Comparison of the identification results demonstrated that (1) UAV multispectral imagery with 0.07 m spatial resolution achieved the highest accuracy, with an F1 score of 89.1%. Next is the fused ultra-high-resolution BJ3N satellite imagery at 0.3 m, with an F1 score of 88.9%. In contrast, BJ3A imagery with a raw spatial resolution of 2 m performed poorly, with an F1 score of only 28%. These results underscore that finer spatial resolution in remote sensing imagery directly enhances the ability to detect subtle canopy changes indicative of PWN infestation. (2) For UAV, BJ3N, and BJ3A imagery, the identification accuracy for PWN-infected trees showed no significant differences across various band combinations at equivalent spatial resolutions. This indicates that spectral resolution plays a secondary role to spatial resolution in detecting PWN-infected trees using ultra-high-resolution optical imagery. (3) The 0.3 m BJ3N satellite imagery exhibits low false-detection and omission rates, with F1 scores comparable to higher-resolution UAV imagery. This indicates that a spatial resolution of 0.3 m is sufficient for identifying PWN-infected trees and is approaching a point of saturation in a subtropical mountain monsoon climate zone. In conclusion, ultra-high-resolution satellite remote sensing, characterized by frequent data revisit cycles, broad spatial coverage, and balanced spatial-spectral performance, provides an optimal remote sensing data source for identifying PWN-infected trees. As such, it is poised to become a cornerstone of future research and practical applications in detecting and managing PWN infestations globally. Full article

In the context of digital transformation for industrial heritage conservation propelled by China’s National Industrial Heritage Management Measures, evidence regarding the trade-offs among accuracy, completeness, and efficiency within the acquisition–registration–integration pipeline, as well as transferable methodologies, remains inadequate. Addressing key challenges in information integration for industrial architectural heritage in Inner Mongolia—such as fragile media, weak sustainability, and severe information silos—demands a systematic solution. This paper proposes a BIM-based three-dimensional digital preservation framework centered on “Space-Time-Value” and empirically validates its workflow effectiveness and database interoperability. Focusing on the Inner Mongolia University of Technology Architecture Building, a prime exemplar of adaptive reuse in the region, we employed terrestrial 3D laser scanning and Unmanned Aerial Vehicle (UAV) oblique photogrammetry to acquire a 13.8-billion-point cloud. Using Autodesk Revit, we developed an LOD400 model (comprising 12 component types and 349 parametric families), achieving systematic integration of structural data, spatial evolution information, and non-geometric attributes. Comparative evaluation shows that this workflow outperforms baselines in geometric accuracy, facade completeness, and processing efficiency, while significantly enhancing the integration and retrieval capabilities for heterogeneous data. The research establishes a “Multi-source Data Integration + Sustainable Utilization” digital paradigm for industrial architectural heritage, providing a replicable methodology for whole-life-cycle management and adaptive reuse. Full article

In geophysics, aeromagnetic surveying based on unmanned aerial vehicles (UAV) is a widely employed exploration technique, that can analyze underground structures by conducting data acquisition, processing, and inversion. This method is highly efficient and covers large areas, making it widely applicable in mineral exploration, oil and gas surveys, geological mapping, and engineering and environmental studies. However, during flight, interference from the aircraft’s engine, electronic systems, and metal structures introduces noise into the magnetic data. To ensure accuracy, mathematical models and calibration techniques are employed to eliminate these aircraft-induced magnetic interferences. This enhances measurement precision, ensuring the data faithfully reflect the magnetic characteristics of subsurface geological features. This study focuses on aeromagnetic data processing methods, conducting numerical simulations of magnetic interference for aeromagnetic surveys of UAVs with the Tolles–Lawson (T-L) model. Recognizing the temporal dependencies in aeromagnetic data, we propose a Transformer neural network algorithm for aeromagnetic compensation. The method is applied to both simulated and measured flight data, and its performance is compared with the classical Multilayer Perceptron neural networks (MLP). The results demonstrate that the Transformer neural networks achieve better fitting capability and higher compensation accuracy. Full article

With the development of unmanned aerial vehicle (UAV) technology, multimachine collaborative operations have become the core model for increasing mission effectiveness. However, large-scale UAV clusters face challenges such as dynamic security threats, heterogeneous data fusion difficulties, and resource-constrained decision-making delays. Traditional single-machine intelligent architectures have limitations when addressing new threats, such as insufficient real-time response capabilities. To address these issues, this paper presnts an LLM-layered collaborative security architecture (LLM-LCSA) for multimachine collaborative security. This architecture optimizes the spatiotemporal fusion efficiency of multisource asynchronous data through cloud–edge–end collaborative deployment, combining an end lightweight LLM, an edge medium LLM, and a cloud-based foundation LLM. Additionally, a Mixture of Experts (MoEs) intelligent algorithm that dynamically activates the most relevant expert models by leveraging a threat–expert association matrix is introduced, thereby increasing the accuracy of complex threat identification and dynamic adaptability. Moreover, a resource-aware multi-objective optimization model is constructed to generate optimal decisions under resource constraints. Simulation results indicate that compared with traditional methods, LLM-LCSA achieves an average 7.92% improvement in the threat detection accuracy, reduces the system’s total response time by 44.52%, and enables resource scheduling during off-peak periods. This architecture provides an efficient, intelligent, and scalable solution for secure collaboration among UAV swarms. Future research should further explore its application potential in 6G network integration and large-scale swarm environments. Full article

Advanced and Urban Air Mobility (AAM and UAM) represent emerging transportation concepts that involve the use of novel aircraft technologies to transport passengers and cargo within urban, regional, and intra-regional environments. These systems may include Electric Vertical Take-off and Landing (eVTOL) aircraft, Short Take-off and Landing (STOL) aircraft, and unmanned aerial vehicles (UAVs), which are being considered for a range of applications including passenger transport, cargo delivery, and other specialized operations. This study introduced a state-specific analytical framework that integrates different methodologies and data to enable a more precise evaluation of AAM viability in the State of Illinois, compared to generic national or global assessments, capturing the state’s unique mobility patterns, infrastructure constraints, and demographic distributions. One of the main goals is to provide a comprehensive evaluation of the potential implications—both challenges and opportunities—associated with AAM and UAM operations. The analysis examines potential impacts on mobility, infrastructure, economic development, and public services, with particular emphasis on identifying key considerations for policy development. The research framework categorizes use cases into two broad types: AAM for the transportation of people and cargo, and AAM for functional applications such as emergency response, agriculture, and infrastructure monitoring. The study provides a detailed quantitative assessment of passenger air taxi services, including demand estimation, business model feasibility analysis, integration effects on existing transportation systems, and infrastructure requirements. For other AAM applications, the analysis identifies operational considerations, regulatory implications, and potential barriers to implementation, establishing a foundation for future detailed evaluation. Full article

In this paper, we propose a cooperative security method for unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to address the scenario of unauthorized rogue drones (RDs) intruding into an airport’s restricted airspace. The proposed method integrates artificial intelligence techniques with engineering solutions to enhance the autonomy and effectiveness of air–ground cooperation in airport security. Specifically, the MADDPG algorithm enables the Security Interception UAVs (SI-UAVs) to autonomously detect and counteract RDs by optimizing their decision-making processes in a multi-agent environment. Additionally, Particle Swarm Optimization (PSO) is employed for distance-based target assignment, allowing each SI-UAV to autonomously select intruder targets based on proximity. To address the challenge of limited SI-UAV flight range, a power replenishment mechanism is introduced, where each SI-UAV automatically returns to the nearest UGV for recharging after reaching a predetermined distance. Meanwhile, UGVs perform ground patrols across different airport critical zones (e.g., runways and terminal perimeters) according to pre-designed patrol paths. The simulation results demonstrate the feasibility and effectiveness of the proposed security strategy, showing improvements in the reward function and the number of successful interceptions. This approach effectively solves the problems of target allocation and limited SI-UAV range in multi-SI-UAV-to-multi-RD scenarios, further enhancing the autonomy and efficiency of air–ground cooperation in ensuring airport security. Full article

Virtual reality (VR) creates immersive environments that allow users to interact with digital content, fostering a sense of presence and engagement comparable to real-world experiences. VR360 technology, combined with affordable head-mounted displays such as Google Cardboard, enhances accessibility and provides an intuitive learning experience. Drones, or unmanned aerial vehicles (UAVs), are operated through remote control systems and have diverse applications in civilian, commercial, and scientific domains. Taiwan’s Indigenous cultures emphasize environmental conservation, and integrating this knowledge into education supports both biodiversity and cultural preservation. The Amis people, who primarily reside along Taiwan’s eastern coast and central mountain regions, face educational challenges due to geographic isolation and socioeconomic disadvantage. This study integrates VR360 and drone technologies to develop a VR learning system for elementary science education that incorporates Amis culture and local environments. A teaching experiment was conducted to evaluate its impact on learning effectiveness and student responses. Results show that students using the VR system outperformed the control group in cultural and scientific knowledge, experienced reduced cognitive load, and reported greater learning motivation. These findings highlight the potential of VR and drone technologies to improve learning outcomes, promote environmental and cultural awareness, and reduce educational barriers for Indigenous students in remote or socioeconomically disadvantaged communities. Full article

In recent years, unmanned aerial vehicle (UAV) technology has been increasingly widely used in natural disaster rescue. To enable fast and accurate localization of rescue targets in disaster environments, this paper proposes a multi-level coordinate system transformation method for quadrotor UAVs based on monocular camera position compensation. First, the preprocessed image object is transformed from pixel coordinates to camera coordinates. Second, to address the issue that coupling errors between the camera and UAV coordinate systems degrade the accuracy of coordinate conversion and target positioning, a Static–Dynamic Compensation Model (SDCM) for UAV camera position error is established. This model leverages a UAV attitude-based compensation mechanism to enable accurate conversion of camera coordinates to UAV coordinates and north-east-down (NED) coordinates. Finally, according to the Earth model, a multi-level continuous conversion chain from the target coordinates to the Earth-centered–Earth-fixed (ECEF) coordinates and the world-geodetic-system 1984 (WGS84) coordinates is constructed. Extensive experimental results show that the accuracy of the overall positioning method is improved by approximately 23.8% after completing our camera position compensation, which effectively enhances the positioning performance under the basic method of coordinate transformation, and provides technical support for the rapid rescue in the post-disaster phase. Full article

Sampling-based algorithms are pivotal for high-dimensional UAV path planning, especially in 3D urban environments. The Rapidly-Exploring Random Tree (RRT) suffers from inadequate sampling methods and a single, fixed sampling policy, which lead to elongated paths and higher computational cost. To address this, we propose a Dynamic Adaptive DACS-RRT* algorithm that builds a dynamic, bidirectional sampling space and fuses low-discrepancy Halton sampling with bridge (narrow-passage) sampling, fundamentally tailoring the sampling process to urban settings. We further construct an adaptive, coordinated sampling strategy that dynamically adjusts between straight-to-goal and frustum-cone expansions by computing their probabilities, thereby overcoming the limitations of a single strategy and strengthening directional guidance. After generating a path, we perform multi-objective smoothing to make UAV trajectories better suited to urban environments. Through simulations in three distinct urban scenarios—and in comparison with five baseline algorithms—DACS-RRT* shows improvements in path length, convergence time, node count, iteration count, obstacle clearance, and turning angle, further validating its practicality in urban settings. Full article

The safeguarding and enhancement of historic buildings and artifacts in Italy’s inner areas are essential to protect their outstanding cultural value. However, these territories often face complex orographic and environmental conditions that make traditional surveying and documentation challenging. To address these issues, this study proposes a framework for the digitalization and virtual dissemination of architectural heritage aimed at supporting safe and sustainable tourism. The proposed approach integrates unmanned aerial vehicle (UAV) photogrammetry with laser scanning to produce three-dimensional models of historic structures. These digital models are then semantically enriched and simplified for use within a web-based virtual reality (VR) platform, enabling interactive learning experiences for increase cultural heritage accessibility. The framework is validated through the case study of the Roccapreturo Tower in Acciano (AQ), located in the inner areas of the Abruzzo region, a landscape characterized by high morphological complexity. Results demonstrate the effectiveness of drone photogrammetry in capturing detailed and accurate representations of cultural heritage assets while ensuring operational efficiency and accessibility. The resulting VR models promote heritage safeguarding and sustainable tourism, confirming the potential of UAV-based technologies in the digital transformation of cultural heritage. Full article

Global brands are increasingly establishing dedicated administrative departments to strengthen sustainability and resilience in their supply chains. However, overlooking these aspects at the supplier level can result in significant costs and systemic vulnerabilities. This study addresses this gap through four key contributions: First, we provide a comprehensive sustainability assessment by simultaneously considering economic, environmental, and social pillars along with resilience, operationalized through twenty-four sub-criteria. Second, we explicitly incorporate human judgment and uncertainty by modeling supplier evaluation with interval weights, capturing the ambiguity and subjectivity inherent in expert decision-making. Third, we propose a novel hybrid methodology, integrating lexicographic goal programming (LGP), the analytical hierarchy process (AHP), and two-stage logarithmic goal programming (TLGP) in a systematic framework. Finally, we validate the approach in real-world contexts through case studies in the electronics and unmanned aerial vehicle (UAV) industries. The results reveal notable differences in supplier rankings when comparing LGP and TLGP, highlighting the methodological implications of advanced goal programming in uncertain environments. Overall, this study advances supplier selection research by offering both a validated decision-support tool for practitioners and methodological insights for scholars working on sustainability and resilience under uncertainty. Full article