Search Results (11,328)

Monitoring forest regeneration is vital for sustainable management, yet traditional ground surveys and early aerial imagery methods face cost, labor, and resolution limitations. Unmanned Aerial Vehicles (UAVs) offer cost-effective, flexible platforms for acquiring ultra-high-resolution data to address these gaps. This paper reviews recent research on UAV applications in forest regeneration surveys (FRS), tracing the evolution from field-based surveys and conventional aerial approaches to current UAV practices, and synthesizing developments in data acquisition, processing workflows, and analysis. It contrasts established Canopy Height Model (CHM) and point-cloud approaches with the growing use of deep learning, particularly Convolutional Neural Networks (CNNs) for seedling detection, crown delineation, density, height estimation, and species classification. Particular attention is given to accuracy assessment, examining sampling design, reference data, prediction-to-reference matching, and evaluation metrics, and highlighting the disconnect between traditional map-validation principles and standard deep-learning metrics that often neglect background classes. The case study applying Mask R-CNN to red pine seedlings in an Adirondack Park plantation achieved stand-level recall of 70.3% and precision of 98.7%, while plot-level DL detections represented only 36.8% of the field-observed seedling count. These results demonstrate the potential of DL for reliably identifying visible red pine seedlings while highlighting its limitations for complete regeneration inventories, particularly when seedlings have small crown sizes. Full article

Vision-based unmanned aerial vehicle (UAV) detection has become increasingly important since it is a key technology in aerial collision avoidance systems. However, the detection of small UAVs is still unsatisfactory in practical applications. To address this problem, this paper proposes EWH-YOLO, a novel deep convolutional neural network-based method for small UAV detection. First, an efficient feature extraction network is designed to exact the multi-level features of small UAVs while reducing the network parameters and computational complexity. Second, a weighted bidirectional feature fusion network is proposed to enhance the low-level and high-level features in the output feature maps. Third, a hybrid bounding box regression loss is introduced to evaluate the difference between the predicted bounding box and the ground-truth bounding box during training and improve the detection accuracy. Finally, a new dataset is created on the basis of considering small UAVs to verify the detection performance. Compared with the state-of-the-art methods, the proposed method achieves higher detection accuracy with lower model complexity. The experimental results demonstrate that the proposed detector significantly improves the detection performance of small UAVs. Full article

While there are evident advantages to deploying UAVs for structural inspection applications within reinforced-concrete structures where human access may be hazardous or restricted, UAV deployment is still inhibited within structures due to a lack of GPS, payload and power limitations, signal attenuation, limited transmission opportunities, and low bandwidth for communication. Within the confines of the present project, a low-cost two-layer six-rotor architecture was developed; a single Pixhawk PX4 manages stabilized flight, while a Raspberry Pi 4 manages mission-level operations. The complete prototype integrated a relative-pose/nearby-object estimator, hybrid 433 MHz and Wi-Fi/MQTT communications, avionics-side energy management, mission continuity via SQLite, and human-in-the-loop fail-safe support. Prototype experiments revealed reduced horizontal drift compared to both tested comparison configurations, continued operation under constrained communication conditions, and a 62.0% reduction in avionics-side power consumption, excluding propulsion. This was followed by a complementary PX4–Gazebo evaluation probing horizontal and vertical proximity responses, six-sector LiDAR processing, and stale-data watchdog functionality. Across 45 repeated simulation runs and 3000 retained sector-level observations, no simulated collisions occurred during the horizontal-approach, vertical-proximity, or watchdog tests. No false sector assignments were observed, and the overall mean absolute error was 0.0285 m. From this limited demonstration, the architecture appears satisfactory at the prototype and simulated-subsystem levels. Further physical testing will be needed prior to operational deployment. Full article

The aerodynamic characterization of propeller-driven UAVs is often constrained by the unfeasibility of testing the complete airframe–propeller assembly in a wind tunnel, since geometric scaling prevents simultaneous similarity of both the airframe and the propeller. To address this limitation, this work presents an integrated experimental–numerical methodology that reconstructs the full-scale free-air aerodynamic behaviour of a tractor-propeller UAV combining wind-tunnel measurements of the scaled airframe (without the propeller) and the full-scale propeller. Computational fluid dynamics (CFD) is not used to predict the full-scale UAV directly; it is used to predict differences between matched configurations, while the absolute aerodynamic level remains anchored to experiments. Dedicated CFD simulations are carried out to isolate three distinct physical contributions: scale effects, wind-tunnel blockage, and propeller installation effects. In the developed methodology, numerical simulations complement the experimental data to obtain corrected full-scale aerodynamic coefficients and propulsive maps together with a longitudinal force-equilibrium model used to determine the longitudinal force-equilibrium operating point. The reconstruction shows that scale and wind-tunnel blockage effects primarily alter the airframe aerodynamic characteristics, with a minor influence on equilibrium incidence, while propeller installation produces a substantial thrust augmentation due to airframe-induced inflow modification. Accounting for these effects leads to an overprediction of the propeller rotational speed by approximately $23\%$ when installation effects are neglected, demonstrating that the installed performance cannot be obtained by a linear superposition of isolated airframe and isolated propeller data. Full article

Unmanned aerial vehicles (UAVs) have been increasingly used as flexible sensing platforms for remote sensing applications due to their rapid deployment and efficient data acquisition capabilities. Cooperative vehicle–UAV systems have shown great potential for large-scale remote sensing monitoring and field verification. However, existing task planning methods often overlook the characteristics of remote sensing verification missions, including fragmented target parcels and spatiotemporal uncertainties caused by complex terrain, which limits scheduling efficiency and robustness. To address these challenges, this paper proposes a spatiotemporal uncertainty-aware task planning framework for vehicle–UAV cooperative remote sensing verification. The framework integrates UAV capability-constrained task region generation, terrain-driven spatial uncertainty risk classification, a dual-channel genetic algorithm (DC-GA), and an uncertainty-aware two-stage scheduling framework (UATSF). Experiments in two real-world study areas validate the effectiveness of the proposed framework. The region-merging strategy reduces total travel distance and travel time while improving UAV utilization, and DC-GA consistently reduces the system makespan across different vehicle configurations. Moreover, the two-stage strategy, which combines deterministic optimization with Monte Carlo robustness assessment, reduces planned completion time by 6.80–11.09% compared with worst-case scheduling while achieving 86.20–98.40% reliability under the modeled uncertainty and assumed simulation settings. The results demonstrate that the proposed framework improves task planning efficiency and robustness for vehicle–UAV cooperative operations in complex terrain environments. Full article

This study assessed the integration of Unmanned Aerial Vehicles (UAVs) and satellite images (Sentinel-1 and Sentinel-2) in advanced machine-learning techniques to monitor the ABV of quinoa crops (Jacha Grano variety) across the Bolivian Altiplano. The proposed method follows a two-step procedure. First, UAV RGB images were used in photogrammetric and deep-learning (Convolutional Neural Networks-CNN) models to estimate reference quinoa ABVs at a 10 m spatial resolution from the crop canopy 3D model and classification, respectively. Secondly, several spectral and polarization/texture indices derived from Sentinel-2 and -1 images were integrated into three decision-tree-based machine-learning models (Random Forest-RF, Gradient Boosting-GB, eXtreme Gradient Boosting-XGB), and one CNN-based machine-learning model to estimate ABV. Additionally, a Stacking Model (STM) build on top of the three decision-tree-based models was considered for comparison. Model evaluation was also performed in a two-step approach. First, a 10-fold cross-validation strategy was used to highlight ABV sensitivity to Sentinel-2 and Sentinel-1 alone and in combination. Secondly, a Leave-One-Plot-Out Cross-Validation (LOPOCV) strategy was used to avoid autocorrelation between the training and evaluation dataset and therefore provided more insight into ABV mapping potential. The results showed that the combination of Sentinel-1 and Sentinel-2 features in the CNN model achieved the best predictive performance with R² and RMSE values of 0.64 and 0.39 m³ ∙ 100 m⁻², respectively. These findings highlight the potential of integrating multi-source information in advanced artificial intelligence algorithms for quinoa ABV monitoring, offering new insights toward the identification of sustainable practices across remote regions with complex socio-economic contexts. Full article

In multi-beam low-Earth-orbit (LEO) satellite communication networks, high-mobility aerial users, such as unmanned aerial vehicles (UAVs), high-speed aircraft, and near-space vehicles, may traverse multiple satellite beams within a short period of time. When the precise position of a target user is not continuously available to the network, the network needs to determine the set of beams in which the user is likely to be located when a paging request arrives. The corresponding communication satellites then transmit paging messages within these candidate beams to reach the target user. If the selected beam set does not cover the user’s actual position, the paging attempt fails; however, excessively enlarging the paging region or frequently updating the user’s location information introduces additional signaling and management overhead. Therefore, the key problem is to construct an accurate and adaptive paging region under the joint mobility of the user and LEO satellite beams. To address this problem, this paper proposes a network-side sensing- and prediction-aided dynamic location-area management method for high-mobility users. First, based on a three-stage motion model of high-mobility users, LEO satellite ephemeris information, and beam coverage parameters, the coverage performance during the whole flight process of high-mobility users is analyzed. Second, a high-mobility user state prediction mechanism integrating a three-stage motion model and square-root cubature Kalman filtering (TSM-SRCKF) is proposed. This mechanism can adaptively adjust the weights of different motion models according to the current motion state of the high-mobility user and suppress the influence of abnormal measurements during the measurement update process, thereby obtaining more reliable position prediction results and error covariance information. Finally, a TSM-SRCKF-aided dynamic location-area management method is proposed. Simulation results show that the root-mean-square error of the high-mobility user position under the proposed mechanism is only 23.3% of that of the comparison mechanism. Compared with the traditional velocity-based dynamic location area design method, the proposed method improves the paging success probability by about 60.1% and reduces the cumulative total management overhead by about 73%. Full article

The integration of unmanned aerial vehicles (UAVs) as aerial base stations has emerged as a key enabler for next-generation wireless networks, particularly in disaster recovery, temporary events, and infrastructure-deficient regions. However, multi-UAV deployments introduce severe co-channel interference due to spectrum reuse and overlapping coverage areas, while existing spectrum allocation methods either rely on centralized optimization with limited scalability or on reinforcement learning frameworks that lack spatial awareness of interference sources. To address these challenges, this paper proposes a Hybrid DeepMUSIC-assisted Cooperative Multi-Agent Deep Reinforcement Learning (MADRL) framework for intelligent spectrum allocation and interference management in multi-UAV 6G networks. The proposed framework integrates a hybrid interference localization module, which fuses the classical MUltiple SIgnal Classification (MUSIC) algorithm with a deep neural network to accurately estimate the direction of arrival (DoA) of interference sources, into a DeepMUSIC-enhanced state representation used by cooperative Deep Q-Network (DQN) agents trained under a Centralized Training and Decentralized Execution (CTDE) paradigm, enabling coordinated yet fully distributed spectrum allocation decisions. Extensive simulations demonstrate that the proposed Hybrid DeepMUSIC module reduces the mean DoA estimation error to approximately 0.105°, more than an order of magnitude better than classical MUSIC and standalone DeepMUSIC estimators. Compared with seven baseline algorithms spanning heuristic, optimization-based, single-agent, and cooperative multi-agent reinforcement learning approaches, the proposed framework achieves the highest network throughput, SINR, spectrum efficiency, and energy efficiency, together with the fastest and most stable training convergence, reaching a stable cooperative reward of 76.246 within approximately 371 training epochs. The framework further maintains near-linear computational scaling with the number of UAV agents, confirming its suitability for real-time deployment in dense, AI-native multi-UAV 6G wireless communication systems. Full article

A series of Ni_xCo_3−xO₄ (x = 0.75, 1, 1.5, 2, 2.25) catalysts with different Ni/Co molar ratios was fabricated via the co-precipitation method. The catalytic performance for CO oxidation and SO₂ resistance of the prepared catalysts was systematically investigated, and their physicochemical properties were characterized by XRD, SEM, BET, H₂-TPR, CO-TPD and in situ DRIFTS. The experimental results reveal that the Ni₂.₂₅Co₀.₇₅O₄ catalyst exhibits the optimal CO oxidation activity, achieving a CO conversion of 93.25% at 120 °C. The superior catalytic performance can be attributed to its large specific surface area, low reduction temperature, and easily activated lattice oxygen species. When exposed to SO₂ at a concentration 10 times the industrial emission limit, all catalysts exhibited varying degrees of activity loss. Among them, NiCo₂O₄ exhibited the slowest deactivation rate and showed the greatest recovery in CO conversion after SO₂ was cut off, suggesting relatively better sulfur tolerance and recoverability among the investigated catalysts. In conclusion, tuning the Ni/Co molar ratio can effectively optimize the low-temperature CO oxidation activity and sulfur resistance of Ni-Co composite oxides. This work provides a useful reference for the structural composition design and practical application of such catalysts in flue gas purification. Full article

Unmanned aerial vehicles (UAVs) are progressively evolving from remotely operated platforms into intelligent autonomous systems. This research addresses the role of Artificial Intelligence (AI) in advancing UAV capabilities and expanding their applications across diverse sectors. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 methodology, studies retrieved from Scopus and relevant academic books and book chapters were screened, resulting in 217 publications retained for final analysis. The analysis introduces a three-layer framework linking AI techniques, UAV functional capabilities, and application domains. The first layer covers the list of adopted AI and ML approaches in the UAV applications. The second layer maps these approaches to key UAV capabilities, including perception, autonomous navigation, control and stability, swarm coordination, communication, and energy optimization. The third layer examines applications in agriculture, logistics, disaster response, environmental monitoring, surveillance, defense, and wireless network systems. The findings show that deep learning enhances aerial perception, reinforcement learning supports adaptive navigation and control, federated learning improves distributed intelligence, and swarm intelligence enables cooperative multi-UAV missions. Despite these advances, AI-enabled UAVs still face challenges related to energy consumption, onboard computation, data availability, communication reliability, safety, ethics, privacy, and regulation. Future progress is expected to be driven by edge AI, Tiny Machine Learning (TinyML), quantum-inspired optimization, explainable artificial intelligence (XAI), human-AI collaboration, and robust swarm coordination. Overall, this review provides a structured synthesis of AI-enabled UAV research and identifies key directions for future innovation. Full article

Unmanned aerial vehicle (UAV) inspection of transmission towers requires joint analysis of corridor geometry, tower components, and localized defects, whereas available datasets provide incompatible annotations at different scales. Tower-HMT is a hierarchical multitask network that shares a ConvNeXt-Tiny encoder and feature pyramid across corridor parsing, component parsing, missing-bolt localization, and component-condition recognition. Four public real-image datasets are organized by native supervision rather than merged into a flat label space. Task-conditioned feature modulation separates dataset statistics; topology and boundary losses preserve thin conductors and lattice edges; and a detached tower-probability gate supplies structural context to a high-resolution bolt head. Source-image groups define non-overlapping training, validation, and test partitions. On held-out data, the network achieved foreground mIoU values of 0.597 for tower-conductor parsing and 0.561 for box-conditioned component parsing, an AP50 of 0.123 for missing-bolt localization, and a macro-F1 of 0.925 for component-condition recognition. Boundary, class-wise, robustness, threshold-sensitivity, and latency results quantify the effects and limitations of hierarchical learning. The framework provides a reproducible real-image baseline for review-oriented inspection, while low missing-bolt localization accuracy and weak component masks remain the principal constraints. Full article

Small cargo unmanned aerial vehicles (UAVs) operating in narrow confined spaces suffer instantaneous collision impacts that induce gyroscope heteroscedastic noise and abrupt angular velocity derivatives, triggering severe dynamic attitude errors in traditional strapdown inertial navigation systems (SINS). Existing algorithms separate angular velocity fitting and noise suppression, adopt unified three-axis weighting, and lack adaptive segmentation for collision disturbances, limiting navigation accuracy without raising computational costs. This paper proposes an integrated heteroscedastic decoupling algorithm for UAV SINS under collision interference. Hermite orthogonal polynomials are utilized to fit non-stationary angular velocity with derivative matching constraints, an optimized single-pass CUSUM detector with steady-state residual compensation is proposed to identify collision-induced variance change points. An axis-differentiated weighting strategy is developed to suppress heteroscedastic noise. Recursive least-squares is adopted to lower online computation overhead. Multi-condition coning motion simulations show Hermite polynomials achieve the lowest attitude RMSE under steady flight; the improved CUSUM detector delivers shorter detection delay, fewer false alarms, and lighter computation than mainstream detection methods, and segmented differentiated weighting eliminates collision-induced noise distortion at the raw measurement stage. The proposed algorithm unifies signal fitting and noise correction with minimal computational overhead, effectively mitigating dynamic errors for lightweight airborne navigation hardware and offering a high-precision front-end preprocessing solution for cargo UAVs operating in cluttered obstacle environments. The proposed algorithm is positioned as a gyro-only front-end preprocessing module; accelerometer-related error compensation and full multi-sensor back-end integration are addressed in ongoing work. Full article

Reliable coordination of unmanned aerial vehicle (UAV) swarms is significantly challenged by random denial-of-service (R-DoS) attacks, which introduce stochastic packet loss, time-varying communication interruptions, and strong concealment. Existing fault-tolerant consensus approaches typically assume known attack information or treat attack detection, topology recovery, and control design as separate processes, resulting in limited resilience under dynamically evolving attack conditions. To address this issue, this paper proposes a detection-guided resilient consensus control framework for UAV swarms under R-DoS attacks. A dual-dimensional statistical detection method is developed by jointly modeling packet reception rate (PRR) and inter-arrival time (IAT), enabling real-time identification of attack-induced anomalies through spatio-temporal feature fusion. Based on the detection results, a distributed topology reconstruction strategy is designed, incorporating redundant node identification and cluster-based dynamic communication reconfiguration. The communication graph is adaptively updated via online adjustment of adjacency and Laplacian matrices, and robustness guarantees for the resulting consensus process are analytically established. Hardware-in-the-loop simulation experiments under both single-leader and multi-leader architectures demonstrate that the proposed method can accurately detect attacked nodes, effectively reconstruct the communication topology, and maintain stable formation coordination under severe R-DoS attacks. The position tracking error is constrained within 0.4 m, validating the effectiveness and robustness of the proposed framework. This study is limited to defensive cyber-resilience in a closed hardware-in-the-loop simulation environment and does not address reconnaissance payloads, weaponization, target selection, or operational attack execution. Unlike methods that assume known attack schedules or treat detection, topology recovery, and control separately, this study focuses on their online coupling under unknown random packet loss; its validation is limited to the stated closed HIL impairment model. Full article

Unmanned aerial vehicles (UAVs), also referred to as unmanned aerial pesticide application systems (UAPASs), are increasingly used for crop protection applications because of their operational efficiency and precision. However, UAV spraying is typically conducted at very-low volumes (VLVs) (10–20 L ha⁻¹), resulting in highly concentrated spray solutions that may alter formulation behavior relative to conventional ground applications. In this study, a total of nineteen commercially available herbicide, insecticide, and fungicide formulations representing multiple formulation classes were evaluated under UAV-relevant (10 L ha⁻¹) and conventional ground application conditions. Tank-mix compatibility, sprayability, droplet size distribution, driftable fines, dynamic surface tension (DST), and droplet spreading were assessed. Tank-mix incompatibility was most frequently observed in mixtures containing emulsifiable concentrate (EC) formulations, with five of 11 commonly used tank mixes exhibiting severe incompatibility at UAV rates despite compatibility at conventional application volumes. Formulations containing suspended actives, including suspension emulsions (SEs), suspension concentrates (SCs), oil dispersions (ODs), and water-dispersible granules (WDGs), showed the greatest risk of filter and screen clogging, whereas EC and soluble liquid (SL) formulations exhibited acceptable sprayability. UAV-rate spray solutions generally produced comparatively finer droplet spectra than ground-rate solutions, increasing driftable fines by up to 36.6% depending on formulation type. DST decreased by 2.5–35.2% under UAV conditions, with the largest reductions observed for SC and EC formulations. Reduced DST was associated with increased droplet spreading, particularly for fungicide formulations, where droplet spreading increased up to 718.8% relative to ground-rate preparations. These results demonstrate that formulation behavior can differ substantially under VLV conditions and that formulation-specific evaluation of compatibility, sprayability, atomization characteristics, and surface-tension-dependent behavior is required when products are deployed through UAV spray systems. Full article

UAV (Unmanned Aerial Vehicle)-to-satellite self-localization is commonly treated as satellite tile retrieval. This makes large-area search tractable, but it also forces a continuous localization problem into a discrete ranking form. Once the task is defined this way, training naturally relies on hard positive–negative tile labels, and inference tends to read coordinates from the top-ranked tile center. The model, therefore, learns image identity more than geographic continuity, while the final estimate remains vulnerable to tile-center quantization and top-1 retrieval errors. We propose PFIRNet (Probability Field Inference Network), a continuous geographic posterior inference framework that reformulates retrieval outputs as evidence for coordinate estimation rather than discrete tile selection. It uses distance-aware geographic supervision to shape candidate responses according to metric proximity, lifts top-k candidates into a coordinate-space probability field, and applies risk-calibrated multi-peak verification to update the estimate only when an alternative posterior peak is sufficiently supported. On DenseUAV, PFIRNet reduces the median localization error to 9.84 m and outperforms both one-stage retrieval methods and two-stage matching baselines. It also remains more robust under sparse and non-aligned satellite galleries. Full article