Search Results (737)

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

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

The planktonic dispersal of Limnoperna fortunei larvae and the byssal attachment of juveniles and adults make this species a major invasive biofouling species in water-conveyance infrastructure, while artificial hydraulic connectivity further facilitates its spread. Dense colonization can reduce conveyance capacity, increase energy consumption, accelerate structural deterioration, and impair water quality, thereby posing multiple risks to infrastructure operation. This narrative and critical review synthesizes evidence from 110 publications retained after screening 537 records retrieved from the Web of Science Core Collection up to 30 June 2026. The review characterizes the stage-specific progression of L. fortunei biofouling from propagule input and early settlement to mature fouling and post-treatment residual risks. It compares the applicability of eDNA/qPCR assays, conventional field surveys, and remotely operated vehicle (ROV)-based image inspection, and evaluates the effectiveness and operational limitations of physical, chemical, coating-based, and biological control measures across different risk stages. Current management often targets individual stages, with limited linkage between monitoring results and subsequent intervention. Accordingly, we propose a risk-oriented decision pathway that integrates early warning, settlement confirmation, fouling-load assessment, targeted removal, and post-treatment verification while accounting for hydraulic safety, water-quality constraints, and asset accessibility. By aligning management actions with biofouling stage and asset condition, this framework provides a basis for more sustainable operation and maintenance of water-conveyance systems. Full article

This study presents a high-resolution geomorphological and habitat map of the Sinuessa coastal sector (Tyrrhenian Sea), revealing the presence of an extensive and exceptionally shallow coralligenous bank developed between 5 and 16 m water depth. Multibeam bathymetry, side-scan sonar backscatter, sediment analyses, and Remotely Operated Vehicle (ROV) observations were integrated within a Geographic Information System (GIS) framework to characterize seabed morphology, acoustic facies, and associated benthic habitats. ROV surveys document a diverse macro- and epimegabenthic community, including both sciaphilous and photophilous taxa, as well as several protected and structuring species. Water depth alone does not discriminate among the mapped substrate classes (Kruskal–Wallis, p = 0.578), whereas acoustic backscatter, slope, and terrain ruggedness all do (p < 0.01), indicating that depth-independent controls govern the distribution of the bioconstruction. We hypothesize that persistently elevated turbidity and terrigenous input from the Volturno and Garigliano river systems reduce light penetration and generate, at 5–16 m, optical conditions comparable to those normally found at greater depths. This hypothesis is consistent with the geomorphological, sedimentological, and biological evidence presented here and with published oceanographic observations in the Gulf of Gaeta, but it has not been verified by in situ optical measurement, which we identify as the priority for future work. Relative backscatter intensity correlates significantly with mean grain size (Spearman ρ = −0.710, p < 0.001) and with gravel and mud content, and the four mapped classes differ significantly in backscatter, slope, and terrain ruggedness. These findings provide new insights into the environmental controls on coralligenous development and highlight the ecological relevance of shallow, turbidity-driven coralligenous systems within highly impacted Mediterranean coastal areas, with direct implications for habitat conservation and spatial management. Full article

Conventional image acquisition requires an electrically powered image sensor to be placed directly behind the camera lens, constraining camera placement. To overcome this issue, we introduce M3-RGB as an incoherent-light fiber imaging system in which a multicore, multimode optical fiber passively relays lens images to a remotely located image sensor. Unlike conventional approaches, M3-RGB is designed to operate directly on incoherent light and requires no electrical power or active components at the sensing interface. Because propagation through the fiber yields spatially scrambled patterns, a neural network is used to reconstruct the original scene by exploiting the spatial locality preserved by the multicore structure. In a controlled optical bench setup, where a liquid crystal display monitor displays road-scene images, we construct a paired dataset of scrambled and ground-truth images and quantitatively evaluate reconstruction performance across different fiber core counts, fiber lengths, and calibration settings, utilizing the peak signal-to-noise ratio and structural similarity index measure as performance metrics. By decoupling imaging electronics from the sensing point, this passive remote image relay approach may expand sensor placement options for potential applications such as all-around perception for mobile robots and autonomous vehicles, surveillance, and inspection in confined spaces. Evaluations in real outdoor environments constitute future work. Full article

Marine debris threatens aquatic habitats and complicates inspections in ports, seabed environments, and offshore infrastructure. Previous lightweight detectors remain vulnerable to weak texture, blurred boundaries, and cluttered multi-scale features, while direct network expansion conflicts with restricted onboard resources. This study adapts YOLO11n by integrating Receptive-Field Aggregation (RFA) with a Residual Channel-Spatial Recalibration (RCSA) implementation based on dynamic residual groups. Experiments used a public 15-class dataset with 10,884 training images and 1001 model-selection validation images containing 1892 annotated objects. All principal checkpoints were trained for 100 epochs. Across seeds 42, 2026, and 3407, RFA + RCSA achieved validation precision 0.861 ± 0.016, recall 0.801 ± 0.012, mean average precision at IoU 0.5 (mAP@0.5) 0.848 ± 0.001, and mAP@0.5:0.95 0.511 ± 0.002. On an audited group-disjoint holdout (498 images; 963 instances), the corresponding means were 0.820 ± 0.033, 0.748 ± 0.014, 0.779 ± 0.018, and 0.467 ± 0.007. The detector contains 4.19 M parameters and requires 8.91 giga floating-point operations (GFLOPs). These results position it as a lightweight candidate for resource-constrained remotely operated vehicle (ROV) perception; they do not establish real-time embedded deployment. Full article

Multi-angle measurements provide essential data on the anisotropic reflectance properties of vegetation, enabling more robust retrievals of leaf area index (LAI), clumping index (CI), and canopy gap fraction compared to conventional single-view remote sensing. While Unmanned Aerial Vehicles (UAVs) offer unprecedented centimeter-level spatial resolution and flexible deployment, their application exposes a fundamental scale mismatch between ultra-high-resolution imagery and traditional bidirectional reflectance distribution function (BRDF) models. This review explicitly identifies that conventional 1D radiative transfer models (RTMs), which rely on the assumption of a statistically homogeneous canopy, suffer from severe scale-dependent biases, such as systematically underestimating hotspot reflectance (e.g., observed biases of 25% to 40% in 5-cm resolution UAV studies over specific vegetation canopies), and structural-optical confounding when directly applied to UAV data. At centimeter scales, macroscopic structural heterogeneity disrupts this homogeneity, necessitating the use of 3D RTMs that can explicitly simulate geometric occlusion and complex multiple scattering processes in highly heterogeneous environments. To bridge these theoretical and operational gaps, this review uniquely synthesizes UAV-specific multi-angle methodologies, systematically correlating canopy architectural types with optimal sensor configurations, flight strategies, and BRDF modeling frameworks. By evaluating recent advancements in multimodal data fusion, physics-informed machine learning, and physiological parameter retrieval, this review provides a comprehensive roadmap for decoupling structural and biochemical traits, highlighting how multi-angle directional signatures can substantially elevate classification accuracy, with specific experiments on spectrally similar crops and mixed tree species demonstrating improvements from roughly 40% to over 89%. Ultimately, it establishes practical, decision-oriented guidelines for overcoming transient illumination and co-registration errors, advancing high-fidelity quantitative monitoring and stress detection in complex forest ecosystems. Full article

Unmanned aerial vehicle (UAV) remote sensing can reveal spatial variability in maize, but its value depends on whether observations support timely and reliable management. This structured critical review synthesizes UAV applications from stand establishment and canopy development to water and nutrient assessment, stress monitoring, yield prediction, and decision support. The evidence corpus comprised 82 sources, including 51 core maize–UAV studies, evaluated by growth stage, validation strength, and operational endpoint. RGB, multispectral, hyperspectral, thermal, and three-dimensional methods provide complementary information for plant counting, canopy traits, treatment-related water and nitrogen responses, visible stress mapping, and within-experiment yield variation. Most evidence, however, comes from experimental or site-specific settings. Independent testing across sites, years, cultivars, and production environments remains uncommon, as do physiological confirmation of interacting stresses and translation of diagnostic maps into machinery-ready operations. UAV sensing should therefore be viewed as complementary to satellite observations and field scouting, with its advantage determined by target, scale, timing, and decision requirements. Future research should prioritize phenology-aware acquisition, independent field validation, uncertainty relative to management thresholds, interoperable prescription and machinery data, and closed-loop evaluation of input use, crop response, yield, and economic return. These steps are needed to move from high-resolution mapping toward reproducible precision management in maize. Full article

Reliable inspection of insulator defects from unmanned aerial vehicle (UAV) remote sensing imagery is essential for the safe operation of power transmission systems. However, the task remains challenging due to fine-grained defect patterns, thin structures, large-scale variations, and complex backgrounds in aerial scenes. To address these issues, we propose Insulator-DETR, an end-to-end detection transformer specifically designed for UAV-based insulator defect inspection. The proposed framework preserves the set-prediction paradigm of DETR while introducing task-oriented modifications. In the encoder, a Parallel Attention MLP integrates multi-scale spatial perception and phase-aware token interaction to enhance the representation of fine textures and structural details. In the decoder, a Masked Linear Attention mechanism combines efficient global modeling with local contextual aggregation, enabling accurate localization of irregular defects. Furthermore, a convolutional feed-forward design is adopted to strengthen spatial interactions. Extensive experiments on two newly annotated UAV insulator-defect datasets and a public benchmarks demonstrate that Insulator-DETR consistently outperforms state-of-the-art detectors in both detection accuracy and recall. Full article

Hyperspectral anomaly detection (HAD) identifies targets by spectral differences. However, large deep learning models overfit under the small-sample conditions typical of remote sensing, where anomalies are sparse and annotations costly. We propose a lightweight network on the GT-HAD transformer backbone for hyperspectral imagery. The design includes: (1) capacity reduction via multi-layer perceptron (MLP) ratio and embedding dimension optimization; (2) a Decoupled Projection Gating Fusion (DPGF) module enforcing spatial–spectral decoupling via dual-path projection and complementary regularization; (3) analysis of capacity–generalization tradeoffs across six airborne hyperspectral datasets. Experiments reveal an inverted-U relationship between capacity and generalization. The Mini configuration (121 K parameters) achieves a 53% parameter reduction. It yields AUC improvements on three datasets, maintains negligible performance loss (<0.1%) on two datasets, and exhibits a measurable decline on only one dataset, compared with the 255 K baseline. Notably, the baseline attains higher accuracy with only 25% training data on Pavia (+3.69% area under the receiver operating characteristic curve, AUC). This dataset-specific observation provides empirical evidence that capacity–data mismatch can induce severe overfitting in deep HAD models. Under a 25% training ratio, DPGF shows observable performance trends on spectrally homogeneous scenes, and zero initialization ensures identical performance to that of the baseline at the initial training stage, eliminating insertion risk during module deployment. However, the limited diversity of sensors and scene types constrains the generalizability of the observed trends. These results demonstrate that spatial–spectral decoupling with lightweight design suppresses overfitting in few-shot HAD, guiding compact models for onboard satellite and unmanned aerial vehicle (UAV) applications. Full article

This study addresses target-to-ROV relative localization in an unmanned surface platform-assisted UAV–ROV search-and-recovery system. Because the submerged ROV is not assumed to be visible from the air, the UAV observes the floating target and a GNSS-equipped ROV-associated surface buoy in the same image. The buoy position and target-to-buoy image displacement are combined to construct a world-frame target-position measurement, whose covariance accounts for buoy GNSS uncertainty and correlated image-projection errors. An upward-looking ROV imaging sonar provides range–bearing measurements. A delay-aware extended Kalman filter fuses the asynchronous observations using sea-state- and confidence-dependent covariance adaptation and normalized-innovation gating. ROV acoustic/inertial navigation uncertainty is propagated into the sonar measurement covariance and the reported relative-state covariance, avoiding duplication of the same navigation error in the aerial channel. The method is evaluated using a JONSWAP-based temporal disturbance model, Monte Carlo simulations, and single-factor and joint sea-state–occlusion–delay sensitivity tests. Under the nominal sea-state-5 condition, the proposed method achieves a mean ROV-frame relative RMSE of 0.992 m, compared with 1.083 m for ROV-only localization and 1.054 m for fixed-covariance fusion, with no run exceeding the 5 m divergence threshold. The results demonstrate improved relative-localization robustness within the simulated environment. Full article

Abandoned, lost, or otherwise discarded fishing gear (ALDFG) is a persistent form of marine pollution requiring survey approaches capable of resolving individual items across large spatial extents. While uncrewed aerial vehicles (UAVs) can capture imagery at resolutions sufficient to resolve individual debris items, their use remains largely constrained to visual line-of-sight (VLOS) operations, limiting large-scale coastal monitoring. This case study develops and field-tests an operational framework for low-altitude beyond visual line-of-sight (BVLOS) UAV surveys, in which DEM-based communication viewshed modelling incorporating first Fresnel zone clearance is used to plan BVLOS missions. A lightweight fixed-wing UAV flown at 60 m AGL completed 20 missions across 210 km of remote northern Australian coastline. Communication viewshed modelling reliably guided mission planning with 90.5% of waypoints placed within predicted high-clearance zones maintaining moderate-to-strong command-and-control (C2) link quality in flight. Manual screening confirmed that the resulting imagery was of sufficient quality, with 291 derelict fishing nets detected. In a simulated VLOS operational scenario, 76.3% of these detections fell beyond VLOS range, and equivalent coverage would require an estimated 8.8-fold increase in mission count. These findings demonstrate that fixed-wing UAVs operating under low-altitude BVLOS conditions can support large-scale image acquisition in remote coastal areas, particularly when enabled by communication-aware mission planning. Full article

Remote sensing is widely recognized as a key technology across a wide range of technical and scientific domains, especially in agriculture. Although satellite data have long supported crop monitoring, their limitations in spatial resolution, revisit frequency and cloud coverage have often constrained their applications. High-resolution satellites, available from the beginning of the 2000s, have improved performance, particularly in the field of precision agriculture, but they remain expensive and inflexible. Unmanned Aerial Vehicles perform better in precision agriculture, offering flexibility and high levels of detail; however, their limited operational areas and short endurance flight times constrain their effectiveness. In this evolving landscape, High Altitude Pseudo Satellites (HAPSs), particularly high-altitude balloons, are emerging as a promising new technology that could fill the gaps between satellite and drone remote sensing. These platforms provide large area coverage with high-resolution imagery and long endurance flights at low operational expenses and ease of deployment. This study investigates the operational characteristics, strengths, and geometric limitations of data acquired by the CubeHAPS^® platform, a high-altitude pseudo-satellite system, as a prerequisite for its application in precision agriculture. Focusing on experimental campaigns conducted in northern Italy in summer 2024 and 2025, the research characterizes platform stability, image block consistency, and photogrammetric quality through internal metrics. The results demonstrate measurable improvements between the two campaigns, attributed to the introduction of a stabilization system in 2025 and establishing the conditions under which the platform can support reliable photogrammetric reconstruction. Full article

ROVs are essential for marine exploration and underwater operations, yet conventional teleoperation relies heavily on skilled human operators, and many autonomous methods stop at high-level planning rather than low-level actuation, limiting robustness in disturbed and cluttered environments. This paper proposes ODARRL, an obstacle- and disturbance-aware sensor-to-thruster (ST) end-to-end residual reinforcement learning framework for safe trajectory execution of underwater robots. Using a three-stage curriculum, ODARRL first acquires a basic policy from MPC demonstrations in a static obstacle-free environment, then improves disturbance-robust tracking under random currents, and finally extends to scenarios involving both currents and obstacles. A Dual-Horizon Attention Disturbance Encoder is further designed to capture current-related features from long- and short-term histories, which are fused with robot states and reference information as the input to the ST end-to-end policy. Experiments in Marine Gym with BlueROV2 Heavy demonstrate that ODARRL achieves more stable and robust trajectory tracking under random currents, reducing the mean total tracking error by 69.3%, 31.9%, 45.8%, 73.0% and 25.8% relative to the MPC-imitation policy, PPO, SAC, A2C and VNRS-SAC, respectively. With obstacles introduced, curriculum-initialized policies also exhibit higher path progress and more stable task completion during obstacle-avoidance training. Full article

To address the motion control challenges of remotely operated vehicles (ROVs) under model uncertainties, external disturbances, and uncertain hydrodynamic parameters, this study proposes a fractional-order super-twisting sliding mode control (FOST-SMC) strategy based on improved active disturbance rejection control (IADRC). The proposed method reduces dependence on accurate dynamic models and enhances disturbance rejection capability by integrating IADRC with FOST-SMC. A sine-function-based nonlinear extended state observer (ESO) was developed to improve lumped disturbance estimation and noise robustness. The proposed ESO reduces the root mean square (RMS) estimation error from 2.226 × 10⁻⁵ to 5.224 × 10⁻⁶, corresponding to a 76.5% reduction compared with the conventional ESO. Lyapunov analysis verified the stability of the closed-loop system. MATLAB/Simulink version R2024a (MathWorks, Natick, MA, USA) simulations based on the Falcon ROV model demonstrated improved tracking performance under step response, sinusoidal tracking, and three-dimensional trajectory tracking with time-varying disturbances and Gaussian white noise. Compared with conventional active disturbance rejection control (ADRC), the proposed controller achieved average RMSE reductions of 87.0%, 57.2%, and 49.4 to 65.4% in different tracking scenarios, respectively. The proposed strategy provides an effective approach for robust ROV motion control in uncertain underwater environments. Full article