Search Results (1,232)

Large-area remote-sensing data provide essential pre-mission information for unmanned ground vehicles, but their spatial support and temporal latency may obscure local terrain changes. A remaining challenge is to translate heterogeneous regional evidence and recent local observations into a consistent, updateable, and planner-ready map. This study presents a satellite–unmanned aerial vehicle (UAV) workflow for constructing and incrementally maintaining an off-road traversability map for mission-level global planning. A common H3 index organizes satellite imagery, terrain, soil, road evidence, and local UAV semantic observations while retaining their native spatial support and provenance. The map separates environmental-prior, semantic, and traversability-cost layers to support interpretable fusion and independent updating. A confidence-hierarchical conflict resolution mechanism resolves inconsistencies in the regional prior, while an observer-agnostic interface projects UAV semantic observations onto local map cells. RGB imagery is used by the primary UAV observer, and digital surface model (DSM) is evaluated as an optional semantic-observation modality. Evaluation included a manually reviewed regional benchmark, a unified buffered spatial holdout, cell-level update assessment, and 40 fixed replanning tasks. Conflict resolution reduced high-risk omissions. RGB-only SegFormer-B2 achieved the highest semantic accuracy with moderate computational complexity. UAV override achieved a cell-level F1 score of 96.96% and limited the false-positive accumulation associated with conservative union. Replanning further revealed a trade-off between hazardous-cell avoidance and search-graph connectivity. The proposed workflow provides a maintainable interface between multi-source remote sensing and global UGV planning rather than a replacement for onboard perception, local obstacle avoidance, or vehicle control. Full article

The 3D geometric documentation of cultural heritage monuments requires spatial datasets that are accurate, complete and suitable for conservation, monitoring, visualization and heritage management. However, complex geometries, occlusions, limited accessibility, vegetation and other field-acquisition constraints often prevent a single surveying technique from providing a complete and metrically reliable representation. In this context, photogrammetry and simultaneous localization and mapping (SLAM)-based mapping provide complementary capabilities, with each method offering advantages and limitations regarding metric accuracy, spatial coverage, detail representation, acquisition flexibility and operational efficiency. This work develops, applies and evaluates a reproducible end-to-end workflow for the metric 3D documentation of complex cultural heritage monuments through multi-sensor integration. The proposed approach combines the metric robustness and visual richness of photogrammetric reconstruction with the rapid acquisition and spatial coverage enabled by SLAM-based mapping, while producing reusable datasets for conservation planning, comparative studies, education and broader heritage applications. The workflow integrates unmanned aerial vehicle (UAV) and close-range photogrammetry, SLAM-based mapping and geodetic control within a common reference system and is demonstrated through the documentation of a historic monastery. Both datasets showed centimetre-level agreement with geodetic observations, while photogrammetry yielded fuller exterior coverage and higher-quality texture, and SLAM enabled rapid interior coverage. The CH-PhotoSLAM3D dataset is released to support reproducibility and further research. Full article

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

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

Formations of fixed-wing unmanned aerial vehicles (UAVs) must maintain mission-level coordination despite actuator degradation, bias faults, model uncertainty, wind disturbances, and command limits. This study presents a mission-oriented formation-recovery framework. Acceptable relative position and velocity performance is represented by an ellipsoidal tolerance set, and resilient formation recovery time (RFRT) is the elapsed time from fault onset to the first permanent re-entry into that set. The controller generates relative demand commands that are reconstructed from the parent limited command and then clipped componentwise. A second-order edge-based extended state observer, driven by the relative limited command, estimates the physical edge fault–disturbance mismatch, while the command-saturation residual is retained explicitly in the closed-loop analysis. A distributed fault-tolerant controller combines linear recovery feedback, observer-based compensation, and a continuous robust term. The analysis uses a finite Lyapunov energy-jump inequality at step-fault instants and an upper-right Dini derivative at saturation breakpoints to establish uniform ultimate boundedness, a sufficient recovery-set inclusion condition, and an RFRT upper bound. In the one-leader–four-follower baseline simulation, permanent re-entry occurs at 22.3313 s, corresponding to an RFRT of 2.3313 s. Relative to Edge-ESO-FTC, the proposed method reduces the post-fault peak metric by 61.2%, RFRT by 25.2%, cumulative position deviation by 54.6%, and out-of-bounds duration by 67.7%. 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

Human-oriented smart cities and landscape systems increasingly depend on reusable, high-resolution geospatial observations, yet unmanned aerial vehicle (UAV) imagery is commonly acquired for a single task and retained by separate organizations. This study diagnoses the resulting reuse bottlenecks using an improved combinatorial weighting multi-criteria decision method (ICW-MCDM). Seven experts assessed four dimensions and 17 criteria. Data Rights (0.0912), Data Security (0.0823), Share Policy (0.0819), Data Description (0.0804), and Incentives (0.0706) received the highest integrated weights. A transparent raw-score comparator recovered the same five-item set, while bootstrap and leave-one-out checks supported a governance-oriented leading set with panel dependence for some criteria. After C1–C6 were excluded, Data Description, Reliable Data, Access Permissions, Service Facilities, Search and Discovery, and Data Citation and Provenance became the leading post-entry requirements. By 11 August 2026, a national directory platform had recorded 336,753 visits, fewer than ten formal applications, and two completed university research deliveries. The cases demonstrate small-scale matching, cross-institutional aggregation, and controlled delivery, but not general platform effectiveness or downstream GeoAI outcomes. The study separates governance entry from technical readiness and identifies governance and technical prerequisites for GeoAI-ready UAV data infrastructure. 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

Reservoir impoundment can reactivate pre-existing landslides and reorganize slope topography, thereby changing seepage conditions and subsequent deformation. However, crack mapping, geomorphic interpretation, and hydrodynamic diagnosis are still often treated as separate tasks. This study investigates the Hemenkou (HMK) landslide in the Wudongde Reservoir area, China, using multi-scale space–air–ground observations, including multi-temporal optical satellite images, unmanned aerial vehicle (UAV) photogrammetry, pyramid scene parsing network (PSPNet)-based crack segmentation, global navigation satellite system (GNSS) monitoring, and convergent cross mapping (CCM). The remote sensing record shows a progressive damage sequence: cracks were mainly restricted to the upper source area in 2012, crown cracking intensified and propagated downslope by December 2020, and the UAV survey of 10 June 2024 revealed a mature tension-crack network concentrated in Zone II. ResNet-50-PSPNet achieved the best crack-extraction performance among the tested models, with Precision = 0.9120, Recall = 0.9041, F1 = 0.9081, and IoU = 0.8316. The mapped cracks are dominated by short, narrow, northeast–southwest-oriented tension cracks. GNSS monitoring reveals strong spatial heterogeneity, with stepwise deformation concentrated in Zone II. CCM provides strong directional evidence for the influence of reservoir water-level fluctuation on Zone II deformation, whereas the weaker rainfall signal is consistent with a secondary reinforcing role. The apparent increase in the rainfall-related CCM signal from 2021 to 2023 is consistent with progressive crack expansion and potentially enhanced hydraulic connectivity in Zone II. Taken together, these observations support the interpretation that post-deformation topography, particularly the tension-crack network and disturbed toe, may organise preferential seepage pathways and increase the sensitivity of the landslide to reservoir drawdown. The study provides an integrated remote sensing and monitoring framework for process-based interpretation of reservoir landslides. Full article

Using a balanced panel of 30 Chinese provinces from 2018 to 2024, this study examines the association between the crop-protection unmanned aerial vehicle (UAV) service area and the agricultural insurance claims paid-to-premium income ratio. The outcome is interpreted narrowly as annual paid-claims burden relative to premium income, not as an actuarial incurred loss ratio or a measure of profitability, solvency, or sustainability. Two-way fixed-effects models with province-clustered standard errors show that an additional 100,000 hectares of UAV service area is associated with a 1.07-percentage-point lower ratio. Decomposition regressions show a negative association with log claims paid (β = −0.0153, p = 0.040) but no significant association with log premium income (β = −0.0020, p = 0.787). The association remains negative across robustness checks, with weaker evidence for the lagged specification. In 10,000 within-year province-reassignment placebo draws, the observed coefficient is extreme relative to the placebo distribution (randomization p = 0.0001). A clustered bootstrap is consistent with a broad multi-hazard pathway rather than causal mediation. Formal tests show that detectable regional differences are concentrated in comparisons involving the Western region. Given the observational design, the findings document associations rather than causal effects. Full article

Rock slope landslide potential, tunnel design, and the engineering of underground spaces critically require an analysis of rock joint orientation and spacing. Joint set measurements are typically determined by hand in the field using a compass–clinometer to measure the orientation of the geologic planes with respect to north and the dip of the plane with respect to the horizontal. Unmanned aerial vehicles (UAVs) can be utilized to capture hundreds of photos to create high-resolution 3D models of a rock outcrop, capturing visible joint set discontinuities on a faceted surface of a triangular irregular network (TIN). Computing methods of facets and facet normals from a point cloud allow for the characterization of discontinuity orientations without the need for manual measurements in the field. However, these methods used to calculate facets and facet normals result in the addition of noise in the dataset, which increases the difficulty of analysis. A two-stage filtering process employing density-based spatial clustering of applications with noise (DBSCAN), and the second derivative of the remaining clusters, removes data that are not representative of a discontinuity within the intact rock mass. Finally, an unsupervised clustering algorithm, K-Means Clustering, is applied to the dataset to extract the dip and dip direction of discontinuities. The methodology used to identify the orthogonal joint sets on a dam spillway demonstrated good performance across all identified joint sets, with the estimated variability in dip and dip direction broadly comparable to that observed in the manual field dataset. This indicates that this newly developed proof-of-concept approach can reliably capture the orientation and variability of orthogonal joint sets from large datasets. Full article

Reliable traffic-flow models are important for traffic analysis, simulation and intelligent transport management, yet fundamental traffic-flow relationships are frequently transferred between countries despite differences in road design, traffic composition and driver behavior. This study developed and validated probabilistic speed–density and flow–density models for Croatian motorways and expressways. Sixteen representative road segments were selected through traffic and spatial analysis, clustering and stratified sampling. Traffic was recorded using an unmanned aerial vehicle, producing 25,634 individual vehicle observations and 5994 valid one-minute records. Seven deterministic functions and more than 60 probability distributions were examined. A negative exponential function achieved the highest overall performance score for the deterministic component. Speed variation was represented by a Generalized Extreme Value distribution whose scale and shape parameters changed across four density groups. Probabilistic flow values were derived from generated speed values to preserve the relationship q = gV. Fivefold cross-validation produced an average MAE of 15.75 km/h, RMSE of 19.45 km/h and MAPE of 14.37%. The deterministic component also produced the lowest MAE and MAPE among 11 comparison models for both speed and flow. By providing empirically derived relationships that capture both typical traffic conditions and the observed variability in traffic flow, the proposed model can support traffic simulation, traffic-state assessment, and the development of smart mobility and traffic-management applications. The findings contribute to a more reliable representation and assessment of traffic-flow conditions on motorways and expressways, with potential applicability to comparable road environments. Further observations under high-density conditions and validation at independent locations are required before the model is applied more widely or transferred to other road environments. Full article

Accurate radar tracking of highly maneuverable unmanned aerial vehicles (UAVs) is a key enabling technology for low-altitude airspace surveillance, counter-UAS defense, and UAS traffic management (UTM). Once a non-cooperative UAV has been detected, estimating its motion state must cope with nonlinear polar-coordinate observations, unknown maneuver-mode switching, and multi-scale state variations driven by agile drone flight, making it difficult for classical IMM/UKF filters and deep sequence models to preserve local maneuver response and long-term temporal consistency. We propose MSGate, a multi-scale gated temporal network organized along an “observation-representation-fusion-constraint” pipeline. A non-learnable physical front end maps polar measurements into a Cartesian observation trajectory of the same dimension as the UAV state. Multi-scale gated convolution and RoPE-Transformer encoding extract local maneuver responses and long-range dependencies. A shared gated dual-path decoder fuses the two paths adaptively at each time step and channel, and velocity-smoothness and position-velocity kinematic consistency terms regularize the predicted trajectory. On the real-UAV datasets UZH-FPV, EuRoC MAV, and NeuroBEM, under a unified range-azimuth observation protocol, MSGate attains the lowest average position and velocity errors (Pos-RMSE $0.0486\mathrm{m}$; Vel-RMSE $0.1767\mathrm{m}/\mathrm{s}$), outperforming the strongest time-series baseline TimeMixer, and generalizes to a separate nano-quadrotor dataset (NanoBench). MSGate provides an accurate, maneuver-robust solution for radar state estimation of highly maneuverable UAVs. Full article

Unmanned aerial vehicles (UAVs) are increasingly considered as enabling technologies for last-mile delivery, emergency medical response, and smart-city applications. However, drone-based logistics, emergency communication, sensing activities, and future aerial mobility are often addressed as separate research domains. This article proposes a four-dimensional planning framework for drone-enabled mobility, integrating goods, information, sensing, and human mobility within a unified conceptual structure. The framework is developed through a literature-informed conceptual analysis and previous applied research experiences related to drone-assisted logistics and emergency communication. Goods mobility includes parcel delivery, medical logistics, emergency supply transport, and hybrid operational models involving trucks, public transport, depots, and micro-hubs. Information mobility refers to the use of drones as mobile communication tools for emergency warnings, citizen interaction, drone-to-infrastructure communication, and infomobility services. Sensing mobility concerns traffic monitoring, environmental observation, disaster mapping, crowd monitoring, and infrastructure inspection. Human mobility is considered as an emerging extension related to urban air mobility (UAM), electric vertical take-off and landing (eVTOL) systems, and low-altitude aerial corridors. Cross-cutting issues such as energy autonomy, solar-assisted drones, multimodal integration, safety, communication, regulation, sustainability, and public acceptance are discussed. The proposed framework provides a structured basis for assessing drones as components of sustainable, resilient, and multimodal mobility systems. Full article

The rapid acquisition of spatial information over affected areas is critical for emergency decision-making and disaster assessment in complex environments. Unmanned aerial vehicles (UAVs) have become an important tool for disaster information acquisition due to their rapid deployment and flexible observation capabilities. However, multi-UAV remote sensing information acquisition in complex obstacle-laden environments still faces challenges such as insufficient coverage efficiency, limited obstacle avoidance capability, and weak cooperation. This paper proposes a multi-UAV cooperative remote sensing coverage method based on an improved rotational potential field (IRPF). A disaster area model is first constructed. The artificial potential field is then enhanced by integrating separation forces and rotational guidance mechanisms to improve obstacle avoidance and information acquisition capability in complex environments, while coverage feedback is incorporated to enable dynamic observation region allocation. The experimental results show that the proposed method achieves a multi-run success rate of 100.0% over 20 independent experiments, with a final coverage rate of 93.3% in the representative scenario. The UAV system reaches the predefined 85% coverage threshold at step 106 and maintains zero collisions throughout the entire process. This method provides an effective approach for multi-UAV cooperative coverage planning in simulated complex environments, providing a potential approach for cooperative coverage planning in simulated disaster environments. Full article