Search Results (743)

Subtropical mountain economic tree plantations are constrained by pronounced spatial heterogeneity in resource availability, yet the spatial scales at which soil properties, topography, and canopy structure regulate vegetation vigor remain poorly resolved. To address this gap, a spatially consistent multi-scale dataset combining 10 m high-density soil sampling, UAV-LiDAR, and multispectral remote sensing was used to quantify the scale-dependent drivers of the Leaf Chlorophyll Index (LCI) across 3–50 m within a Chinese hickory (Carya cathayensis Sarg.) plantation. The relative contributions of canopy, soil, and topography to LCI were decomposed across scales using an interpretable machine-learning framework (XGBoost–SHAP). At fine scales (3–10 m), vegetation vigor was primarily controlled by tree-level canopy structure, particularly tree height, reflecting localized resource acquisition. At intermediate scales (10–20 m), a distinct coupling window emerged, characterized by increased interaction complexity: LCI was predominantly driven by interactions between canopy structure and soil nutrient availability, whereas single-factor effects weakened. Notably, at 20 m this interaction pattern largely weakened and reverted to single-metric dominance. At broader scales (>30 m), complex interactions re-emerged, and dominant SHAP contributions shifted from nutrients and canopy structure toward topography and soil texture. These findings reconcile strong fine-scale drivers with weaker predictability at intermediate extents and demonstrate that soil–canopy relationships reorganize across spatial scales rather than remaining static. On the basis of these findings, a scale-hierarchical framework for precision forestry is proposed that aligns management interventions with the ecological scales at which dominant correlates operate across spatial supports. Full article

Archaeological feature detection at complex, mixed-environment sites requires accurate, efficient methods for identifying subtle morphological signatures. This study presents a semi-automated remote sensing pipeline for the detection and delineation of archaeological earthworks at Cahokia Mounds (Illinois, USA), a major Mississippian urban center and UNESCO World Heritage Site. Three LiDAR datasets, two collected via UAV-mounted sensors and one from a piloted aircraft survey, were processed into Digital Terrain Models and transformed into Local Relief Models (LRM). K-means clustering was applied to segment the LRMs into feature classes, followed by contour bounding using the OpenCV library to outline mounds and borrow pits. Additionally, SAR-derived Local Incidence Angle (LIA) rasters from PALSAR-3 and Sentinel-1 were processed through angular deviation mapping to identify slope anomalies associated with archaeological features. Results across all five datasets demonstrate the complementary strengths of LiDAR and SAR: LiDAR excels at resolving elevation-defined features such as mound footprints, while LIA captures directional slope behavior that highlights mound edges, borrow pit rims, and linear features such as causeways. Comparative analysis of LiDAR acquisition frequencies reveals minimal differences in archaeological feature recovery between pulse settings, suggesting that sensor platform choice matters more than power-density tradeoffs for this application. Despite the need for human review to filter modern disturbances and natural false positives, the integrated workflow meaningfully accelerates prospection and reduces interpretive subjectivity. The methods are scalable, site-invariant, and work with open-access data, making them applicable to archaeological landscapes worldwide. Full article

The performance of haul trucks in open-pit mining is strongly affected by haul-road geometry, surface condition, rolling resistance, and operational traffic regimes. However, existing studies often consider road-surface mapping, vehicle dynamic response, and onboard telemetry as separate information streams, which limits the reproducible assessment of how road-related factors are associated with VIMS-derived fuel-use proxy burden, mechanical dynamic response, and transport-cycle instability. This study proposes a field-based, segment-level multisensor framework that integrates unmanned aerial vehicle/light detection and ranging (UAV/LiDAR) road-surface reconstruction, global positioning system/inertial measurement unit (GPS/IMU) trajectory and vibration data, and Caterpillar Vial Information Management System (VIMS) telemetry into a unified spatiotemporal analytical dataset. The methodological contribution consists in the synchronization of heterogeneous data sources at the road-segment level, the calculation of interpretable road-condition and vehicle-response indicators, and the statistical assessment of road-related effects while explicitly accounting for confounding factors such as longitudinal grade, payload state, speed regime, truck class, and operational variability. Unlike studies that use LiDAR mapping, vibration monitoring, or onboard telemetry as separate diagnostic channels, the proposed approach introduces a segment-level analytical framework in which road morphology, truck response, and operational penalties are aligned within the same spatial unit, interpreted under confounder-aware conditions, and verified through repeat-pass reproducibility and robustness checks. The framework was tested on haul roads around the Ekibastuz open-pit coal mine. The field analysis identifies road segments where degraded surface morphology, increased waviness, unfavorable longitudinal profile, and higher rolling resistance coincide with increased mechanical dynamic response, VIMS-derived fuel-use proxy burden, braking instability, and travel-time variability. The results are interpreted as controlled field-supported associations rather than as isolated causal effects. The proposed maintenance ranking should therefore be regarded as a decision-support output, while the operational effectiveness of specific repair interventions requires future before–after validation. Full article

This study conducted a six-month time-series micro-topographic analysis using high-resolution UAV LiDAR technology to precisely characterize the complex terrain changes in regressive tidal creeks within coastal wetlands. To overcome the unique challenges posed by vegetation-dense regressive tidal flats, the LiDAR Penta Return (5-pulse) mode was applied, yielding high-density point cloud data with an average of 174 pts/m². The analysis successfully reproduced the bare earth surface beneath the vegetation canopy at sub-centimeter-level precision, overcoming the limitations of conventional optical surveying, and enabled quantitative detection of micro-topographic changes of ±25 cm or greater. Time-series analysis based on the DEM of Difference (DoD) revealed spatiotemporally asymmetric erosion and deposition patterns concentrated at the lower elevation zone (0.0–2.0 m) and slope boundaries of the regressive tidal creek. However, the apparent large elevation changes in the lowest, intermittently inundated creek-bed zone (including a maximum of about 3.7 m between the summer surveys, T2–T1) were found to scale monotonically with the tide level at the time of each flight, indicating that they are governed by the tide-dependent water-surface return rather than by genuine bed erosion. After excluding this water-affected zone, the consistently sub-aerial surface showed only modest net change over the six-month period, indicating that the regressive tidal creek adjusts gradually rather than through abrupt large-magnitude erosion and deposition. This study presents the essential value of high-precision time-series monitoring for assessing the geomorphic stability of coastal wetlands in an environment where extreme weather events under climate change are increasing in frequency. Full article

Reliable and scalable landmine detection technologies are essential for humanitarian mine action (HMA), yet standardized benchmarks for Unmanned Aerial Vehicle (UAV)-based sensing in operationally relevant environments remain limited. This study presents a comprehensive evaluation of 34 multimodal datasets acquired over a standardized seeded test site for landmine and unexploded ordnance detection. Nine sensing modalities, including RGB, thermal, multispectral, hyperspectral, LiDAR, and Synthetic Aperture Radar (SAR), are evaluated using the Anomaly, Identifiable Anomaly, Unique Identifiable Anomaly (AIU) index to establish a unified framework for quantifying detection fidelity. Results indicate that RGB imagery achieves the highest surface detection rate (94.8%), with 45.4% of targets classified as uniquely identifiable, reducing false-positive risk. For sub-surface detection, handheld electromagnetic induction (EMI) and magnetometry exceed 95% detection for ferrous items but fall below 10% for plastic ordnance. Ground-penetrating radar (GPR) is the only modality capable of detecting buried plastic targets (55.6% for cart-based systems), whereas UAV-mounted GPR remains limited (18.2%) at current operational flight heights. Based on the comparative analysis, we discuss the gaps in current detection capabilities, compare false-positive rates across modalities, and perform a cost–benefit analysis fitting contamination scenarios with the most cost-effective detection method. All datasets are publicly released, along with an interactive web-map, to support reproducible benchmarking and cross-modality comparison in UAV-enabled explosive hazard detection. Full article

Accurate and transferable estimation of forest carbon is essential for operational forest management and national greenhouse gas reporting, yet it remains challenging because of variation in stand structure and site conditions. Unmanned aerial vehicle-based light detection and ranging (UAV-LiDAR) provides detailed structural information for modelling tree-level carbon, but model transferability across sites is often limited. In this study, we compared three modelling approaches—a linear mixed-effects model (LMM), a generalised additive model (GAM), and Random Forest (RF)—within a unified framework of multi-site, locally post hoc calibrated, and fully local model-fitting strategies. Using data from 20 radiata pine (Pinus radiata D. Don) plantation stands across New Zealand (35,201 trees), a leave-one-site-out (LOSO) framework was used to assess multi-site model transferability and support post hoc calibration, while local models were evaluated using repeated within-site train/test splits. We also evaluated how prediction accuracy changed with increasing local sample size and compared random tree selection with plot-based sampling. Multi-site models showed poor generalisation, with mean relative RMSE ranging from 35.9% to 56.9% and substantial site-level bias. Applying post hoc calibration to the multi-site model using a 50-tree sample reduced prediction error by 30 to 60% (mean relative RMSE 22.8–25.0%) and substantially reduced bias across sites. The fitting of fully local models with the same sample size yielded only modest further improvements (mean relative RMSE 21.9–23.1%). Gains in accuracy were minimal with increasing sample sizes above 50 trees for post hoc calibration and 175 trees for the fully local models, and differences in accuracy between sampling strategies were small. These results show that post hoc calibration of multi-site UAV-LiDAR models with a small local sample provides a practical and efficient approach for tree-level carbon estimation in plantation forests. Full article

Autonomous obstacle avoidance for UAVs in dynamic obstacle-dominated complex environments must address time-varying local collision risks from multiple directions under the constraints imposed by local sensing, environmental uncertainty, execution safety, and limited onboard computation. Planning-based methods often require frequent replanning or explicit obstacle prediction, whereas conventional reinforcement learning policies may produce myopic decisions under partial observability. To address these limitations, this study proposes a dynamic obstacle-avoidance framework that combines a temporal LiDAR representation with safety-aware action correction in recurrent reinforcement learning. Multi-layer LiDAR observations are constructed using sector-wise minimum pooling. Adjacent two-frame stacking and a CNN-LSTM architecture are then used to extract local geometric structures and short-term dynamic cues, and a velocity-control policy is optimized using Recurrent PPO. In addition, a smooth velocity-projection safety shield is introduced to modify policy outputs and reduce collision risk during both training and policy execution. Experiments conducted in mixed static–dynamic obstacle scenarios based on Gym-PyBullet-Drones show that the proposed method achieves an average success rate of 91.9% across four test configurations, with an average online computation time of 0.78 ms. Ablation studies further support the contributions of two-frame observations, LSTM-based temporal modeling, and the safety shield. Full article

Canopy base height (CBH) is a key descriptor of forest vertical structure and an essential input for fire behavior modeling and ecosystem assessments, yet it remains difficult to retrieve reliably from satellite observations. Spaceborne waveform LiDAR from the Global Ecosystem Dynamics Investigation (GEDI) mission provides detailed information on vertical vegetation structure through relative height (RH) metrics, but existing CBH studies have largely relied on empirically selected percentiles or indirect calibration approaches. Here, we present a physically informed framework for CBH estimation that interprets the full GEDI RH profile as a continuous representation of vertical energy distribution and identifies CBH as a structural transition within this profile. Three RH-based approaches—the first-derivative, clustering-threshold, and crown-length methods—were evaluated against independent UAV LiDAR observations. Among them, the clustering-threshold approach achieved the best agreement with UAV-derived CBH (R² = 0.71, RMSE = 1.27 m) and was selected for regional-scale mapping. Sparse GEDI-derived CBH samples were further integrated with Sentinel-2 optical data using a gradient boosting regression model to generate wall-to-wall CBH maps for the Jiagedaqi District, northeastern China, achieving an RMSE of 1.01 m against independent validation data. The results demonstrate that CBH can be retrieved directly from GEDI RH metrics without requiring region-specific airborne LiDAR calibration of the GEDI-based CBH retrieval itself, while UAV LiDAR is used only for independent validation. By advancing the interpretation of spaceborne waveform LiDAR for structural boundary detection, this study expands the utility of GEDI data for large-scale mapping of fire-relevant forest structural attributes. Full article

Accurate extraction of transmission towers from Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) point clouds is a prerequisite for overhead transmission line (OTL) acceptance. This task remains challenging because tower points are heavily entangled with ground, vegetation, conductors, and insulators, especially in complex terrain. To address this issue, we propose a shape prior-guided coarse-to-fine framework for tower extraction from UAV LiDAR point clouds. First, candidate tower regions are localized from the scene point cloud through preprocessing, near-ground suppression, and density-based clustering. Second, the least-disturbed central body of each candidate tower is identified in a slice-wise manner and used to estimate the tower orientation and four principal structural axes. Third, side-view and front-view structural envelopes are progressively inferred to suppress non-tower points around the tower body and tower head. Finally, a base-constrained filtering strategy is introduced to remove residual ground and low-vegetation points within the tower footprint. Experiments conducted on multiple OTL datasets acquired in different regions of China, including plains and mountainous areas, demonstrate that the proposed method achieves robust and efficient tower extraction across diverse scenarios. The results indicate that explicit structural priors offer a promising complement to feature-driven and data-intensive approaches, particularly in scenarios with limited annotated data and strict real-time requirements. The proposed method processes scene point clouds containing tens to hundreds of millions of points, with an average extraction time of approximately 100 to 300 s per tower depending on scene density. Full article

Single-photon detection is a promising approach for low–slow–small Unmanned Aerial Vehicle (UAV) detection, holding great value in urban air defense and security monitoring. In complex urban environments, intense non-uniform building clutter and multi-echo aliasing easily submerge weak target signals, severely limiting traditional single-photon systems under low signal-to-background ratios. To address this, this paper proposes an urban-oriented detection strategy based on a free-running single-photon array, and designs a dual-optimized pulse position modulation laser detection and range image enhancement algorithm. By establishing temporal correlations via pulse sequence convolution, the algorithm effectively isolates weak UAV echoes from strong background clutter to break through detection limitations. Compared with the popular Markov correction method that often suppresses overlapping weak targets under strong reflections, the proposed method significantly improves small-target feature retention, successfully balancing background elimination and detection sensitivity. Field tests and quantitative evaluations demonstrate that the system reliably eliminates building clutter and achieves stable continuous tracking of weak UAV signals within 1.5 km, providing a highly robust and effective technical solution for urban low-altitude surveillance. Full article

The digital documentation of existing buildings is particularly important when original construction drawings or reliable as-built records are unavailable. This study evaluates the feasibility and selected dimensional consistency of a locally registered Scan-to-BIM workflow integrating unmanned aerial vehicle (UAV) photogrammetry for exterior documentation and smartphone LiDAR for interior data capture. A two-storey reinforced-concrete building with unavailable original project documentation was selected as a single case study. Exterior images were acquired using a DJI Mavic 3E (DJI, Shenzhen, China), while interior spaces were scanned using an iPhone 16 Pro Max (Apple Inc., Cupertino, CA, USA) and Polycam v5.1.5 in LiDAR mode. The UAV images were processed in Agisoft Metashape Professional 2.2.0 to generate the exterior photogrammetric point cloud, and the smartphone LiDAR data were organised with this dataset in Autodesk ReCap Pro 2025. Both point clouds were then used as geometric references for creating a geometry-oriented as-is BIM model in Autodesk Revit 2025. To evaluate selected dimensional consistency, 32 independent field measurements collected using a steel tape measure and a laser distance meter were compared with corresponding BIM-derived dimensions. The dimensional comparison yielded a mean absolute error (MAE) of 29.56 mm, a root mean square error (RMSE) of 31.21 mm, a maximum absolute error (MaxAE) of 46.00 mm, and a mean signed error (MSE) of +29.56 mm. These results indicate centimetre-level dimensional consistency for the selected validation dimensions, with a small systematic positive offset in the BIM-derived dimensions. The workflow can support preliminary geometric documentation and general as-is BIM for a small existing building, but it does not demonstrate survey-grade georeferencing, full registration accuracy, modelling reproducibility, or general applicability without further testing. Full article

The humid coastal Lingnan region of South China, including the Chaoshan area of eastern Guangdong, is frequently exposed to disaster-prone weather conditions such as high humidity, typhoon-related winds, heavy rainfall, and salt-laden coastal air. These long-term environmental exposures may contribute to surface deterioration risks of architectural heritage. Located in Shantou, Yingchuan Shijia has shown five visible surface deterioration types—cracks, staining, saltpetering, plants, and spalling—under the combined influence of environmental exposure, material aging, previous disturbance, and insufficient maintenance. To address the limitations of manual inspection, this study explores a conservation-oriented intelligent workflow integrating YOLO-based detection, digital documentation, and screening-level conservation interpretation. Digital documentation used UAV imagery, mobile LiDAR scanning, measured drawings, and SketchUp-based three-dimensional modeling. The dataset was built in three stages: a 99-image preliminary dataset, where YOLOv8 showed only basic learning capability with low performance metrics, including Precision of 33.0 ± 3.0%, Recall of 28.0 ± 1.0%, mAP50 of 25.0 ± 1.0%, and mAP50-95 of 11.0 ± 1.0%; a 362-image non-augmented case-study dataset, where YOLOv8 still showed limited performance, with mAP50 of 20.0 ± 1.0% and mAP50-95 of 8.0 ± 1.0%; and a final YOLO-format case-study dataset of 2000 images after training-set-only augmentation using 11 geometric and photometric transformation methods. After augmentation, YOLOv8 mAP50 increased to 62.0 ± 2.0%. Under the same augmented-data condition, YOLOv13 showed Precision of 89.0 ± 1.0%, Recall of 77.0 ± 1.0%, mAP50 of 84.0 ± 1.0%, and mAP50-95 of 65.0 ± 1.0%, indicating relatively higher validation performance than YOLOv8. In the normalized confusion matrix, the background missed-detection values for cracks and saltpetering were 0.29 and 0.22, respectively, indicating that weak-feature and low-contrast deterioration types remained challenging. Based on YOLOv13, a mini program was developed to organize detection outputs and provide field-oriented preliminary conservation hints. Overall, this study provides a preliminary workflow linking digital collection, image-based deterioration detection, Grad-CAM visualization, and assisted field recording for the preventive conservation of Chaoshan overseas-Chinese residences in humid coastal heritage environments. Full article

Accurate and wide-area estimation of seaweed biomass is essential for evaluating blue carbon. Conventional diver surveys and two-dimensional (2D) aerial imagery analysis face challenges such as intensive labor and biomass underestimation. While Unmanned Aerial Vehicle-based Light Detection and Ranging (UAV-LiDAR) provides dense 3D spatial data, classifying point clouds in extremely shallow coastal waters with dense kelp and artificial structures remains difficult. This study establishes a high-accuracy biomass estimation method using UAV-LiDAR and PointNet. A heuristic hybrid filtering approach combining physical constraints and local statistics was developed to automatically generate high-quality reference data. The trained PointNet successfully segmented complex point clouds into four classes with an overall accuracy of 94.2%. To calculate biomass, we introduced a volume correction model based on point cloud density (coverage) to mitigate overestimation caused by internal canopy gaps. This correction yielded estimated wet weights nearly identical to the in situ measurements (an approximate 3% difference), confirming highly accurate biomass reproduction. Furthermore, while the conventional 2D maximum likelihood method underestimated total biomass, our 3D point cloud analysis successfully quantified the dense, overlapping canopy. This framework significantly improves the efficiency and accuracy of blue carbon monitoring. Full article

Preserving cultural heritage sites requires an interoperable digital framework capable of integrating heterogeneous spatial data and supporting immersive interaction for inspection and management. This study investigates the integration of multiple heritage data representations—including IFC-based Heritage Building Information Modeling (HBIM), terrestrial and UAV LiDAR point clouds, and 3D Gaussian Splatting reconstructions—into a unified digital management environment for the East Hall (Aula Timur) heritage site within the Bandung Institute of Technology (ITB) campus. A semantic–spatial interoperability workflow is proposed to harmonize BIM, point cloud, and landscape-scale data within a common georeferenced context, supported by a CityGML-based base map of the surrounding site. An immersive virtual environment was implemented using a head-mounted display to enable walkthrough-based inspection and damage annotation. All datasets were georeferenced within a unified coordinate system, allowing spatial registration between digital objects and the physical heritage site. The results demonstrate that multi-source heritage datasets can be integrated with high geometric accuracy, achieving TLS registration errors of approximately 2 mm and georeferencing residuals within 11.1 cm (horizontal) and 0.95 cm (vertical), while preserving semantic information and ensuring spatial coherence across HBIM, GIS, and immersive environments. The system is implemented in VR, with an architecture designed to support future MR-based on-site annotation and visualization. The proposed framework establishes a foundation for future heritage digital twin deployments and supports informed conservation decisions. Full article

Precision agriculture increasingly relies on machine learning applied to high-resolution data acquired by unmanned aerial vehicles (UAVs) to support crop monitoring, stress detection, and yield forecasting. This survey presents a structured review of machine learning methods for UAV-enabled precision agriculture and organizes over 100 peer-reviewed studies within a unified four-dimensional taxonomy defined by sensing modality, data type, model family, and analytical task. The taxonomy enables systematic comparison across RGB, multispectral, hyperspectral, LiDAR, and IoT data sources and across classical machine learning, deep learning, hybrid sequential models, and emerging transformer-based architectures. We analyze how modeling choices interact with data characteristics to influence robustness, cross-environment generalization, computational efficiency, and deployment feasibility on UAV and edge platforms. Recurring challenges include limited labeled data, domain shift across seasons and fields, multimodal heterogeneity, occlusion, and real-time processing constraints. We identify emerging research directions, including data-efficient learning, representation-level multimodal fusion, domain adaptation, lightweight architectures for embedded deployment, and uncertainty aware decision support. By formalizing the landscape through a unified taxonomy, this survey provides a foundation for designing scalable, robust, and deployable machine learning systems for next-generation precision agriculture. Full article