Search Results (865)

This paper presents an onboard, markerless perching system for a quadrotor UAV, validated in outdoor flight experiments, to reduce hovering energy during long-endurance unmanned missions. Existing autonomous landing research predominantly focuses on planar surfaces, cooperative environments with visual markers, or specialized hardware, limiting scalability to scenarios requiring detection and perching on thin rod-like targets in uncooperative outdoor settings. This study proposes a markerless perching system for autonomously perching a drone on a hurdle’s horizontal bar. The system employs a single-axis gimbal camera, altitude LiDAR, and ToF sensor, integrating perception, post-processing, and control. On the perception side, we augment a YOLOv12n-based segmentation model with a high-resolution P2 pathway for small-object detection and apply module compression for real-time inference on edge devices. Robustness is improved by jointly utilizing the full hurdle and horizontal bar while constructing negative samples to suppress false positives. On the control side, a state machine controller leverages centroid coordinates, orientation, and distance measurements to achieve a stable long-range approach and precise close-range alignment. Experiments on a Jetson Orin NX-based system demonstrate successful perching in all six outdoor flight tests. Ablation studies quantitatively analyze each component’s contribution to perching success rate and completion time. This research validates perching technology’s practical applicability through outdoor markerless perching on thin 3D structures. Full article

Effective port and coastal surveillance require persistent monitoring, flexible deployment, and reliable target detection in dynamic maritime environments. This paper presents a system- and deployment-oriented autonomous tethered drone architecture, integrated with AI-based perception, for persistent maritime surveillance in ports and coastal areas. Mounted on a moving maritime platform and powered through a tether, the drone provides a persistent elevated viewpoint without the endurance limitations of conventional battery-powered Unmanned Aerial Vehicles (UAVs). The system combines maritime platform integration, tethered flight operation, fail-safe and safety mechanisms, and a distributed Artificial Intelligence (AI) pipeline for real-time object detection and tracking. The perception module is based on YOLOv8m for vessel detection and BoT-SORT for multi-object tracking, enabling continuous monitoring of maritime targets in realistic operational scenarios. Field trials conducted from moving vessels in maritime environments demonstrate autonomous take-off and landing, stable surveillance operation under realistic wind and wave conditions, and effective vessel detection and tracking on real image sequences. The results show the potential of AI-enabled tethered drone surveillance as a persistent and operationally relevant tool for maritime monitoring and security. Full article

For Unmanned Aerial Vehicles (UAVs), optical sensing for reliable landing and the detection of the landing area is a crucial element. In low-light conditions, at night, and in foggy weather, where optical sensing is not feasible, thermal imaging can be utilised. Although this situation has been widely researched, most UAV landing approaches rely on GNSS assistance or single-mode detection, which limits their robustness and scalability in real-world operations. This study proposes an actively heated thermal helicopter landing pad designed using electrically powered resistive heating elements and a high-emissivity surface coating. Furthermore, optical and thermal images collected during actual UAV flight experiments under daytime and night-time conditions were processed using image fusion techniques with AVGF, DWTF, GPF, LPF, MPF, and HWTF fusions, and their performance in deep learning models was compared. The obtained optical, thermal, and fused datasets are used to train and evaluate deep learning-based helicopter landing pad detection models based on the YOLOv8 architecture. Experimental results show that models trained with single-mode data exhibit limited cross-domain generalisation, while fusion-based learning significantly improves detection robustness in optical and thermal domains. Among the evaluated methods, LPF, MPF and HWTF provide the most consistent performance improvements. The findings indicate that electrically heated thermal helicopter landing pads, when combined with image fusion and deep learning-based detection, can increase the landing detectability of UAVs at night and in low-visibility conditions. This detection-focused approach contributes to UAV flight safety by enhancing the visibility of the landing area without relying on active infrared markers or additional navigation infrastructure. Full article

Outdoor ecological practice is essential for cultivating ecological literacy; however, there is currently a relative lack of comprehensive outdoor practical teaching case designs for class-based teaching. This study describes the design of an experimental teaching case for ecological education involving UAV-based field data collection. For the scheme, we selected the Xinhui Tangerine Peel Germplasm Resources Conservation Center in Jiangmen City, Guangdong Province as the study area, utilizing the DJI Phantom 4 RTK drone, which serves as the equipment for experimental teaching. The experiment is structured into three phases: indoor preparation, field execution, and data processing. Students from four groups collaboratively conducted aerial surveys across 24 partitioned plots, with flight altitudes stratified between groups to ensure safety and data integrity. (1) In the indoor preparation phase, appropriate single-flight operational units were defined. QGIS software (version 3.26.2) was employed for zonal mission planning, and suitable flight altitudes were estimated using contour data. (2) Field experiment phase. This involved conducting a comprehensive survey of the on-site environment, selecting suitable takeoff and landing points, dividing students into teams to carry out UAV-image-acquisition tasks, and assigning different altitudes for flight routes among the teams. (3) After the fieldwork, students processed imagery using Agisoft Metashape (version 2.0.1) to generate orthomosaics and digital surface models, and engaged in ecological interpretation of the results. The experimental design ensured orderly execution, complete data coverage, and active student participation. The results indicate the approach effectively enhanced students’ UAV operational skills, outdoor problem-solving abilities, and teamwork capabilities, while deepening their ecological understanding through real-world inquiry. This case provides a replicable model for integrating UAV technology into ecological education, contributing to the transformation of ecological awareness into actionable practice. Full article

In response to the significantly reduced efficiency of magnetic coupling wireless charging for unmanned aerial vehicles (UAVs) caused by their high sensitivity to transmitter and receiver coil alignment, as well as landing point errors, a position identification method based on the detection coil-induced voltage fingerprint and embedded neural network regression is proposed. This enables position alignment through a 2D mechanical structure. Firstly, by means of an S–S compensation topology with a bipolar (BP) symmetrical four-detection-coil array deployed at the transmitter, the system effectively suppresses primary direct coupling, ensuring that the position of the receiver coil predominantly determines the detection signals. Secondly, by establishing a voltage fingerprint database during the offline stage and utilizing a multi-layer perceptron–radial basis function (MLP-RBF) regression model, the system achieves high-precision end-to-end positioning and alignment control during the online stage through induced voltage acquisition and data processing. Finally, experiments demonstrate that the proposed method achieves centimeter-level positioning accuracy, with an average error of approximately 1.2 cm and a maximum error of less than 1.8 cm, presenting excellent deployability and engineering applicability. Full article

Dual-propulsion UAVs employ separate rotors for rotary-wing and fixed-wing modes to achieve VTOL (vertical take-off and landing) and high-speed cruise. This paper analyzes steady climb in high-speed flight by utilizing the redundant rotary-wing rotors. We develop the models of aerodynamic forces and thrust forces of a dual-propulsion UAV to obtain its longitudinal dynamic model. The maneuverability of the UAV is analyzed based on the dynamic model to reveal whether a steady climb at a given climb angle is possible within allowable thrust forces. The analytical results show that the climb flight performance of the UAV can be enhanced by utilizing the redundant control inputs during high-speed flights. Flight experiments not only demonstrate that several climb flight states predicted by the analysis are successfully realized, but also that steady climb at a higher climb angle, unattainable in conventional fixed-wing mode, is made possible by simultaneously using the rotors for rotary-wing mode. The enhanced flight performance would increase the number of missions that the UAV can accomplish. Full article

Monarch butterflies have declined in both eastern and western populations. Conservation initiatives that support this imperiled species are being implemented in lands managed by the energy and transportation sectors. Vegetation management strategies that encourage the presence of milkweed (Asclepias spp.), the larval host of monarch butterflies (Danaus plexippus), or floral resources to support pollinators are being practiced across North America; however, survey methods to evaluate the success of these strategies vary in accuracy and scalability. In this study, we compared five methods to quantify milkweed stem density and land cover estimates: (1) Site al, (2) Transect plot, (3) Square plot, (4) Large transect (informed by the Monarch CCAA methodology), and (5) Machine learning of images collected by UAVs. These methods encompass full coverage ground counts, partial ground counts, and aerial imagery using object-based image analysis. Sites included distribution, transmission, and gas line ROWs, solar arrays, and transportation easements. We found that Site al and Machine learning most consistently quantified milkweed stem density across sites. Partial ground count methods were likely to over or underestimate milkweed populations. Habitat characteristics (woody, broadleaf, grass, and bare ground) estimates were inconsistent across method and site. The intent of this study was to provide land managers with insight as to the most accurate, efficient, and economical approach to quantify milkweed populations and habitat characteristics. Full article

Landslide hazards occur frequently in the Ya’an region; therefore, accurately identifying and delineating potential landslide areas is crucial for disaster prevention and mitigation. Although deep learning-based detection methods using optical remote sensing imagery are widely adopted, the complex terrain and diverse land cover in this area often result in blurred boundaries and weakened textural features, making it difficult to precisely define spatial extents. To overcome these challenges, this study proposes an improved YOLOv11 model for landslide detection. Building on the YOLOv11 baseline, we designed a novel Multi-Scale Detail Enhancement module and integrated it into the neck network to effectively aggregate shallow-level details with deep-level semantic information, thereby enhancing the model’s ability to represent ambiguous boundaries. Additionally, we incorporated the lightweight SimAM attention mechanism into the backbone network. This mechanism dynamically suppresses background noise based on an energy minimization principle, improving feature discriminability within landslide regions and enabling precise boundary boxes. We conducted validation experiments in the Ya’an region using a custom dataset constructed from high-resolution UAV orthoimagery, comparing our method against mainstream models such as YOLOv8 and YOLOv10. The results show that the proposed improved YOLOv11 model achieves a precision of 90.2%, a recall of 84.8%, and an mAP of 92.7%. This enhanced performance demonstrates the model’s effectiveness in detecting landslides under complex terrain conditions, providing a practical technical reference for efficient hazard screening and dynamic monitoring. Full article

Soil salinization severely threatens agricultural sustainability in saline–alkali regions, and high-throughput, efficient screening of salt-tolerant rice varieties is critical to mitigating this threat. Traditional evaluation methods are constrained by low throughput, limited spatiotemporal resolution, and the lack of standardized indicators. To address these gaps, this study established a multi-scale spectral phenotyping framework integrating ground-based hyperspectral, UAV-borne multispectral, and Sentinel-2 satellite remote sensing data for high-throughput screening of salt-tolerant rice. Field experiments were conducted with 12 rice lines at five key growth stages in Ningxia, China, with synchronous ground spectral measurements and UAV image acquisition on the same day for each stage. Five feature selection methods were employed to screen salt stress-sensitive hyperspectral bands, with classification accuracy validated via a Support Vector Machine (SVM) model. The results showed that: (1) rice spectral characteristics varied dynamically across growth stages, and first-order differential transformation effectively amplified subtle spectral variations in stress-sensitive regions; (2) the Minimum Redundancy–Maximum Relevance (mRMR) method outperformed other methods, achieving 100% classification accuracy at key growth stages, with sensitive bands dominated by red edge bands (58.33%); (3) the constructed Salt Stress Index (SIR) showed strong correlations with classical vegetation indices and rice yield, and could clearly distinguish salt-tolerant and salt-sensitive rice varieties, with stable performance against field environmental noise; and (4) band matching between UAV and Sentinel-2 data enabled multi-scale data fusion and regional-scale salt stress monitoring. This framework realizes the transformation from qualitative spectral description to quantitative salt tolerance evaluation, providing standardized technical support for salt-tolerant rice breeding and precision management of saline–alkali lands. Full article

This paper presents a methodological framework for integrating UAV-based photogrammetry and GIS technologies to generate a high-accuracy digital elevation model (DEM) for urban land-use planning. The study was conducted in an urbanized area characterized by heterogeneous topography, mixed vegetation cover, and fragmented land use, which complicate high-resolution terrain modeling. UAV surveys were performed using multiple photogrammetric blocks with centimeter-level ground sample distance and a dense ground control network supported by geoid-based height corrections. The resulting DEM was independently validated using control points derived from large-scale topographic data. The achieved vertical accuracy (RMSE ≈ 0.25 m) confirms the applicability of UAV-derived DEMs for large-scale mapping (1:1000–1:2000) and urban spatial analysis. Unlike studies focused on runoff simulation, this work emphasizes the accuracy-controlled generation and validation of DEMs as a primary spatial dataset for urban planning applications. The results demonstrate that DEM accuracy depends strongly on flight planning, ground control distribution, and hybrid automatic–manual point cloud refinement. Full article

Maintaining high power delivery under uncertain landing positions is a key challenge for wireless charging of unmanned aerial vehicles (UAVs). This paper presents a data-driven power optimization method based on the Soft Actor-Critic algorithm for multi-transmitter single-receiver wireless power transfer (MTSR-WPT) systems. To support effective learning without explicit online parameter identification, a physics-informed dual-current state representation is constructed from measurable current responses, combining a zero-phase current with the current response under the applied phase command. The agent is trained using a reward defined directly from normalized load power, and the transmitter voltage phases serve as the control actions. In simulations of a five-transmitter system, the learned policy achieves about 97% of the theoretical maximum power in the training region and about 96% in the expanded evaluation region. Additional robustness studies show strong performance under moderate measurement noise and substantial recovery under model mismatch after short fine-tuning. Experimental validation on a physical prototype confirms the effectiveness of the method, yielding an average power improvement of 188% from a zero-phase baseline and reaching 87% of the maximum power measured on the hardware platform. These results support the proposed method as a practical data-driven alternative to model-dependent MTSR-WPT power optimization for UAV wireless charging. Full article

Monitoring atmospheric turbulence data of the near-surface sublayer presents a difficult challenge on complex and heterogeneous terrain such as mixed land areas where weather facilities are not always available. This study uses tilt data derived from the flight logs of two hovering unmanned aerial vehicles (UAVS) as portable probes (DJI Mavic 2 and DJI Mavic 3 Classic) to compare the turbulence spectral characteristics of two adjacent contrasting surfaces; a built open courtyard and an open grass field. Turbulence spectra were divided into three ranges: $E_1$ (0.05–0.2 Hz), $E_2$ (0.2–1 Hz), and $E_3$ (1–5 Hz). A 30 s moving mean and welch methods were used to filter out noise to ensure that the resulting spectra only showed the small tilts that were derived to show atmospheric turbulence. Normalization was applied to compare spectral shapes. Comparisons were made within platform (M2 vs. M2, M3 vs. M3). Observations show that spectral shapes generally agree. Contrasts were systematic within the energy bands and were not global. The study concludes on the notion that the unobstructed surfaces produce stronger fluctuations at the largest scales, whereas the built environments intensify turbulence at smaller scales. Full article

GNSS reflectometry (GNSS-R) altimetry has been widely used for retrieving surface elevation over oceans, cryosphere, and land. Recently, UAV-borne GNSS-R systems have gained attention due to their flexibility for low-altitude and localized observations. However, lightweight UAV platforms impose strict payload and real-time processing constraints. At low altitudes, the small geometric delay between direct and reflected signals often leads to waveform overlap, degrading conventional altimetry algorithms. In this study, a lightweight UAV-borne GNSS-R receiver and a signal-separation-based altimetry method are proposed. Direct and reflected signals are separated using waveform characteristics without relying on external height information, mitigating the impact of waveform overlap. Simulations and experiments using a SPIRENT 9000 GNSS simulator demonstrate stable height retrieval under dynamic low-altitude conditions while maintaining real-time capability, confirming the feasibility of lightweight UAV GNSS-R altimetry for rapid elevation monitoring. Full article

Land surface bidirectional reflectance distribution functions (BRDF) are critical for quantitative remote sensing but are significantly constrained by scale effects, limiting the interoperability of multi-resolution data and the accuracy of quantitative inversion, thereby rendering the investigation of BRDF multi-scale effects increasingly urgent. This study utilized UAV (Unmanned Aerial Vehicle)-based multi-angular observations and the RPV model to retrieve the BRDF of typical land covers, employing the Window Averaging Method to simulate multi-scale responses and systematically investigate the relationship between BRDF characteristics and spatial scale. The results indicate the following key findings: (1) The RPV (Rahman–Pinty–Verstraete) model demonstrated high robustness and inversion accuracy, yielding RMSE (Root Mean Square Error) below 0.06 and RRMSE (Relative RMSE) below 25% across all land covers, with the 840 nm band exhibiting superior performance. (2) Significant spatial scale effects were observed, where BRDF characteristics varied distinctively with scale but eventually stabilized at specific thresholds; specifically, the stabilization scales were identified as 1.3 m for bare soil, 1.5 m for tea plantations, 1 m for rice, and 2 m for forests. (3) The scale evolution of BRDF features exhibited a parallel trend with spatial heterogeneity, a correlation that enables the quantitative identification of optimal observation scales for different land cover types. Full article

Crop monitoring over large land extensions represents a central challenge in precision agriculture, especially in polyculture contexts where species with different nutritional needs are combined. This study presents a methodology to manage and analyze large volumes of multispectral images captured by unmanned aerial vehicles (UAVs) in order to identify and monitor crops at the plant level. The images are efficiently stored and retrieved using a Hilbert Curve, which reduces the complexity of the search process from $O\left(n^2\right)$ to $O(log(n\left)\right)$ where n represents the number of indexed data points). The system connects to a distributed Structured Query Language (SQL) database, allowing for fast image retrieval based on GPS coordinates and other metadata. Additionally, the Normalized Difference Vegetation Index (NDVI) is calculated using reflectance data from the red and near-infrared channels, adjusted by semantic segmentation masks generated with a U-Net model, which allows for species-specific evaluations. The methodology was evaluated on a 20,000 m² polyculture farm with coffee, avocado, and plantain crops, using a dataset of 270 aerial images partitioned into 70% for training and 30% for validation. The results show improvements in retrieval speed and precision with the Hilbert Space-Filling Curve (HSFC) approach, and an accuracy of 82.3% and an the Mean Intersection over Union (MIoU) of 68.4% in species detection with the U-Net model. Overall, this integrated framework demonstrates a scalable potential for precision agriculture in complex polyculture systems, facilitating efficient data management and targeted crop interventions. Full article