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Traffic safety at signalized intersections is significantly affected by the driver's behavior, psychology, and the design of traffic signals, especially in mixed traffic conditions. At signalized intersections, the traffic signal design influences the traffic behavior during the yellow change interval. The indecisiveness or the dilemma in decision-making during the yellow change interval often results in hard deceleration or red-light running, which concerns the safety of the road users in the form of rear-end and side-angled conflicts. Thus, it is essential to understand how traffic behavior and signal design changes affect safety. The present work investigates traffic behavior at signalized intersections operating under mixed traffic conditions using high-quality vehicular trajectory data obtained using unmanned aerial vehicles (UAVs). The analyzed traffic behavior is related to Type I and Type II dilemma zones at intersections with two types of traffic flow conditions: oversaturated and undersaturated. However, it is found that the position of these dilemma zones is near the stop line for an oversaturated intersection and away from the stop line for an undersaturated intersection, as it depends on the approach speeds of vehicles. The driver's decision-making behavior, which depends on the driver's psychology, is also analyzed, and a binary logistic regression model is developed for determining the Type II dilemma zone by considering various parameters at the onset of the yellow change interval. Further, the variation in the length of the Type I dilemma zone is studied by performing a sensitivity analysis and proposing measures for mitigating its negative effects. The findings underscore the critical importance of considering both traffic behavior and signal design in the safety analysis of signalized intersections, particularly in mixed traffic environments where driver behavior, psychology, and vehicle interactions are more unpredictable. Practical Applications: The findings from this study have significant practical applications for enhancing traffic safety at signalized intersections, especially in mixed traffic conditions like those in India. Utilizing high-quality trajectory data collected via UAVs, this research precisely identifies dilemma zones and comprehensively analyzes driver behavior during the yellow change interval. One key application is in optimizing traffic signal design and timing. By accurately locating Type I and Type II dilemma zones, traffic engineers can adjust signal timings and phase durations to minimize hard decelerations and red-light running, thereby reducing rear-end and side-angle collisions. The development of a binary logistic regression model for predicting Type II dilemma zones can be integrated into intelligent traffic management systems. This model allows for dynamic signal timing adjustments based on real-time traffic conditions, improving the adaptability and efficiency of traffic signals. Additionally, the proposed mitigation measures, such as adjusting the yellow change interval length and implementing speed control measures, can be directly applied by traffic authorities to enhance intersection safety, particularly in mixed traffic conditions. [ABSTRACT FROM AUTHOR]

Commonly found landmines, such as the TM-62M, MON-100, and PDM-1, in the recent Russia–Ukraine war confirm the continued use of metals in munitions. Traditional demining techniques, primarily relying on handheld metal detectors and Ground Penetrating Radar (GPR) systems, remain state of the art for subsurface detection. However, manual demining with handheld metal detectors can be slow and pose significant risks to operators. Drone-based metal detection techniques offer promising solutions for rapid and effective landmine detection, but their reliability and accuracy remain a concern, as even a single missed detection can be life-threatening. This study evaluates the potential of an airborne metal detection system as an alternative to traditional handheld detectors. A comparative analysis of three distinct metal detectors for landmine detection is presented: the EM61Lite, a sensitive airborne metal detection system (tested in a pseudo-drone-based scenario); the CTX 3030, a traditional handheld all-metal detector; and the ML 3S, a traditional handheld ferrous-only detector. The comparison focuses on the number of metallic targets each detector identifies in a controlled test field containing inert landmines and UXOs. Our findings highlight the strengths and limitations of airborne metal detection systems like the EM61Lite and emphasize the need for advanced processing techniques to facilitate their practical deployment. We demonstrate how our experimental normalization technique effectively identifies additional anomalies in airborne metal detector data, providing insights for improved detection methodologies. [ABSTRACT FROM AUTHOR]

Efficiently managing agricultural systems necessitates accurate data collection from crops to examine phenotypic characteristics and improve productivity. Traditional data collection processes for specialty horticultural crops are often subjective, labor-intensive, and may not provide accurate information for precise management decisions in phenotypic studies and crop production. Reliable and standardized techniques to record and evaluate crop features using agricultural technology are essential for improving agricultural systems. The objective of the research was to develop a methodology for accurate measurement of blackberry flowers and vegetation coverage using UAV remote sensing and image analysis. The UAV captured 20,812 images in the visible spectrum, and ImageJ software (version 1.54k) was used for segmenting floral and vegetative coverage to calculate variety-specific flower coverage. A moderately strong positive correlation (r = 0.71) was found between flower-to-vegetation ratio (FVR) and visually estimated flower area, validating UAV-derived flower coverage as a reliable method for estimating flower density in blackberries. The regression model explained 51% of the variance in flower estimates (R2 = 0.51), with a root mean square error (RMSE) of 2.79 flower/cm2. Additionally, distinct temporal flowering patterns were observed between primocane- and floricane fruiting blackberries. Vegetative growth also exhibited stability, with strong correlations between consecutive weeks. The temporal analysis provided insight into growth phases and flowering peaks critical for time-sensitive management practices. UAV computer vision for quantifying blackberry phenotypic features is an effective tool and a unique methodology that speeds up the data collection process at high accuracy for breeding research and farm data management. [ABSTRACT FROM AUTHOR]

To meet the real-time interactive formation changes and personalized performance requirements of drone swarms, this paper proposed a new offline trajectory generation algorithm based on distributed model predictive control (DMPC). This method began with mathematical modeling of point-to-point formation changes in drone formations and solved the optimization problem. This paper introduced a new on-demand collision avoidance strategy for DMPC and proposed a rapid DMPC algorithm for multi-drone point-to-point transitions. By combining with intelligent target point allocation, the method reduced computational pressure while achieving more efficient flight trajectories. The study conducted simulations and compared the method with existing methods for performance evaluation. The results show that the proposed method significantly improves the success rate of trajectory generation, the rate of trajectory generation, and the efficiency of trajectory flight. The new algorithm quickly generates efficient flight trajectories for point-to-point formation changes in drone swarms, meeting the real-time interactivity and personalized needs of drone swarm flight performances. [ABSTRACT FROM AUTHOR]

In order to improve the positioning accuracy of the traditional master-slave unmanned aerial vehicle (UAV) cooperative navigation algorithm based on range and angle measurement information, given the angle and range measurement errors of low-cost slave UAV measuring equipment, the error model of the cooperative navigation system of master-slave UAV is reconstructed. The navigation and measurement errors of the slave UAV are estimated and compensated. The state equations and measurement equations are derived, then the algorithm is implemented using Kalman filter. Simulation results show that for a slave UAV's inertial navigation system based on a low accuracy micro electromechanical system (MEMS) with a gyro drift level of 10 (°) /h, the root mean square (RMS) of the east and the north velocity errors within 500 s are 0.25 m/s and 0.74 m/s, respectively, and the RMS of the latitude and the longitude errors are 17.10 m and 9.10 m, respectively, under single master UAV combined navigation. The speed accuracy is 3-10 times higher than that of the traditional algorithm, and the position accuracy is about 20 times higher. The positioning accuracy is close to the level of master UAV under double mater UAVs' measurement references. The estimation accuracy of range measurement errors of the slave UAV is high, while the estimation accuracy of angle measurement errors is affected by the heading accuracy of the slave UAV itself. The estimation accuracy of angle measurement error can be further improved if heading references such as magnetic heading exist. [ABSTRACT FROM AUTHOR]

This paper studies the problem of plane search of unmanned aerial vehicles (UAVs) using dynamic sensors. A gradient-type nominal search strategy is studied. Since this strategy leads to local optimum which causes the suspension of the task, it does not guarantee to meet the goal of covering the search domain such that each point is surveyed for a certain level. A new strategy is consequently developed based on the nominal search strategy. Furthermore, a novel search strategy, taking advantage of area division, is proposed for the demand that the excessive search level and time cost are both as low as possible. Simulation results of a single UAV and multi-UAVs demonstrate the feasibility of the search strategies. [ABSTRACT FROM AUTHOR]

Aiming at the noise, cost and safety problems of current unmanned aerial vehicle (UAV) operation within cities, this paper divides the airspace into layers by altitudes and proposes an operation cost and crash risk model based on noise protection zones under aerodynamic and other constraints. This model satisfies the standard operation conditions of UAVs, and reduces the noise impact on the environment and the ground population, as well as operation cost and crash risk. Thus, based on the noise protection zone, the improved Dubins path planning method is used. It combines the Dubins path planning idea with the tangent line of the geometric circle, and adds node processing to enrich the optional paths for UAV operation and optimize the paths. As the Dijkstra algorithm is used as the the best path searching algorithm, and the lowest total cost is set as the goal, a UAV optimal operation path is searched and compared with the A* algorithm. Simulation experiments verify the effectiveness of the proposed model and the improved method. They reduce the noise impact of UAV operation and the operation cost, and improve the safety and efficiency of the operation. The results indicate that the optimal operation altitude of the example UAV, a biplane medium-sized UAV with a mass of 15 kg and a paddle disk area of about 1.313 m², is 40 m, and its operation minimum total cost is 5.42; compared with the results in the other altitude layers, the total operation cost is reduced by 37.56% at most and 5.91% at least. [ABSTRACT FROM AUTHOR]

The threat power transmission and distribution projects pose to the ecological environment has been widely discussed by researchers. The scarcity of early environmental monitoring and supervision technologies, particularly the lack of effective real-time monitoring mechanisms and feedback systems, has hindered the timely quantitative identification of potential early-stage environmental risks. This study aims to comprehensively review the literature and analyze the research context and shortcomings of the advance warning technologies of power transmission and distribution projects construction period using the integrated space–sky–ground system approach. The key contributions of this research include (1) listing ten environmental risks and categorizing the environmental risks associated with the construction cycle of power transmission and distribution projects; (2) categorizing the monitoring data into one-dimensional, two-dimensional, and three-dimensional frameworks; and (3) constructing the potential environmental risk knowledge system by employing the knowledge graph technology and visualizing it. This review study provides a panoramic view of knowledge in a certain field and reveals the issues that have not been fully explored in the research field of monitoring technologies for potential environmental damage caused by power transmission and transformation projects. [ABSTRACT FROM AUTHOR]

Accurate delineation of spring phenology (e.g., start of growing season, SOS) of deciduous forests is essential for understanding its responses to environmental changes. To date, SOS dates from analyses of satellite images and vegetation index (VI) −based phenological models have notable discrepancies but they have not been fully evaluated, primarily due to the lack of ground reference data for evaluation. This study evaluated the SOS dates of a deciduous broadleaf forest estimated by VI-based phenological models from three satellite sensors (PlanetScope, Sentinel-2A/B, and Landsat-7/8/9) by using ground reference data collected by a small unmanned aerial vehicle (sUAV). Daily sUAV imagery (0.035-meter resolution) was used to identify and generate green leaf maps. These green leaf maps were further aggregated to generate Green Leaf Fraction (GLF) maps at the spatial resolutions of PlanetScope (3-meter), Sentinel-2A/B (10-meter), and Landsat-7/8/9 (30-meter). The temporal changes of GLF differ from those of vegetation indices in spring, with the peak dates of GLF being much earlier than those of VIs. At the SOS dates estimated by VI-based phenological models in 2022 (Julian days from 105 to 111), GLF already ranges from 62% to 96%. The moderate accuracy and large uncertainties of SOS dates from VI-based phenological models arise from the limitations of vegetation indices in accurately tracking the number of green leaves and the inherent uncertainties of the mathematical models used. The results of this study clearly highlight the need for new research on spring phenology of deciduous forests. [ABSTRACT FROM AUTHOR]

In this article, an attitude-position obstacle avoidance trajectory tracking control scheme based on barrier functions is proposed for a quadrotor unmanned aerial vehicle (UAV). First, the barrier functions are designed based on the relationship between the position and attitude of the quadrotor UAV and obstacles. Then, by decoupling the quadrotor UAV system into a position control subsystem and an attitude control subsystem and incorporating integral barrier Lyapunov functions (IBLFs) into the backstepping process, a novel obstacle avoidance tracking control strategy for the position subsystem and the attitude subsystem is constructed. Compared with the existing obstacle avoidance results, the designed obstacle avoidance strategy can simultaneously achieve obstacle avoidance on the position and attitude of the quadrotor UAV. Finally, the efficiency of the proposed method is verified by simulation results. [ABSTRACT FROM AUTHOR]

Despite comprising only one per cent of global forests, mangroves provide vital ecological and economic benefits to their ecosystems. Due to its decreasing extent over the past decade, there is a rise in research innovations supporting mangrove conservation. Specifically, consumer-grade Unmanned Aerial Vehicles (UAV) were proven effective as potential remote sensing alternatives to support mangrove research and monitoring in recent studies. As most studies use custom UAV-mounted sensors for mangrove species classification, similar studies using a UAV's default red–green–blue (RGB) cameras were scarce. This study explores the potential of high-resolution RGB aerial images through state-of-the-art object detection algorithm, YOLOv5 to detect the dominant Rhizophora mangroves in Sarawak, Malaysia. A total of 400 RGB images were equally selected from two study areas and allocated into three datasets, two corresponding to each study area and one combining all images. The annotation process was performed using a previously proposed novel method, assisted by YOLOv5 for a semi-automated annotation process with expert verification. Systematic training experiments were conducted to select an optimal epoch size across models trained with each dataset. The final models produced an average true positive rate of 73.8% and 71.7% for each study site, while the combined dataset model produced an average true positive rate of 73.7%. Overall, this study demonstrated the potential of UAV-based RGB images and deep learning object detection architectures to identify specific mangrove objects, while also highlighting key considerations for similar future research. [ABSTRACT FROM AUTHOR]

The diverse range of uses of unmanned aerial vehicles has garnered significant attention in research. The scientific literature that supports the data obtained from UAVs recording information from various sensors is presented in this manuscript. It summarizes current developments in remote sensing, including radar, photogrammetry, thermal imaging, light detection and ranging sensors (LiDAR), data gathering, and analysis. It is predicated on the instruments' ability to gather and analyze accurate data. To identify some of the most urgent research problems, it also shows surveys based on research methodologies. The present research focuses on the proliferation and social effects of unmanned aerial vehicles (UAVs). It also encourages novice researchers to pursue this area of study and suggest novel approaches to the design or setup of these flying machines. UAVs have entirely transformed due to advancements in internet technology and current technologies which include camera defects, environmental monitoring, charging, impediments, crop monitoring, energy consumption, military applications, and technology gaps. [ABSTRACT FROM AUTHOR]

Decision-making for autonomous maneuvering in dynamic, uncertain, and nonlinear environments represents a challenging frontier problem. Deep deterministic policy gradient (DDPG) is an effective method to solve such problems, but it is found that complex strategies require extensive computation and time in the learning process. To address this issue, we propose a node clustering (NC) method, inspired by grid clustering, integrated into the DDPG algorithm for the learning of complex strategies. In the NC method, the node membership degree is defined according to the specific characteristics of the maneuvering decision-making problem, and error handling strategies are designed to reduce the number of transitions in the replay database effectively, ensuring that the most typical transitions are retained. Then, combining NC and DDPG, an autonomous learning and decision-making algorithm of maneuvering is designed. The algorithm flow and the pseudo-code of the algorithm are given. Finally, the NC_DDPG algorithm is applied to a typical short-range air combat maneuvering decision problem for verification. The results show that the NC_DDPG algorithm significantly accelerates the autonomous learning and decision-making process under both balanced and disadvantageous conditions, taking only about 77% of the time required by Vector DDPG. The scale of NC impacts learning speed; the simulation results across five scales indicate that smaller clustering scales significantly increase learning time, despite a high degree of randomness. Compared with Twin Delayed DDPG (TD3), NC_DDPG consumes only 0.58% of the time of traditional TD3. After applying the NC method to TD3, NC_DDPG requires approximately 20–30% of the time of NC_TD3. [ABSTRACT FROM AUTHOR]

Accurately understanding the stand composition of shelter forests is essential for the construction and benefit evaluation of shelter forest projects. This study explores classification methods for dominant tree species in shelter forests using UAV-derived RGB, hyperspectral, and LiDAR data. It also investigates the impact of individual tree crown (ITC) delineation accuracy, crown morphological parameters, and various data sources and classifiers. First, as a result of the overlap and complex structure of tree crowns in shelterbelt forests, existing ITC delineation methods often lead to over-segmentation or segmentation errors. To address this challenge, we propose a watershed and multi-feature-controlled spectral clustering (WMF-SCS) algorithm for ITC delineation based on UAV RGB and LiDAR data, which offers clearer and more reliable classification objects, features, and training data for tree species classification. Second, spectral, texture, structural, and crown morphological parameters were extracted using UAV hyperspectral and LiDAR data combined with ITC delineation results. Twenty-one classification images were constructed using RF, SVM, MLP, and SAMME for tree species classification. The results show that (1) the proposed WMF-SCS algorithm demonstrates significant performance in ITC delineation in complex mixed forest scenarios (Precision = 0.88, Recall = 0.87, F1-Score = 0.87), resulting in a 1.85% increase in overall classification accuracy; (2) the inclusion of crown morphological parameters derived from LiDAR data improves the overall accuracy of the random forest classifier by 5.82%; (3) compared to using LiDAR or hyperspectral data alone, the classification accuracy using multi-source data improves by an average of 7.94% and 7.52%, respectively; (4) the random forest classifier combined with multi-source data achieves the highest classification accuracy and consistency (OA = 90.70%, Kappa = 0.8747). [ABSTRACT FROM AUTHOR]

This survey provides a comprehensive comparison of prominent open-source unmanned aerial vehicle (UAV) autopilots, focusing on their hardware compatibility, software features, and communication protocols. Additionally, it assesses the impact of these autopilots on research and education by examining their potential for integration with companion computers, compatibility with robot operating system (ROS) middleware and the MATLAB/Simulink environment, and the availability of simulation-in-the-loop (SITL) and hardware-in-the-loop (HITL) simulation tools. The paper concludes with a discussion of the advantages and disadvantages of these leading open-source autopilots. [ABSTRACT FROM AUTHOR]

Given rapid glacier thinning and retreat observed in the Pyrenees in recent decades, an updated glacier inventory and continuous mass balance assessments are important to understand the ongoing variability and changes of these very small glaciers (< 0.5 km2). The mass balance years 2021/22 and 2022/23 were characterised by prolonged extreme heat waves and reduced snow duration that severely affected the Pyrenees, which also impacted their glaciers. This paper reviews the criteria for classifying ice bodies as glaciers or ice patches, presents the latest high-resolution glacier inventory for the Pyrenees, and quantifies the mass losses caused by the extreme climate conditions in 2022 and 2023. The glacierised area was determined by manual mapping of high-resolution (0.2 m spatial resolution) aerial orthomosaics acquired by unmanned aerial vehicles (UAVs) and aerial orthophotos (0.25 m spatial resolution) for the few glaciers not surveyed by UAVs. 3D point clouds, also obtained from UAV flights, were used to update the results for the change in surface elevation (glacier thickness) and mass balance between 2020 and 2023. For the Pyrenees, the total glacierised area in 2023 is 143.2 ± 1.8 ha in 15 different glaciers and 8 ice masses were degraded to ice patches according to our criteria. The resulting area change between 2020 and 2023 is -94.8 ha, representing a -39.8% decrease of the glaciarised area from 2020 to 2023, increasing the annual ratio of area change from 2020 to 2023 by -8.7% yr−1 compared to the period 2011–2020 (-2.4% yr−1). The change in glacier thickness measured on 12 glaciers shows a decrease of -2.52 m yr−1 for the period 2020–2023, which represents a significant acceleration in glacier thickness loss compared to -0.80 m yr−1 for the period 2011–2020. The three glaciers (Infiernos, Monte Perdido and Aneto) on which annual geodetic measurements were carried out showed slightly higher glacier thickness losses (-0.91 m yr−1) in the first mass balance year (2020/21) than in the previous decade (2011–2020), while the losses in the last two mass balance years (2021/22 and 2022/23) were three to four times higher (-3.42 m yr−1 and -3.07 m yr−1 respectively) and exceeded the record values. [ABSTRACT FROM AUTHOR]

Mining-induced ground fissures are common problems associated with mining damage in shallowly buried coal seams in the western mining area of China. To evaluate the surface mining damage of the 12203 working face of the Huojitu Colliery in Shendong mining area, low-altitude infrared aerial surveys were conducted on the ground at the static fissure area (O-A1) and the dynamic fissure area (O-A2) of the working face. The temperature evolution patterns of fissures, sand and plants in the infrared images were analysed. The relationship between overburden fractures and surface fissure temperature was revealed, and the influence range and temperature self-healing period of the surface affected by underground mining were determined. The results indicated that underground mining could lead to a decrease in the ground temperature above the working face. The surface temperature evolution can be divided into three zones: a temperature stabilization zone before mining, a temperature cooling zone during mining, and a temperature recovery zone after mining. The temperature of sand and plants above the working face exhibited quadratic curve changes in O-A1 and O-A2, respectively. The length of the temperature reduction zone affected by mining is 40 m in O-A2, and 46.8 m in O-A1. The temperature recovery periods of ground fissures in O-A1 and O-A2 were 4.0 and 4.6 d, respectively. These findings could provide a basis for evaluating mining ground damage. [ABSTRACT FROM AUTHOR]

Introduction: The emergence rate of crop seedlings is an important indicator for variety selection, evaluation, field management, and yield prediction. To address the low recognition accuracy caused by the uneven size and varying growth conditions of crop seedlings under salt-alkali stress, this research proposes a peanut seedling recognition model, MS-YOLOv8. Methods: This research employs close-range remote sensing from unmanned aerial vehicles (UAVs) to rapidly recognize and count peanut seedlings. First, a lightweight adaptive feature fusion module (called MSModule) is constructed, which groups the channels of input feature maps and feeds them into different convolutional layers for multi-scale feature extraction. Additionally, the module automatically adjusts the channel weights of each group based on their contribution, improving the feature fusion effect. Second, the neck network structure is reconstructed to enhance recognition capabilities for small objects, and the MPDIoU loss function is introduced to effectively optimize the detection boxes for seedlings with scattered branch growth. Results: Experimental results demonstrate that the proposed MS-YOLOv8 model achieves an AP50 of 97.5% for peanut seedling detection, which is 12.9%, 9.8%, 4.7%, 5.0%, 11.2%, 5.0%, and 3.6% higher than Faster R-CNN, EfficientDet, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and RT-DETR, respectively. Discussion: This research provides valuable insights for crop recognition under extreme environmental stress and lays a theoretical foundation for the development of intelligent production equipment. [ABSTRACT FROM AUTHOR]

Despite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate of the system by using interference technology to enhance it. To this end, the system not only deploys multiple UAV base stations (BSs) to provide services to legitimate users but also assigns dedicated UAV jammers to send interference signals to active or potential eavesdroppers to disrupt their eavesdropping effectiveness. Based on this configuration, we formulate the optimization process of parameters such as the user association variables, UAV trajectory, and output power as a sequential decision-making problem and use the single-agent soft actor-critic (SAC) algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm to achieve joint optimization of the core parameters. In addition, for specific scenarios, we also use the multi-agent soft actor-critic (MASAC) algorithm to solve the joint optimization problem mentioned above. The numerical results show that the normalized average secrecy rate of the MASAC algorithm increased by more than 6.6% and 14.2% compared with that of the SAC and TD3 algorithms, respectively. [ABSTRACT FROM AUTHOR]

Species-level phenology models are essential for predicting shifts in tree species under climate change. This study quantified phenological differences among dominant miombo tree species and modeled seasonal variability using climate variables. We used TIMESAT version 3.3 software and the Savitzky–Golay filter to derive phenology metrics from bi-monthly PlanetScope Normalized Difference Vegetation Index (NDVI) data from 2017 to 2024. A repeated measures Analysis of Variance (ANOVA) assessed differences in phenology metrics between species, while a regression analysis modeled the Start of Season (SOS) and End of Season (EOS). The results show significant seasonal and species-level variations in phenology. Brachystegia spiciformis differed from other species in EOS, Length of Season (LOS), base value, and peak value. Surface solar radiation and skin temperature one month before SOS were key predictors of SOS, with an adjusted R-squared of 0.90 and a Root Mean Square Error (RMSE) of 13.47 for Brachystegia spiciformis. SOS also strongly predicted EOS, with an adjusted R-squared of 1 and an RMSE of 3.01 for Brachystegia spiciformis, indicating a shift in the growth cycle of tree species due to seasonal variability. These models provide valuable insights into potential phenological shifts in miombo species due to climate change. [ABSTRACT FROM AUTHOR]

Remote visual inspections are valuable tools for maintaining bridges in safe operation. In the case of old structures with incomplete documentation, the verification of dimensions is also an essential aspect. This paper presents an attempt to use a Scanning Total Station (STS) and Unmanned Aerial Vehicle (UAV) for the inspection and inventory of bridge dimensions. The STS's measurements are conducted by applying two methods: the direct method using a total station (TS) and advanced geometric analyses of the collected point cloud. The UAV's measurements use a Structure from Motion (SfM) method. Verification tests were conducted on a steel truss railway bridge over the largest river in Poland. The measurements concerned both the basic dimensions of the bridge and the details of a selected truss connection. The STS identified a significant deviation in the actual geometry of the measured connection and the design documentation. The UAV's inspection confirmed these findings. The integration of STS and UAV technologies has demonstrated significant advantages, including STS's high accuracy in direct measurements, with deviations within acceptable engineering tolerances (below a few mm), and the UAV's efficiency in covering large areas, achieving over 90% compliance with reference dimensions. This combined approach not only reduces operating costs and enhances safety by minimizing the need for heavy machinery or scaffolding but also provides a more comprehensive understanding of the structural condition. [ABSTRACT FROM AUTHOR]

In recent years, micro-sized electric unmanned aerial vehicle (UAVs) have gained widespread applications in both defense and civilian sectors owing to their advantages of lightweight construction, portability, and cost-effectiveness. However, a significant drawback is the difficulty in discerning and controlling the flight attitude and trajectory of these UAVs when operating beyond the line of sight. Consequently, they heavily rely on remote control for flight operations, leading to a high degree of operational complexity. Hence, it is challenging to maintain the desired characteristics of micro-sized aerial vehicle systems, such as high sensitivity and stability. In this study, we aimed to enhance the longitudinal flight stability of micro-sized electric UAVs. To achieve this goal, control of a UAV's elevator was utilized to ensure stability in pitch and roll during flight. We employed the Digital Airborne Tactical Communications System (DATCOM) software developed by the United States Air Force to calculate the fundamental aerodynamic coefficients of the UAV. Subsequently, the longitudinal motion state space equation was employed to derive transfer functions for the pitch angle θ and the horizontal stabilizer deflection de. Furthermore, we utilized Simulink to compare the effects of two control methods, traditional Proportional-Integral-Derivative (PID) and fuzzy PID, on the longitudinal flight stability of the UAV. We aimed to identify the optimal PID values for the UAV. Finally, we validated through practical flight tests that Fuzzy PID can enhance the longitudinal flight stability of the UAV while also contributing to new technological solutions for stability in micro-sized UAV flight. [ABSTRACT FROM AUTHOR]

Rural wetlands are complex landscapes where rivers, croplands, and villages coexist, making water quality monitoring crucial for the well-being of nearby residents. UAV-based imagery has proven effective in capturing detailed features of water bodies, making it a popular tool for water quality assessments. However, few studies have specifically focused on drone-based water quality monitoring in rural wetlands and their seasonal variations. In this study, Xiangfudang Rural Wetland Park, Jiaxin City, Zhejiang Province, China, was taken as the study area to evaluate water quality parameters, including total nitrogen (TN), total phosphors (TP), chemical oxygen demand (COD), and turbidity degree (TUB). We assessed these parameters across summer and winter seasons using UAV multispectral imagery and field sample data. Four machine learning algorithms were evaluated and compared for the inversion of the water quality parameters, based on the situ sample survey data and UAV multispectral images. The results show that ANN algorithm yielded the best results for estimating TN, COD, and TUB, with validation R2 of 0.78, 0.76, and 0.57, respectively; CatBoost performed best in TP estimation, with validation R2 and RMSE values of 0.72 and 0.05 mg/L. Based on spatial estimation results, the average COD concentration in the water body was 16.05 ± 9.87 mg/L in summer, higher than it was in winter (13.02 ± 8.22 mg/L). Additionally, mean TUB values were 18.39 Nephelometric Turbidity Units (NTU) in summer and 20.03 NTU in winter. This study demonstrates the novelty and effectiveness of using UAV multispectral imagery for water quality monitoring in rural wetlands, providing critical insights into seasonal water quality variations in these areas. [ABSTRACT FROM AUTHOR]

Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, by using methods such as keyword co-contribution analysis and author co-occurrence analysis in bibliometrics, we found out the hot-spots of this field. UAV platforms equipped with various types of cameras and other advanced sensors, combined with artificial intelligence (AI) algorithms, especially for deep learning (DL) were reviewed. Acknowledging the critical role of comprehending crop diseases and pests, along with their defining traits, we provided a concise overview as indispensable foundational knowledge. Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. Furthermore, we summarized crop diseases and pests monitoring techniques using DL and introduced the application for prediction and classification. Take it a step further, the newest and the most concerned applications of large language model (LLM) and large vision model (LVM) in agriculture were also mentioned herein. At the end of this review, we comprehensively discussed some deficiencies in the existing research and some challenges to be solved, as well as some practical solutions and suggestions in the near future. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) as an aerial base station or relay device is a promising technology to rapidly provide wireless connectivity to ground device. Given UAV's agility and mobility, ground user's mobility, a key question is how to analyze and value the performance of UAV-based wireless channel in the terahertz (THz) band. In this paper, a three-dimensional (3D) time-varying channel model is proposed for UAV-based dual-mobility wireless channels based on geometric channel model theory in THz band. In this proposed channel model, the small-scale fading (e.g., scattering fading and reflection fading) on rough surfaces of communication environment and the atmospheric molecule absorption attenuations are considered in THz band. Moreover, the statistical properties of the proposed channel model, including path loss, time autocorrelation function (T-ACF) and Doppler power spectrum density (DPSD), have been derived and the impact of several important UAV-related and vehicle-related parameters have been investigated and compared to millimeter wave (mm-wave) band. Furthermore, the correctness of the proposed channel model has been verified via simulation, and some useful observations are provided for the system design of THz UAV-based dual-mobility wireless communication systems. [ABSTRACT FROM AUTHOR]

Identification of nutritious rice varieties through non-destructive detection technology is important for high-quality seed production. With the development of technology, rapid and non-destructive identification methods based on unmanned aerial vehicle (UAV) remote sensing technology are increasingly gaining attention in the scientific community. This study utilized hyperspectral imaging technology to acquire spectral reflectance data from the rice canopy during the grain filling stage. Different models (stepwise multiple linear regression (SMLR) and the Back Propagation Neural Network (BPNN)) for estimating rice protein content based on canopy spectral information were constructed using both multiple stepwise regression and BP neural networks. The results showed that the model based on BPNN estimation performed best for predicting grain protein content, with an R2 = 0.9516 and RMSE = 0.3492, indicating high accuracy and stability in the model. Overall, hyperspectral imaging technology combined with various models could significantly help to identify rice varieties. Further, the current findings provide a technical reference for the selection of high-quality rice varieties in a non-destructive manner. [ABSTRACT FROM AUTHOR]

In beyond fifth-generation (B5G) networks, free-space optical (FSO) communication is anticipated to play a crucial role thanks to its high data rates and minimal system complexity. Therefore, infrequently occurring poor weather can impair its performance. The combination of FSO and radio frequency (RF) has proven a successful solution to address the increasing demand for high data rates in wireless communication networks. Due to their adaptability in terms of deployment and movement, unmanned aerial vehicles (UAVs) are also projected to be crucial in B5G networks. This paper investigates a UAV-aided hybrid FSO/DRF backhauling system using a matching game theory and Multi-Agent deep reinforcement learning (MARL) framework. We deploy a UAV to provide a user offloading service to an existing ground base station (GBS) facing a reduced backhaul capacity due to weather attenuation (e.g., fog). It is considered that the GBS has a pre-installed FSO backhaul connection to a macro-base station. However, during adverse weather conditions, the FSO backhaul is severely affected, compromising the reliability of the FSO link. The novelty here is the hybrid FSO/RF backhauling system and how it is utilized to address weather-related challenges. The UAV is deployed at the ideal height to increase system throughput using MARL, and the frequency division between the GBS and the UAV is also tuned. The system's effectiveness is assessed using meteorological data from the British cities of Edinburgh and London. The numerical outcomes demonstrate the suggested scheme's superiority and efficacy over traditional approaches. The time estimation shows that for a maximum of 30 users, the time consumed is 300 s which is lesser and effective. [ABSTRACT FROM AUTHOR]

The Galileo High Accuracy Service (HAS) is a free of charge Global Navigation Satellite System (GNSS) augmentation service provided by the European Union. It is designed to enable real-time Precise Point Positioning (PPP) with a target accuracy (at the 95% confidence level) of 20 cm and 40 cm in the horizontal and vertical components, respectively, to be achieved within 300 s. The performance of the service has been confirmed with geodetic-grade receivers. However, mass market applications require low-cost GNSS receivers connected to low-cost antennae. This paper focuses on the performance of the real-time static and kinematic positioning achieved with Galileo HAS and low-cost GNSS receivers. The study is limited to GPS + Galileo dual-frequency positioning, thus exploiting the full potential of Galileo HAS SL1. We demonstrate that the target accuracy of Galileo HAS SL1 is reached with both geodetic-grade and low-cost receivers in dual-frequency static and kinematic applications in open-sky conditions. Precision of a few centimeters is reached for static positioning, while kinematic positioning results in subdecimeter precision. Vertical accuracy is limited by missing phase center offset models for low-cost antennas. In general, the performance of low-cost hardware using Galileo HAS for real-time PPP is comparable to that of geodetic-grade hardware. Therefore, combining low-cost GNSS receivers with Galileo HAS is feasible and justified. [ABSTRACT FROM AUTHOR]

AbstractLandmines significantly hinder the rapid movement of military forces and pose major obstacles during critical operations. Unmanned aerial vehicles (UAVs), commonly known as drones, offer several significant advantages for landmine detection. Most studies have tested drone-based systems at controlled sites and demonstrated their effectiveness in limited scenarios, with no examples of using these systems to detect landmines and relay the results to mine clearance machines (MCMs) for actual removal in real military operations. This study evaluated the use of UAVs equipped with magnetometers to detect metallic landmines in military applications. By conducting a series of controlled experiments, the research identified optimal flight conditions—2 m/s flight speed, 1 m survey interval, and 0.5 m sensor altitude—that balance accuracy and operational efficiency. The findings demonstrate that UAV-based magnetometer systems can significantly enhance mine clearance operations by providing near real-time data to MCMs. This approach offers a safer, faster, and more cost-effective alternative to traditional landmine detection methods by addressing the limitations of ground-based operations, such as high risk to human operators and inefficiency. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Unmanned aerial vehicle (UAV) imagery highlights the extent of illegal deforestation in protected areas for the biodiverse humid forest of central Madagascar. The ultra‐high‐resolution (<10‐cm pixel) images enable the creation of detailed forest 3D base maps and provide the means to quantify forest stand losses. To help communities safeguard their forests, local non‐governmental organisations can use UAV maps in combination with weekly deforestation alerts to facilitate an immediate on‐ground response that significantly restricts illegal activity. Integrating ultra‐high‐resolution UAV mapping and coarse‐resolution freely available satellite imagery should have much wider applications in Madagascar and the humid tropics for community‐based conservation. Summary: This study of the Ambohimahamasina humid forest shows that small UAVs offer a detailed (<10‐cm pixel), rapid and cost‐effective solution to provide maps of detailed deforestation patterns not visible in satellite imagery.Calculating forest extent and volume are valuable ways to rapidly assess forest losses and prioritise areas for ground patrols. The use of 3‐dimensional measurements for above ground carbon estimates indicate how, in the future, these metrics could be used to calculate carbon payments for conservation programs.By combining UAV and free satellite imagery, an effective alert system has been developed that supports community initiatives in the protection of their natural forest resources.The wealth of ultra‐high‐resolution UAV data collected in this study provides insights into forest dynamics, supports local community forest management, and has the potential to measure the value of the forest. [ABSTRACT FROM AUTHOR]

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Recent advancements in Unmanned Aerial Vehicle (UAV) technology have made them effective platforms for data capture in applications like environmental monitoring. UAVs, acting as mobile data ferries, can significantly improve ground network performance by involving ground network representatives in data collection. These representatives communicate opportunistically with accessible UAVs. Emerging technologies such as Software Defined Wireless Sensor Networks (SDWSN), wherein the role/function of sensor nodes is defined via software, can offer a flexible operation for UAV data-gathering approaches. In this paper, we introduce the "UAV Fuzzy Travel Path", a novel approach that utilizes Deep Reinforcement Learning (DRL) algorithms, which is a subfield of machine learning, for optimal UAV trajectory planning. The approach also involves the integration between UAV and SDWSN wherein nodes acting as gateways (GWs) receive data from the flexibly formulated group members via software definition. A UAV is then dispatched to capture data from GWs along a planned trajectory within a fuzzy span. Our dual objectives are to minimize the total energy consumption of the UAV system during each data collection round and to enhance the communication bit rate on the UAV-Ground connectivity. We formulate this problem as a constrained combinatorial optimization problem, jointly planning the UAV path with improved communication performance. To tackle the NP-hard nature of this problem, we propose a novel DRL technique based on Deep Q-Learning. By learning from UAV path policy experiences, our approach efficiently reduces energy consumption while maximizing packet delivery. [ABSTRACT FROM AUTHOR]

This research analyses the snow depth distribution in canopy gaps across two plots in Central Pyrenees, to improve understanding of snow–forest and topography interactions. Snow depth maps, forest structure–canopy gap (FSCG) characteristics and topographic variables were generated by applying Structure from Motion algorithms (SfM) to images acquired from Unmanned Aerial Vehicles (UAVs). Six flights were conducted under different snowpack conditions in 2021, 2022 and 2023. Firstly, the snow depth database was analysed in terms of the ratio between the radius of the canopy gap and the maximum height of the surrounding trees (r/h), in order to classify the gaps as small‐size, medium‐size, large‐size, or open areas at both sites independently. Then Kendall's correlation coefficients between the snow depth, FSCG and topographic variables were computed and a Random Forest (RF) model for each survey was implemented, to determine the influence of these variables in explaining snow depth patterns. The results demonstrate the consistency of the UAV SfM photogrammetry approach for measuring snowpack dynamics at fine scale in canopy gaps and open areas. At the northeast exposed Site 1, the larger the r/h observed, the greater was the snow depth obtained. This pattern was not evident at the southwest exposed Site 2, which presented high variability related to the survey dates and categories, highlighting the relevance of topography for determining optimum snow accumulation in forested areas. Slope systematically exhibited a negative and significant correlation with snow depth and was consistently the highest‐ranked variable for explaining snow distribution at both sites according to the RF models. Distance to the Canopy Edge also presented high influence, especially at Site 1. The findings suggest differences in the main drivers throughout each site and surveys of the topographic and FSCG variables are needed to understand snow depth distribution over heterogeneous mountain forest domains. [ABSTRACT FROM AUTHOR]

Despite comprising only one per cent of global forests, mangroves provide vital ecological and economic benefits to their ecosystems. Due to its decreasing extent over the past decade, there is a rise in research innovations supporting mangrove conservation. Specifically, consumer-grade Unmanned Aerial Vehicles (UAV) were proven effective as potential remote sensing alternatives to support mangrove research and monitoring in recent studies. As most studies use custom UAV-mounted sensors for mangrove species classification, similar studies using a UAV’s default red–green–blue (RGB) cameras were scarce. This study explores the potential of high-resolution RGB aerial images through state-of-the-art object detection algorithm, YOLOv5 to detect the dominant Rhizophora mangroves in Sarawak, Malaysia. A total of 400 RGB images were equally selected from two study areas and allocated into three datasets, two corresponding to each study area and one combining all images. The annotation process was performed using a previously proposed novel method, assisted by YOLOv5 for a semi-automated annotation process with expert verification. Systematic training experiments were conducted to select an optimal epoch size across models trained with each dataset. The final models produced an average true positive rate of 73.8% and 71.7% for each study site, while the combined dataset model produced an average true positive rate of 73.7%. Overall, this study demonstrated the potential of UAV-based RGB images and deep learning object detection architectures to identify specific mangrove objects, while also highlighting key considerations for similar future research.

The diverse range of uses of unmanned aerial vehicles has garnered significant attention in research. The scientific literature that supports the data obtained from UAVs recording information from various sensors is presented in this manuscript. It summarizes current developments in remote sensing, including radar, photogrammetry, thermal imaging, light detection and ranging sensors (LiDAR), data gathering, and analysis. It is predicated on the instruments’ ability to gather and analyze accurate data. To identify some of the most urgent research problems, it also shows surveys based on research methodologies. The present research focuses on the proliferation and social effects of unmanned aerial vehicles (UAVs). It also encourages novice researchers to pursue this area of study and suggest novel approaches to the design or setup of these flying machines. UAVs have entirely transformed due to advancements in internet technology and current technologies which include camera defects, environmental monitoring, charging, impediments, crop monitoring, energy consumption, military applications, and technology gaps.

The unmanned aerial vehicle (UAV) blood transportation service is a convenient blood delivery way provided by the Shenzhen Blood Centre. It is also an essential part of the “Air-ground Integrated” blood delivery model. UAV blood delivery can avoid ground traffic or impassable road conditions, making blood transportation faster and more efficient, which is really important for urgent blood transfusions. The 5G+ UAV blood transportation intelligent platform achieves real-time high-definition video and cold chain temperature feedback throughout the entire process under the support of artificial intelligence and visual image analysis technologies. This UVA blood transportation system allows for visualized, manageable, controllable and traceable intelligent, integrated and open management, ensuring the quality and safety of cold chain blood transportation. Multiple medical institutions in Shenzhen have already implemented UAV blood delivery. However, there is currently a lack of safety standards for UAV blood transportation. To address this, the Blood Transfusion Branch of Shenzhen Medical Association has organized experts to discuss and formulate this consensus that aims to promote the standardized management of UAV blood delivery to better serve clinical patients.

Abstract Mining-induced ground fissures are common problems associated with mining damage in shallowly buried coal seams in the western mining area of China. To evaluate the surface mining damage of the 12203 working face of the Huojitu Colliery in Shendong mining area, low-altitude infrared aerial surveys were conducted on the ground at the static fissure area (O-A1) and the dynamic fissure area (O-A2) of the working face. The temperature evolution patterns of fissures, sand and plants in the infrared images were analysed. The relationship between overburden fractures and surface fissure temperature was revealed, and the influence range and temperature self-healing period of the surface affected by underground mining were determined. The results indicated that underground mining could lead to a decrease in the ground temperature above the working face. The surface temperature evolution can be divided into three zones: a temperature stabilization zone before mining, a temperature cooling zone during mining, and a temperature recovery zone after mining. The temperature of sand and plants above the working face exhibited quadratic curve changes in O-A1 and O-A2, respectively. The length of the temperature reduction zone affected by mining is 40 m in O-A2, and 46.8 m in O-A1. The temperature recovery periods of ground fissures in O-A1 and O-A2 were 4.0 and 4.6 d, respectively. These findings could provide a basis for evaluating mining ground damage.

Unmanned aerial counting of pacific salmon in the rivers of the Okhotsk district (Khabarovsk Region) in 2021–2023 is analyzed. Estimates of the spawning redds number and the spawners abundance are presented for the monitoring sites. Prospects for using these indices for forecasting the stocks are discussed. Specifics of unmanned counting are considered, and options for further development of the methodology are proposed. The method of unmanned counting is suitable for information support of the stock forecasts only, but can be modified for application in fishery management and spawning grounds inventory. Possibilities for further automation in the unmanned counting of pacific salmon are considered on the base of the authors’ experience and literature information. Proposed approaches to automation are outlined for key stages of unmanned counting, as spatial coverage planning, aerial photography acquisition, photogrammetric processing, vectorization, and abundance estimation. Features of the automation implementation depend on purposes of the counting and are different for the stock forecasting, fishery management, and spawning grounds inventory. Adequate automation should be based on ensuring consistency and mutual balance of approaches used at different stages of the counting.

Non-contact remote sensing techniques, such as terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry, have been globally applied for landslide monitoring in high and steep mountainous areas. These techniques acquire terrain data and enable ground deformation monitoring. However, practical application of these technologies still faces many difficulties due to complex terrain, limited access and dense vegetation. For instance, monitoring high and steep slopes can obstruct the TLS sightline, and the accuracy of the UAV model may be compromised by absence of ground control points (GCPs). This paper proposes a TLS- and UAV-based method for monitoring landslide deformation in high mountain valleys using traditional real-time kinematics (RTK)-based control points (RCPs), low-precision TLS-based control points (TCPs) and assumed control points (ACPs) to achieve high-precision surface deformation analysis under obstructed vision and impassable conditions. The effects of GCP accuracy, GCP quantity and automatic tie point (ATP) quantity on the accuracy of UAV modeling and surface deformation analysis were comprehensively analyzed. The results show that, the proposed method allows for the monitoring accuracy of landslides to exceed the accuracy of the GCPs themselves by adding additional low-accuracy GCPs. The proposed method was implemented for monitoring the Xinhua landslide in Baoxing County, China, and was validated against data from multiple sources.

Abstract Mapping dominant tree species in miombo woodlands is essential for enhancing their monitoring and management. We evaluated PlanetScope imagery to map Julbernardia globiflora, Brachystegia spiciformis, and Pterocarpus tinctorius in Tongwe Forest Reserve, Tanzania. The study assessed the effectiveness of PlanetScope bands in discriminating tree species and investigated how different months/seasons influenced tree species classification. Optimal months (seasons) and spectral bands were selected using Principal Component loading, temporal pattern analysis, mean decrease in accuracy, and mean decrease Gini techniques. Random forest classification was employed for tree species classification, and accuracy was assessed using an error matrix. The study identified March, July, and September as optimal months for acquiring imagery, with effective bands including blue, green-1, green, yellow, red, and red-edge. July and September imagery in the dry season achieved higher overall accuracies of 65% and 69%, respectively, while March imagery in the wet season reached 55%. The highest overall accuracy of 72% was achieved using images from different seasons. Producer’s accuracy was highest for Brachystegia spiciformis (79%) and Julbernardia globiflora (95%), whereas Pterocarpus tinctorius had lower accuracy (25%). User’s accuracy varied with 74% for Brachystegia spiciformis, 70% for Julbernardia globiflora, and 67% for Pterocarpus tinctorius. Mapping accuracy was notably high for Brachystegia spiciformis and Julbernardia globiflora, reflecting their high sample size (dominance) and distinct phenology. The yellow and red bands were particularly effective in distinguishing miombo tree species demonstrating PlanetScope’s capability. Future research should focus on scaling up PlanetScope’s application for broad miombo tree species mapping.

Introduction. In recent years, unmanned aerial vehicles (UAVs) have been a rapidly advancing field. In all areas of UAV application, positioning accuracy is of particular importance. For outdoor environments, satellite navigation systems (such as GPS) are always the method of choice. However, for indoor environments, GPS signal weakening becomes a serious obstacle for determining the UAV location. A number of studies have been carried out to develop various indoor positioning technologies that meet the criteria of compactness and light weight, thus suitable for small aircrafts, including optical flows, inertial measurement systems, ultrasound, etc. However, there is a lack of comparative studies reviewing indoor positioning technologies for autonomous UAVs. The existing reviews fail to provide a comprehensive assessment of such technologies and their operational principles according to the main criteria. In this connection, this paper aims to review modern indoor positioning technologies and their operational principles, conducting evaluation according to such criteria as accuracy, operating range, cost. The assessment of promising machine vision-based technologies is carried out.Aim. To classify modern indoor navigation technologies for UAVs; to assess the technologies under consideration according to various criteria.Materials and methods. The current technologies for UAV indoor positioning were classified by the signal type used for connection and their capability to process information without external signals. The technologies were assessed according to the following criteria: accuracy, operating range, cost, as well as their advantages and disadvantages.Results. А classification and evaluation table of UAV indoor positioning technologies is proposed; a review of the current developments in the field is given.Conclusion. A review of UAV indoor positioning technologies has been carried out. In addition, the prospects of machine vision-based technologies are outlined.

The rapid development of electric vehicles (EVs) and unmanned aerial vehicles (UAVs) provides new ways for personnel search and material distribution during emergency periods. This paper proposes an EV-UAV joint rescue system, in which the UAVs use the EVs as charging and maintenance base stations to provide various services for the objects to be rescued, and the EVs can use distributed generations to obtain diversified electricity supply, which improves the adaptability and endurance level of the system in emergencies. The coordinated scheduling model of the EV-UAV system is established in the mixed-integer linear programming (MILP) formulation, which considers factors including electricity consumption, electricity replenishment, loading capacity, distribution route, and distribution time window of the EVs and the UAVs. Case studies verify the validity of the model proposed, compare the EV-UAV and ground vehicle (GV)-UAV rescue systems, and illustrate the technical characteristics and application potential of the EV-UAV system in emergency assistance.