Unmanned aircraft systems (UAS) remain a relatively new domain of aviation despite having existed for decades. Rapid technological advances and the significant expansion of UAS applications over the past decade have accelerated research and development, while the emergence of electric vertical take-off and landing (eVTOL) aircraft has further increased interest in future unmanned and highly automated air transport. As a result, numerous studies have proposed concepts for UAS operations, traffic management, and integration into existing aviation systems. This paper evaluates the consistency of these studies to identify common principles and establish an extended intersection representing a unified approach. A systematic comparison was conducted across predefined areas, including operational environments, airspace organisation, traffic management concepts, operational assumptions, and supported UAS types. The analysis shows that, although many studies present comprehensive frameworks, they often provide only limited coverage of key operational aspects. Consequently, most concepts lack sufficient scope, detail, and complexity to serve as standalone references for future UAS Traffic Management (UTM) development. Nevertheless, the reviewed studies do not fundamentally contradict one another and generally converge on the same strategic direction. The findings suggest that future UTM development can effectively build upon the concepts proposed by the principal organisations driving research and standardisation, while extending these frameworks with greater operational detail and implementation guidance.
The U-space concept represents an important ac celerator aimed at speeding up the integration of unmanned aircraft systems into existing airspace through the provision of services. In particular, the Network identification service and the Traffic information service depend on the supply of high-quality data. The i-Conspicuity concept aims to make all participants operating in the same airspace visible to each other, including general aviation. However, general aviation pilots usually do not want to share any information about themselves, especially not precise positional data. They also resist the installation of additional systems on their aircraft. This article aims to explore the possibilities of using the Open Glider Network system for U-space operations. The article focuses mainly on the current distribution of Open Glider Network ground receiving stations with the goal of optimizing their placement within the territory of the Czech Republic. The result of this article is a new constellation of ground receiving stations developed on the basis of the existing network of TV signal transmitters.
Using low-cost and commercial elements, we designed and realized a drone-borne microwave scatterometer in the 1–6 GHz range for subsurface imaging applications. The core of the instrument of a portable vector network analyzer associated to lightweight horn antennas produced using 3D printing. It is carried by DJI drones and accurate positioning is performed with the help of the free Centipede GNSS system. We validated our DroneSCAT instrument over a coastal parabolic sand dune, allowing the detection of buried paleosols down to 3 m. We then tested it for archeological prospecting and detected a strong reflector buried 1 m deep. The DroneSCAT instrument allows the detection of subsurface structures, with the benefit of a very agile system and without need for contact with the ground.
Forestry practices such as forest conservation, improved management, and afforestation offer important pathways for climate change mitigation through carbon sequestration. However, forests also influence the surface energy balance via changes in albedo, which can offset or enhance climate benefits depending on stand characteristics. Despite its importance, empirical data on how albedo dynamics evolves in plantation systems remain limited. This study evaluated surface albedo in hybrid poplar and white spruce plantations of varying ages and fertilization treatments in southern Quebec, using drone-mounted pyranometers. Measurements were conducted during the growing season, as well as under leaf-off and snow-covered conditions, to capture both structural and seasonal variability. Albedo remained below 0.3 during the growing season and was primarily controlled by stand development, with higher values observed in younger plantations and lower, more stable values in more mature plantations. Species effects emerged with canopy closure, as hybrid poplar exhibited higher albedo than white spruce at intermediate-to-late developmental stages. In contrast, fertilization had no consistent or persistent effect on albedo and was mainly associated with very early stages of plantation development. Leaf senescence did not lead to systematic increases in albedo, whereas snow cover exerted a dominant influence, substantially increasing surface reflectance. These results highlight the central role of canopy structure and surface conditions in controlling albedo dynamics in plantation systems. From a climate perspective, higher albedo in young plantations and under snow-covered conditions may contribute to radiative cooling, particularly in short-rotation systems such as hybrid poplar.
The performance of a commercially available uncrewed surface vessel (USV) was evaluated through extensive testing in several lakes and rivers around Charlotte, North Carolina, USA, to assess suitability of the platform to be used by a state transportation agency in various bathymetric mapping applications, including collection of sounding measurements near bridges. A first-person-view camera system was integrated onto the vessel to allow surveying in closer proximity to structures. This article reports on the experience gained in operating the USV, including visualization of results from several representative deployments. Data across 41 h of on-water testing at 10 unique locations is aggregated and analyzed to provide statistics concerning the expected battery consumption, signal quality of the global navigation satellite system receiver with distance from occluding structures, vehicle handling characteristics, and different deployment modes. This article may be useful to transportation agency personnel and other users of USV technology to gain a better understanding of the practical considerations, capabilities, and limitations of a representative small USV with a single-beam echosounder.
The work is devoted to the development of a mathematical model and a program for online modeling of the pulse jet engine working cycle and characteristics to assess the possibility of its use on unmanned aerial vehicles. The developed universal thermodynamic model describes the instantaneous change in pressure and temperature, taking into account the mixing of flows, combustion, and heat exchange in the combustion chamber of valved and valveless types of pulse jet engines, as well as the flows through the pipes and reed valves of various designs. Unlike the known works, a special online program, Pulsejet-Sim, for pulsejet modeling by a wide range of users has been developed for the first time, which is posted on a special website as a web-oriented software service that does not require downloading to the user’s computer and allows for instant calculation using server resources and secure data cloud storage. Using the developed software, preliminary mathematical modeling of known samples of pulse jet engines was performed, which showed generally satisfactory qualitative and quantitative agreement of the modeling results (error less than 10%) with the available experimental data on thrust, specific fuel consumption, cycle frequency, and other parameters.
Effective traffic management for Advanced Air Mobility (AAM) operations in low-altitude urban airspace is crucial for safety and scalability. Our study aims to bridge a critical gap in AAM traffic management by minimizing travel delays in both centralized and distributed Providers of Services for Urban Air Mobility (PSU) settings. Key contributions include methods to 1) Sectorize urban airspace for effective AAM management, 2) Centrally plan AAM routes considering limited capacities in corridors and vertiports, and 3) Manage airspace in distributed PSU settings while considering traffic flow capacities and interactions among PSUs. Specifically, the research combines community detection algorithms with Voronoi diagrams to sectorize individual PSU airspace. Corridor route planning is performed with a custom-weighted Dijkstra's algorithm. Centralized AAM traffic flow management adopts Mixed-Integer Programming (MIP) to minimize overall network delay costs. Distributed PSU network management is formulated as bi-level optimization using cooperative game theory and MIP, where individual PSUs update their strategies based on game theory outcomes. The simulation environment features a randomized no-fly zone, population density maps, and vertiport capacities assigned to artificial cities. Three vehicle configurations with varying ranges and adjustable speeds (i.e., minimum to cruise speeds) are simulated under three service priorities in Monte Carlo simulations. AAM flight operations are evaluated by optimization cost and runtime. This research provides a technical framework and insights into the comparison of centralized and distributed AAM network managements. The paper will facilitate informed decision-making in the development and implementation of AAM traffic management strategies.
Introducing drones into the health care sector is a recent advancement with minimal investigation of the context-specific factors related to their ethical deployment in First Nation environments. This qualitative study aimed to gain First Nations’ insights into the ethical use of drones within health care settings, responding to calls for drone perspectives in global health. In the summer of 2024, we held eight semi-structured interviews with First Nations Peoples working in drone technology in Canada. We employed thematic analysis, generating 18 inductive codes, which led to the construction of six themes: cultural sensitivity and inclusion, health care delivery and accessibility, ethical and legal considerations, education and community engagement, challenges and limitations, and future potential and recommendations. Our findings enhance understanding of the context-specific concerns and challenges that may arise when deploying drones within First Nations communities, specifically for health care use. Our recommendations stress engaging First Nations communities as essential partners. By addressing cultural, ethical, and practical considerations, stakeholders may create more effective and inclusive drone projects that improve health care delivery and empower First Nations communities.
Quadrotor drones face multiple challenges such as accuracy and real-time performance when tracking targets in a constantly changing and dynamic environment. To improve the target tracking accuracy and flight control stability of quadrotor drones in dynamic scenes, a quadrotor drone control method combining multi-scale target tracking algorithm and Type-2 fuzzy control is proposed. Firstly, a multi-scale object detection method based on kernel correlation filter is adopted, which can effectively cope with target scale and position changes through multi-scale analysis. Second, employing Type-2 fuzzy control to handle uncertainties in the control process ensures that the quadcopter drone accurately adjusts its flight state during target tracking. Experimental results show that the accuracy of the multi-scale object detection method based on kernel correlation filter is 0.97 on 1600 datasets. In terms of the comprehensive performance of target tracking and flight control, the accuracy of the Type-2 fuzzy control model is 0.92, the precision is 0.91, the recall rate is 0.91, the F1 value is 0.90, and the area under the curve value reaches 0.93, demonstrating strong target tracking ability and control accuracy. Experimental results show that the proposed multi-scale target tracking fuzzy control for quadrotor drones has excellent performance, providing a reliable control scheme for the application of quadrotor drones in complex dynamic environments.
The escalating global biodiversity crisis requires innovative and scalable solutions to monitor wildlife populations. Recent developments in remote sensing and deep learning offer promising avenues for improving the conservation of large mammals, including African elephants. This paper introduces a framework that utilizes drone video streams and integrates state-of-the-art object detection (YOLOv11) and tracking (BoT-SORT) methods, which are significantly enhanced by a custom post-track re-identification algorithm, to capture temporal dynamics and track individual elephants over time. The framework facilitates automated video analysis and elephant counting, generating key metrics such as individual elephant movement speed, group movement patterns, and elephant cluster statistics. By automating aspects of data processing and analyses, this approach provides valuable insights that contribute to more efficient and data-driven decision-making in wildlife research.
Wind flow patterns around building features are crucial for the safe operation of drones and urban air mobility missions. Fixed anemometer stations and computational fluid dynamics simulations face limitations in measuring airflow behind buildings. The use of a drone-mounted ultrasonic anemometer addresses these limitations by providing high-resolution measurements, flexibility in positioning, and the ability to capture real-time localized wind patterns in complex urban environments. This paper presents a wind tunnel study using full factorial design of experiment on a low-cost drone-mountable ultrasonic anemometer to be used to measure urban wind fields. Measurements under uniform flow conditions, with reference instruments, validate the anemometer's performance in airspeed and direction measurements. The study also explores the anemometer's capabilities in measuring wake characteristics behind a cylinder in turbulent flow. The results demonstrate that using compact ultrasonic anemometers for drone-based anemometry in urban environments can yield adequate measurements within specific angle of attack ranges.
Peatland restoration addresses multiple United Nation’s Sustainable Development Goals and offers numerous ecosystem services. Quantifying surface water storage and captured sediment volumes of emplaced dams on restoration sites is highly desirable, for example as evidence to funders, but cannot be practically achieved with field surveys due to the number of sites and number of dams per site. In contrast, camera-equipped drones can efficiently capture high spatial resolution photographs of restoration sites which can be used to construct digital surface models, from which volumetrics can be calculated using Geographical Information Systems. This approach was demonstrated using a 5-year repeat drone survey of a restoration site (∼0.18 km2) containing 125 stone dams and 329 coir rolls. Stone dams and coir rolls were estimated to provide 248.0 m3 of potential surface water storage in July 2019, of which ∼71% was filled by captured sediment by July 2024. Stone dams accounted for ∼83% of potential surface water storage volume in 2019 and ∼68% in 2024, as well as ∼88% of the captured sediment. Efficiently quantifying surface water storage provides evidence to restoration funders with water-related interests and may help with the development of water-related credits/metrics as additional mechanisms to privately finance peatland restoration.
This systematic analysis seeks to assess drones’ practical uses, legal issues, benefits, and limitations in emergency medical services, thereby contributing to a better understanding of their future potential. Data from peer-reviewed articles about drone deployment in medical situations were gathered by thoroughly searching electronic databases and pertinent literature. The studies were evaluated based on their methodology, context-specific issues, and findings on drone operating efficacy. The review highlighted various benefits of drone use, including notably shorter reaction times and increased access to remote or difficult-to-reach locations. However, obstacles such as legal restrictions, limited payload capabilities, and technical constraints in harsh weather conditions were significant. Use of drones to quickly transport Automated External Defibrillators (AEDs) in urban and rural settings, which can double the chance of surviving if done during the time of first intervention. Drones have the potential to be a strong asset to emergency medical services, improving patient care and response times in crucial but regular situations. Technical, legislative, and logistic barriers still need to be overcome to envisage its future use. Additional research is necessary to enhance the functionality of drones and the standardization of their integration alongside public health emergency response planning to balance innovation with safety and to realize maximal benefit with adherence to regulatory provisions.
We introduced a novel framework for integrating drone technology with flood risk mapping called Drone Optimized Flood Risk Map (DOFRM), it uses a Drone Optimized Grid, GIS-based Multi-Criteria Decision Model (MCDM) and Analytical Hierarchy Process (AHP). After reviewing specifications of 178 contemporary drones, we determined a grid size of 1.2x1.2 km to be optimal for drone surveys. This grid was overlaid onto a flood risk map derived from multiple hazards and vulnerabilities, selected based on an in-depth literature review. Weights for these factors were determined using AHP. Stakeholders, including emergency responders, drone operators, and GIS specialists, evaluated DOFRM’s efficacy. This approach identified 17% of the study area as highly susceptible to flooding. It was subdivided based on critical features including urban areas (3%), active channel (5%), roads (6%), rail networks (1%) stream networks (3%) and all populated areas (9%) for smart drone employment in large scale floods. The method allows for further subdivision of critical features based on grid size and available drone effort for various survey objectives. This approach can be applied by disaster response organizations for incorporating drones into disaster mitigation planning and large-scale flood survey, marking a new paradigm in flood mitigation strategies.
Unmanned aerial vehicles, or drones, have become a weapon of choice on the modern battlefield. As military and civilian industries try to develop effective counter-drone systems, early detection of flying drones still poses multiple challenges to state-of-the-art computer vision technologies. These challenges include a growing variety of military and commercial drones, their small size compared to piloted aircraft, blending with the background, similarity to birds, etc. In our experiments on drone images of variable size, we have observed a rapid drop in the accuracy of a state-of-the-art drone detection model when applied to distant drones that take a relatively small area on the entire image. However, we show that this early detection accuracy can be significantly improved by applying the drone detection model to an image masked by the Canny edge detector. We suggest applying the model to the original and the masked images in parallel and determining a drone detection decision by the highest confidence value under the condition that the detected object is not recognized as a bird by a general-purpose object detection model. The results of our evaluation experiments confirm the effectiveness of the proposed drone detection approach.
The digital revolution in construction is driving the nexus of emerging technologies. Technologies, such as the Artificial Intelligence of Things (AIoT), are transforming the sector by enhancing efficiency, promoting sustainability, and fostering innovation in construction projects. However, extant literature has failed to document integrated applications of Artificial Intelligence (AI) and Internet of Things (IoT). The interface of AIoT with drones and their applications in the construction industry is underreported. To address this gap, the current study systematically reviewed literature from the Scopus and Web of Science repositories to uncover the applications of AIoT-enabled drones. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols were applied to review 33 highly relevant articles. The results show applications of AIoT-powered drones in various construction sectors such as land surveying and site selection, site layout, analytics and logistical planning, quality and progress monitoring, safety management, regulatory compliance, facility and asset management, and disaster management. Theoretical and practical implications, challenges, and future directions for research and industry to adopt AIoT-powered drones are reported. This study is a pioneering effort investigating the applications of AIoT-powered drones in the construction industry with equal benefits for researchers, academics, and industry practitioners
The safe integration of new and emerging technologies such as remotely piloted aircraft (RPA) into existing airspace will require pilots’ acceptance of such technology. Therefore, the main aim of the present research is to understand pilots’ willingness to operate with crewed and uncrewed aircraft in an integrated airspace. The research also aims to examine whether factors such as vertical separation, automation levels, and technology acceptance affect pilots’ level of airspace integration acceptance. Eighty-five pilots completed a battery of surveys, comprising a demographics questionnaire, a technology acceptance questionnaire, as well as a newly created Aircraft Separation Questionnaire. The results revealed a clear automation acceptance threshold in acceptance between automation levels 3 and 4, the point at which decision-making transitioned from the human to the automation. The analysis found no significant relationship between vertical separation and technology acceptance. These results illustrate that pilots are more receptive towards integrated airspace operations when controlling decisions are undertaken by a human.
This study develops a hybrid optimization framework integrating remotely piloted aircraft systems (RPASs) with conven-tional truck delivery networks to enhance last-mile logistics efficiency. To balance operating cost, service time, regulatory risk, and energy usage, a novel multi-objective mixed-integer linear programming model is developed. High-quality Pareto-optimal solutions are produced by the non-dominated sorting genetic algorithm II, which methodically manages trade-offs between the conflicting goals. Risk assessment is embedded using specific operations risk assessment principles, and energy consumption is optimized through dynamic battery management strategies for RPASs. Extensive computational experiments demonstrate that the proposed hybrid truck-RPAS system achieves notable operational improvements compared to traditional truck-only models. The model yields an 8.3% reduction in operational costs, an 8.6% decrease in delivery time, an 11.2% reduction in cumulative risk indices, and a 9.4% decrease in overall battery usage. Convergence analysis and scalability evaluation further confirm the robustness and practical viability of the proposed solution approach. By integrating regulatory compliance, energy sustainability, and operational resilience, this research provides a scalable and adaptable framework for the effective deploy-ment of RPAS technologies in urban logistics systems, addressing key challenges of modern supply chains and supporting future sustainable transportation initiatives.
Drones are a valuable tool in flood response, providing high-resolution data and real-time monitoring capabilities. However, their limited range, swath, and battery life make it challenging to cover extensive flood-prone areas. This study addresses these limitations by introducing the drone-optimized flood risk map (DOFRM) framework, integrating drones with geographic information systems (GIS) and multi-criteria decision model to prioritize survey areas. The approach leverages analytical hierarchy process (AHP) to rank high-priority zones for effective drone survey and disaster response. The study evaluated 178 drones to identify an optimal survey grid size of 1.2 x 1.2 km for efficient drone operation. This grid was placed over a flood risk map, which is a combination of various hazard and vulnerability factors, with each factor given a weight based on AHP criteria. DOFRM revealed that 17% of the region was highly susceptible to flooding. This high-risk area was further divided based on the critical regions: urban areas (3%), active channels (5%), roads (6%), rail networks (1%), stream networks (3%), and populated areas (9%). DOFRM was perceived effective and easier to use through technology acceptance model-based stakeholder's survey. The framework enables prioritized drone deployment during large-scale flood events by optimizing resources for rapid assessment of vulnerable areas. By combining AHP-based prioritization with a GIS-based drone-optimized grid, the approach offers a systematic solution for flood risk mapping and disaster mitigation. This innovative framework enhances targeted flood surveys, enabling drone operations more effective and responsive to surveying needs.
In this work, three path planning algorithms for autonomous unmanned aerial vehicles (UAVs) are proposed to be implemented in two urban scenarios. The first scenario is a simple environment without obstacles and the second scenario is a realistic environment in which an urban environment with its infrastructures such as bridge is modeled. In the first step, the test environments are modeled using Gazebo 3D with Robot Operating System. Then, 3D occupancy grid mapping using the Octomap library. Lastly, the path planning simulation is conducted for three sampling-based algorithms, namely, Probabilistic Roadmap (PRM), Rapidly Exploring Random Tree-star (RRT*), and Expansive Space Tree (EST). Three performance objectives from each algorithm are evaluated, i.e., planning time, path length, and number of subpoints. The results show that in a simpler environment with fewer disturbances, PRM or EST is more suited as they take less computational time. In complex environments with more obstacles, EST is more suitable to be implemented as it generates shorter paths with reasonable time but when the shortest path distance is mandatory, selecting RRT* is preferable. Overall, the selected methods can be easily applied in an actual environment by programming the instructions in a microcontroller of a UAV equipped with similar depth camera sensors.
