Editor’s Choice Articles

Micro aerial vehicle (MAV) fleets have gained essential recognition in the decision schemes for precision agriculture, disaster management, and other coverage missions. However, they have some challenges in becoming massively deployed. One of them is resource management in restricted workspaces. This paper proposes a plan to balance resources when considering the practical use of MAVs and workspace in daily chores. The coverage mission plan is based on five stages: world abstraction, area partitioning, role allocation, task generation, and task allocation. The tasks are allocated according to agent roles, Master, Coordinator, or Operator (MCO), which describe their flight autonomy, connectivity, and decision skill. These roles are engaged with the partitioning based on the Voronoi-tessellation but extended to heterogeneous polygons. The advantages of the MCO Plan were evident compared with conventional Boustrophedon decomposition and clustering by K-means. The MCO plan achieved a balanced magnitude and trend of heterogeneity between both methods, involving MAVs with few or intermediate resources. The resulting efficiency was tested in the GAMA platform, with gained energy between 2% and 10% in the mission end. In addition, the MCO plan improved mission times while the connectivity was effectively held, even more, if the Firefly algorithm generated coverage paths. Full article

Given the stagnating progress in the fight against malaria, there is an urgent need for area-wide integrated vector management strategies to complement existing intra-domiciliary tools, i.e., insecticide-treated bednets and indoor residual spraying. In this study, we describe a pilot trial using drones for aerial application of Aquatain Mosquito Formulation (AMF), a monomolecular surface film with larvicidal activity, against the African malaria mosquito Anopheles arabiensis in an irrigated rice agro-ecosystem in Unguja island, Zanzibar, Tanzania. Nine rice paddies were randomly assigned to three treatments: (a) control (drone spraying with water only), (b) drone spraying with 1 mL/m², or (c) drone spraying with 5 mL/m² of AMF. Compared to control paddies, AMF treatments resulted in highly significant (p < 0.001) reductions in the number of larvae and pupae and >90% fewer emerging adults. The residual effect of AMF treatment lasted for a minimum of 5 weeks post-treatment, with reductions in larval densities reaching 94.7% in week 5 and 99.4% in week 4 for the 1 and 5 mL/m² AMF treatments, respectively. These results merit a review of the WHO policy regarding larval source management (LSM), which primarily recommends its use in urban environments with ‘few, fixed, and findable’ breeding sites. Unmanned aerial vehicles (UAVs) can rapidly treat many permanent, temporary, or transient mosquito breeding sites over large areas at low cost, thereby significantly enhancing the role of LSM in contemporary malaria control and elimination efforts. Full article

Small unmanned aerial systems (UAS) have been increasingly popular in surveying and mapping tasks. While photogrammetry has been the primary UAS sensing technology in other industries, construction activities can also benefit from accurate surveying measurements from airborne LIDAR. This paper discusses a custom-designed low-cost UAS-based LIDAR system that can effectively measure construction excavation and bulk piles. The system is designed with open interfaces that can be easily upgraded and expanded. An error model was developed to predict the horizontal and vertical errors of single point geo-registration for a generic UAS-LIDAR. This model was validated for the proposed UAS-LIDAR system using calibration targets and real-world measurements from different scenarios. The results indicated random errors from LIDAR at approximately 0.1 m and systematic errors at or below centimeter level. Additional pre-processing of the raw point cloud can further reduce the random errors in LIDAR measurements of bulk piles. Full article

Novel electric aircraft designs coupled with intense efforts from academia, government and industry led to a paradigm shift in urban transportation by introducing UAM. While UAM promises to introduce a new mode of transport, it depends on ground infrastructure to operate safely and efficiently in a highly constrained urban environment. Due to its novelty, the research of UAM ground infrastructure is widely scattered. Therefore, this paper selects, categorizes and summarizes existing literature in a systematic fashion and strives to support the harmonization process of contributions made by industry, research and regulatory authorities. Through a document term matrix approach, we identified 49 Scopus-listed scientific publications (2016–2021) addressing the topic of UAM ground infrastructure with respect to airspace operation followed by design, location and network, throughput and capacity, ground operations, cost, safety, regulation, weather and lastly noise and security. Last listed topics from cost onwards appear to be substantially under-represented, but will be influencing current developments and challenges. This manuscript further presents regulatory considerations (Europe, U.S., international) and introduces additional noteworthy scientific publications and industry contributions. Initial uncertainties in naming UAM ground infrastructure seem to be overcome; vertiport is now being predominantly used when speaking about vertical take-off and landing UAM operations. Full article

A biplane quadrotor is a hybrid type of UAV that has wide applications such as payload pickup and delivery, surveillance, etc. This simulation study mainly focuses on handling the total rotor failure, and for that, we propose a control architecture that does not only handle rotor failure but is also able to navigate the biplane quadrotor to a safe place for landing. In this structure, after the detection of total rotor failure, the biplane quadrotor will imitate reallocating control signals and then perform the transition maneuver and switch to the fixed-wing mode; control signals are also reallocated. A synthetic jet actuator (SJA) is used as the redundancy that generates the desired virtual deflection to control the pitch angle, while other states are taken care of by the three rotors. The SJA has parametric nonlinearity, and to handle it, an inverse adaptive compensation scheme is applied and a closed-loop stability analysis is performed based on the Lyapunov method for the pitch subsystem. The effectiveness of the proposed control structure is validated using numerical simulation carried out in the MATLAB Simulink. Full article

Although drone thermography is increasingly applied as an archaeological remote sensing tool in the last few years, the technique and methods are still relatively under investigated. No doubt there are successes in positive identification of buried archaeology, and the prospection technique has clear complementary value. Nevertheless, there are also instances where thermograms did not reveal present shallow buried architectural features which had been clearly identified by, for example, ground-penetrating radar. The other way around, there are cases where the technique was able to pick up a signals of buried archaeology at a time of day that is supposed to be very unfavorable for thermographic recording. The main issue here is that the exact factors determining the potential for tracing thermal signatures of anthropomorphic interventions in the soil are many, and their effect, context, and interaction under investigated. This paper deals with a systematic application of drone thermography on two archaeological sites in different soils and climates, one in The Netherlands, and one in Italy, to investigate important variables that can make the prospection technique effective. Full article

Traditional forest monitoring has been mainly performed with images or orthoimages from aircraft or satellites. In recent years, the availability of high-resolution 3D data has made it possible to obtain accurate information on canopy size, which has made the topic of canopy 3D growth monitoring timely. In this paper, forest growth pattern was studied based on a canopy point cloud (PC) reconstructed from UAV aerial photogrammetry at a daily interval for a year. Growth curves were acquired based on the canopy 3D area (3DA) calculated from a triangulated 3D mesh. Methods for canopy coverage area (CA), forest coverage rate, and leaf area index (LAI) were proposed and tested. Three spectral vegetation indices, excess green index (ExG), a combination of green indices (COM), and an excess red union excess green index (ExGUExR) were used for the segmentation of trees. The results showed that (1) vegetation areas extracted by ExGUExR were more complete than those extracted by the other two indices; (2) logistic fitting of 3DA and CA yielded S-shaped growth curves, all with correlation R² > 0.92; (3) 3DA curves represented the growth pattern more accurately than CA curves. Measurement errors and applicability are discussed. In summary, the UAV aerial photogrammetry method was successfully used for daily monitoring and annual growth trend description. Full article

There are many challenges that impact the current referral network for Tuberculosis (TB) sputum specimens in Mozambique. In some cases, health facilities are remote and the road infrastructure is poor and at times impassable, leading to delays in laboratory specimen transportation and long turn-around times for results. Drone transportation is a promising solution to reduce transportation time and improve access to laboratory diagnostics if the sample quality is not compromised during transport. This study evaluated the impact of drone transportation on the quality of TB sputum specimens with suspected Mycobacterium tuberculosis. 156 specimens were collected at five (5) health centers and sent to the Instituto Nacional de Saúde (INS) National TB Reference Laboratory. Specimens were then equally divided into two aliquots; one to be transported on land and the other by air using a drone. Control and study group specimens were processed using the NALC-NaOH method. Agreement between sample and control specimens was acceptable, indicating that drone transportation did not affect the quality of TB specimens. The authors recommend additional studies to validate drone transportation of TB specimens over a longer period of time to give further confidence in the adoption of drone delivery in Mozambique. Full article

In recent years, the increasing number of unmanned aerial vehicles (UAVs) in the low-altitude airspace have not only brought convenience to people’s work and life, but also great threats and challenges. In the process of UAV detection and tracking, there are common problems such as target deformation, target occlusion, and targets being submerged by complex background clutter. This paper proposes an anti-occlusion UAV tracking algorithm for low-altitude complex backgrounds by integrating an attention mechanism that mainly solves the problems of complex backgrounds and occlusion when tracking UAVs. First, extracted features are enhanced by using the SeNet attention mechanism. Second, the occlusion-sensing module is used to judge whether the target is occluded. If the target is not occluded, tracking continues. Otherwise, the LSTM trajectory prediction network is used to predict the UAV position of subsequent frames by using the UAV flight trajectory before occlusion. This study was verified on the OTB-100, GOT-10k and integrated UAV datasets. The accuracy and success rate of integrated UAV datasets were 79% and 50.5% respectively, which were 10.6% and 4.9% higher than those of the SiamCAM algorithm. Experimental results show that the algorithm could robustly track a small UAV in a low-altitude complex background. Full article

The rapid growth of data traffic due to the demands of new services and applications poses new challenges to the wireless network. Unmanned aerial vehicles (UAVs) can be a solution to support wireless networks during congestion, especially in scenarios where the region has high traffic peaks due to the temporal and spatial flow of users. In this paper, an intelligent machine-learning-based system is proposed to deploy UAV base stations (UAV-BS) to temporarily support the mobile network in regions suffering from the congestion effect caused by the high density of users. The system includes two main steps, the load prediction algorithm (LPA) and the UAV-BSs clustering and positioning algorithm (UCPA). In LPA, the load history generated by the mobile network is used to predict which macrocells are congested. In UCPA, planning is performed to calculate the number of UAV BSs needed based on two strategies: naïve and optimized, in addition to calculating the optimal positioning for each device requested to support the overloaded macrocells. For prediction, we used two models, generalized regression neural networks (GRNN) and random forest, and the results showed that both models were able to make accurate predictions, and the random forest model was better with an accuracy of over 85%. The results showed that the intelligent system significantly reduced the overhead of the affected macrocells, improved the quality of service (QoS), and reduced the probability of blocking users, as well as defined the preventive scheduling for the UAV BSs, which benefited the scheduling and energy efficiency. Full article

Coastal environments are some of the most dynamic environments in the world. As they are constantly changing, so are the technologies and techniques we use to map and monitor them. The rapid advancement of sUAS-based remote sensing calls for rigorous field and processing workflows so that more reliable and consistent sUAS projects of coastal environments are carried out. Here, we synthesize the best practices to create sUAS photo-based surveying and processing workflows that can be used and modified by coastal scientists, depending on their project objective. While we aim to simplify the complexity of these workflows, we note that the nature of this work is a craft that carefully combines art, science, and technology. sUAS LiDAR is the next advancement in mapping and monitoring coastal environments. Therefore, future work should consider synthesizing best practices to develop rigorous field and data processing workflows used for sUAS LiDAR-based projects of coastal environments. Full article

Uncrewed aerial vehicles (UAVs) are continuously gaining popularity in a wide spectrum of applications, while their positioning and navigation most often relies on Global Navigation Satellite Systems (GNSS). However, numerous conditions and practices require UAV operation in GNSS-denied environments, including confined spaces, urban canyons, vegetated areas and indoor places. For the purposes of this study, an integrated UAV navigation system was designed and implemented which utilizes GNSS, visual, depth and inertial data to provide real-time localization. The implementation is built as a package for the Robotic Operation System (ROS) environment to allow ease of integration in various systems. The system can be autonomously adjusted to the flight environment, providing spatial awareness to the aircraft. This system expands the functionality of UAVs, as it enables navigation even in GNSS-denied environments. This integrated positional system provides the means to support fully autonomous navigation under mixed environments, or malfunctioning conditions. Experiments show the capability of the system to provide adequate results in open, confined and mixed spaces. Full article

Climate-related natural disasters have affected the lives of thousands of people. Global warming creates warmer and drier conditions which increase the risk of wildfires. In large-scale disasters such as wildfires, search and rescue (SAR) operations become extremely challenging due to low visibility, difficulty to breath, and high temperature from fire and smoke. Unmanned aerial vehicles (UAVs), such as drones, have been used to support such operations. In our previous work, a Krypto module is proposed to “sniff” out wireless signals from mobile phones to locate any possible survivors. With the increased popularity of drones, it is possible to allow people to volunteer in SAR operations with their drones. In this paper, we propose an Open Collaborative Platform for multiple drones to assist SAR operations. The open platform manages different searching drones that carry the Krypto module to collaborate by sharing information and planning search paths/areas. With our Open Collaborative Platform, anyone can participate in SAR operations and contribute to finding possible survivors. The novelty of this work is the openness and collaboration of the platform that “crowdsourcing” the searching operation to a large group of people who share information and contribute to finding possible survivors in a large disaster such as wildfires. Our experimental study shows that the Open Collaborative Platform is effective in reducing both the number of drones required and the search time for finding survivors. Full article

Current studies rarely mention radar detection of hybrid vertical take-off and landing (VTOL) fixed-wing drones. We investigated radar signals of an industry-tier VTOL fixed-wing drone, TX25A, compared with the radar detection results of a quad-rotor drone, DJI Phantom 4. We used an X-band pulse-Doppler phased array radar to collect tracking radar data of the two drones in a coastal area near the Yellow Sea in China. The measurements indicate that TX25A had double the values of radar cross-section (RCS) and flying speed and a 2 dB larger signal-to-clutter ratio (SCR) than DJI Phantom 4. The radar signals of both drones had micro-Doppler signals or jet engine modulation (JEM) produced by the lifting rotor blades, but the Doppler modulated by the puller rotor blades of TX25A was undetectable. JEM provides radar signatures such as the rotating rate, modulated by the JEM frequency spacing interval and the number of blades for radar automatic target recognition (ATR), but also interferes with the radar tracking algorithm by suppressing the body Doppler. This work provides an a priori investigation of new VTOL fixed-wing drones and may inspire future research. Full article

This research offers an improved method for the self-organization of a swarm of UAVs based on a social learning approach. To start, we use three different colonies and three best members i.e., unmanned aerial vehicles (UAVs) randomly placed in the colonies. This study uses max-min ant colony optimization (MMACO) in conjunction with social learning mechanism to plan the optimized path for an individual colony. Hereinafter, the multi-agent system (MAS) chooses the most optimal UAV as the leader of each colony and the remaining UAVs as agents, which helps to organize the randomly positioned UAVs into three different formations. Afterward, the algorithm synchronizes and connects the three colonies into a swarm and controls it using dynamic leader selection. The major contribution of this study is to hybridize two different approaches to produce a more optimized, efficient, and effective strategy. The results verify that the proposed algorithm completes the given objectives. This study also compares the designed method with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to prove that our method offers better convergence and reaches the target using a shorter route than NSGA-II. Full article

Path planning of unmanned aerial vehicles (UAVs) is one of the vital components that supports their autonomy and deployment ability in real-world applications. Few path-planning techniques have been thoroughly considered for multirotor UAVs for pursuing ground moving targets (GMTs) with variable speed and direction. Furthermore, most path-planning techniques are generally devised without taking into consideration wind disturbances; as a result, they are less suitable for real-world applications as the wind effect usually causes the UAV to drift and tilt from its original course, impacting the mission’s main objective of having an adequate non-deviant camera aim point and steady coverage over the GMT. This paper presents a novel UAV path-planning technique, based on the artificial potential field (APF) for following GMTs in windy environments, to provide steady and continuous coverage over the GMT, by proposing a new modified attractive force to enhance the UAV’s sensitivity to wind speed and direction. The modified wind resistance attractive force function accommodates for any small variation of relative displacement caused by wind leading the UAV to drift in a certain direction. This enables the UAV to maintain its position by tilting (i.e., changing its roll and pitch angles) against the wind to retain the camera aim point on the GMT. The proposed path-planning technique is hardware-independent, does not require an anemometer for measuring wind speed and direction, and can be adopted for all types of multirotor UAVs equipped with basic sensors and an autopilot flight controller. The proposed path-planning technique was evaluated in a Gazebo-supported PX4-SITL and a robot operating system (ROS) for various simulation scenarios. Its performance demonstrated superiority in handling wind disturbances and showed high suitability for deployment in real-world applications. Full article

Dam removal is a river restoration technique that has complex landscape-level ecological impacts. Unmanned aerial vehicles (UAVs) are emerging as tools that enable relatively affordable, repeatable, and objective ecological assessment approaches that provide a holistic perspective of restoration impacts and can inform future restoration efforts. In this work, we use a consumer-grade UAV, structure-from-motion (SfM) photogrammetry, and machine learning (ML) to evaluate geomorphic and vegetation changes pre-/post-dam removal, and discuss how the technology enhanced our monitoring of the restoration project. We compared UAV evaluation methods to conventional boots-on-ground methods throughout the Bellamy River Reservoir (Dover, NH, USA) pre-/post-dam removal. We used a UAV-based vegetation classification approach that used a support vector machine algorithm and a featureset composed of SfM-derived elevation and visible vegetation index values to map other, herbaceous, shrub, and tree cover throughout the reservoir (overall accuracies from 83% to 100%), mapping vegetation succession as well as colonization of exposed sediments that occurred post-dam removal. We used SfM-derived topography and the vegetation classifications to map erosion and deposition throughout the reservoir, despite its heavily vegetated condition, and estimate volume changes post-removal. Despite some limitations, such as influences of refraction and vegetation on the SfM topography models, UAV provided information on post-dam removal changes that would have gone unacknowledged by the conventional ecological assessment approaches, demonstrating how UAV technology can provide perspective in restoration evaluation even in less-than-ideal site conditions for SfM. For example, the UAV provided perspective of the magnitude and extent of channel shape changes throughout the reservoir while the boots-on-ground topographic transects were not as reliable for detecting change due to difficulties in navigating the terrain. In addition, UAV provided information on vegetation changes throughout the reservoir that would have been missed by conventional vegetation plots due to their limited spatial coverage. Lastly, the COVID-19 pandemic prevented us from meeting to collect post-dam removal vegetation plot data. UAV enabled data collection that we would have foregone if we relied solely on conventional methods, demonstrating the importance of flexible and adaptive methods for successful restoration monitoring such as those enabled via UAV. Full article

This paper presents a framework for performing autonomous precise landing of unmanned aerial vehicles (UAVs) on dynamic targets. The main goal of this work is to design the methodology and the controlling algorithms that will allow multi-rotor drones to perform a robust and efficient landing in dynamic conditions of changing wind, dynamic obstacles, and moving targets. Unlike existing GNSS-based vertical landing solutions, the suggested framework does not rely on global positioning and uses adaptive diagonal approaching angle visual landing. The framework was designed to work on existing camera-drone platforms, without any need for additional sensors, and it was implemented using DJI’s API on Android devices. The presented concept of visual sliding landing (VSL) was tested on a wide range of commercial drones, performing hundreds of precise and robust autonomous landings on dynamic targets, including boats, cars, RC-boats, and RC-rovers. Full article

Although Unmanned Aerial Vehicles (UAVs)-aided wireless sensor networks (WSNs) have gained many applications, it is not for long that research works have been produced to define effective algorithms and protocols. In this article, we address the UAV-enabled WSN (U-WSN), explore the performance and the capability of the UAV, define the UAV functionalities as a communication node, and describe the architectures and the relevant typical technologies that emerge from this new paradigm. Furthermore, this article also identifies the main factors which influence the U-WSN design and analyzes the open issues and challenges in U-WSN. These insights may serve as motivations and guidelines for future designs of UAV-enabled WSNs. Full article

Coaxial rotor systems are appealing for multirotor drones, as they increase thrust without increasing the vehicle’s footprint. However, the thrust of a coaxial rotor system is reduced compared to having the rotors in line. It is of interest to increase the efficiency of coaxial systems, both to extend mission time and to enable new mission capabilities. While some parameters of a coaxial system have been explored, such as the rotor-to-rotor distance, the influence of rotor pitch is less understood. This work investigates how adjusting the pitch of the lower rotor relative to that of the upper one impacts the overall efficiency of the system. A methodology based on blade element momentum theory is extended to coaxial rotor systems, and in addition blade-resolved simulations using computational fluid dynamics are performed. A coaxial rotor system for a medium-sized drone with a rotor diameter of 71.12 cm is used for the study. Experiments are performed using a thrust stand to validate the methods. The results show that there exists a peak in total rotor efficiency (thrust-to-power ratio), and that the efficiency can be increased by 2% to 5% by increasing the pitch of the lower rotor. The work contributes to furthering our understanding of coaxial rotor systems, and the results can potentially lead to more efficient drones with increased mission time. Full article

Ice accretion on commercial aircraft operating at high Reynolds numbers has been extensively studied in the literature, but a direct transformation of these results to an Unmanned Aerial Vehicle (UAV) operating at low Reynolds numbers is not straightforward. Changes in Reynolds number have a significant impact on the ice accretion physics. Previously, only a few researchers worked in this area, but it is now gaining more attention due to the increasing applications of UAVs in the modern world. As a result, an attempt is made to review existing scientific knowledge and identify the knowledge gaps in this field of research. Ice accretion can deteriorate the aerodynamic performance, structural integrity, and aircraft stability, necessitating optimal ice mitigation techniques. This paper provides a comprehensive review of ice accretion on fixed-wing UAVs. It includes various methodologies for studying and comprehending the physics of ice accretion on UAVs. The impact of various environmental and geometric factors on ice accretion physics is reviewed, and knowledge gaps are identified. The pros and cons of various ice detection and mitigation techniques developed for UAVs are also discussed. Full article

Visual inspections of aircraft exterior surfaces are required in aircraft maintenance routines for identifying possible defects such as dents, cracks, leaking, broken or missing parts, etc. This process is time-consuming and is also prone to error if performed manually. Therefore, it has become a trend to use mobile robots equipped with visual sensors to perform automated inspections. For such a robotic inspection, a digital model of the aircraft is usually required for planning the robot’s path, but a CAD model of the entire aircraft is usually inaccessible to most maintenance shops. It is very labor-intensive and time-consuming to generate an accurate digital model of an aircraft, or even a large portion of it, because the scanning work still must be performed manually or by a manually controlled robotic system. This paper presents a two-stage approach of automating aircraft scanning with an unmanned aerial vehicle (UAV) or autonomous drone equipped with a red–green–blue and depth (RGB-D) camera for detailed inspection or for reconstructing a digital replica of the aircraft when its original CAD model is unavailable. In the first stage, the UAV–camera system follows a predefined path far from the aircraft surface (for safety) to quickly scan the aircraft and generate a coarse model of the aircraft. Then, an optimal scanning path (much closer to the surface) in the sense of the shortest flying distance for full coverage is computed based on the coarse model. In the second stage, the UAV–camera system follows the computed path to closely inspect the surface for possible defects or scan the surface for generating a dense and precise model of the aircraft. We solved the coverage path planning (CPP) problem for the aircraft inspection or scanning using a Monte Carlo tree search (MCTS) algorithm. We also implemented the max–min ant system (MMAS) strategy to demonstrate the effectiveness of our approach. We carried out a digital experiment and the results showed that our approach can scan 70% of the aircraft surface within one hour, which is much more efficient than manual scanning. Full article

Generating accurate estimates of group sizes or behaviours of cetaceans from boat-based surveys can be challenging because much of their activity occurs below the water surface and observations are distorted by horizontal perspectives. Automated observation using drones is an emerging research tool for animal behavioural investigations. However, drone-based and boat-based survey methods have not been quantitatively compared for small, highly mobile cetaceans, such as Delphinidae. Here, we conduct paired concurrent boat-based and drone-based surveys, measuring the number of individuals in 21 groups and the behaviour within 13 groups of bottlenose dolphin (Tursiops truncatus). We additionally assessed the ability to detect behaviour events by the drone that would not be detectable from the boat. Drone-derived abundance counts detected 26.4% more individuals per group on average than boat-based counts (p = 0.003). Drone-based behaviour observations detected travelling 55.2% more frequently and association in subgroups 80.4% more frequently than boat-based observations (p < 0.001 for both comparisons). Whereas foraging was recorded 58.3% and resting 15.1% less frequently by the drone than by boat-based surveys, respectively (p = 0.014 and 0.024). A considerable number of underwater behaviours ranging from individual play activities to intra- and inter-species interactions (including those with humans) were observed from the drone that could not be detected from the boat. Our findings demonstrate that drone surveys can improve the accuracy of population counts and behavioural data for small cetaceans and the magnitude of the discrepancies between the two methods highlights the need for cautious interpretation of studies that have relied on boat-derived data. Full article

Accurate retrieval of grassland traits is important to support management of pasture production and phenotyping studies. In general, conventional methods used to measure forage yield and quality rely on costly destructive sampling and laboratory analysis, which is often not viable in practical applications. Optical imaging systems carried as payload in Unmanned Aerial Vehicles (UAVs) platforms have increasingly been proposed as alternative non-destructive solutions for crop characterization and monitoring. The vegetation spectral response in the visible and near-infrared wavelengths provides information on many aspects of its composition and structure. Combining spectral measurements and multivariate modelling approaches it is possible to represent the often complex relationship between canopy reflectance and specific plant traits. However, empirical models are limited and strictly represent characteristics of the observations used during model training, therefore having low generalization potential. A method to mitigate this issue consists of adding informative samples from the target domain (i.e., new observations) to the training dataset. This approach searches for a compromise between representing the variability in new data and selecting only a minimal number of additional samples for calibration transfer. In this study, a method to actively choose new training samples based on their spectral diversity and prediction uncertainty was implemented and tested using a multi-annual dataset. Accurate predictions were obtained using hyperspectral imagery and linear multivariate models (Partial Least Squares Regression—PLSR) for grassland dry matter (DM; $R^2$ = 0.92, $RMSE$ = 3.25 dt ha${}^{-1}$), nitrogen (N) content in % of DM ($R^2$ = 0.58, $RMSE$ = 0.27%) and N-uptake ($R^2$ = 0.91, $RMSE$ = 6.50 kg ha${}^{-1}$). In addition, the number of samples from the target dates added to the training dataset could be reduced by up to 77% and 74% for DM and N-related traits, respectively, after model transfer. Despite this reduction, $RMSE$ values for optimal transfer sets (identified after validation and used as benchmark) were only 20–30% lower than those values obtained after model transfer based on prediction uncertainty reduction, indicating that loss of accuracy was relatively small. These results demonstrate that considerably simple approaches based on UAV hyperspectral data can be applied in preliminary grassland monitoring frameworks, even with limited datasets. Full article

Drones have become popular with the general public for viewing and filming marine life. One amateur enthusiast platform, DroneSharkApp, films marine life in the waters off Sydney, Australia year-round and posts their observations on social media. The drone observations include the behaviours of a variety of coastal marine wildlife species, including sharks, rays, fur seals, dolphins and fish, as well as migratory species such as migrating humpback whales. Given the extensive effort and multiple recordings of the presence, behaviour and interactions of various species with humans provided by DroneSharkApp, we explored its utility for providing biologically meaningful observations of marine wildlife. Using social media posts from the DroneSharkApp Instagram page, a total of 678 wildlife videos were assessed from 432 days of observation collected by a single observer. This included 94 feeding behaviours or events for fur seals (n = 58) and dolphins (n = 33), two feeding events for white sharks and one feeding event for a humpback whale. DroneSharkApp documented 101 interactions with sharks and humans (swimmers and surfers), demonstrating the frequent, mainly innocuous human–shark overlap off some of Australia’s busiest beaches. Finally, DroneSharkApp provided multiple observations of humpback and dwarf minke whales with calves travelling north, indicating calving occurring well south of traditional northern Queensland breeding waters. Collaboration between scientists and citizen scientists such as those involved with DroneSharkApp can greatly and quantitatively increase the biological understanding of marine wildlife data. Full article

In recent years, the drone market has had a significant expansion, with applications in various fields (surveillance, rescue operations, intelligent logistics, environmental monitoring, precision agriculture, inspection and measuring in the construction industry). Given their increasing use, the issues related to safety, security and privacy must be taken into consideration. Accordingly, the development of new concepts for countermeasures systems, able to identify and neutralize a single (or multiples) malicious drone(s) (i.e., classified as a threat), has become of primary importance. For this purpose, the paper evaluates the concept of a multiplatform counter-UAS system (CUS), based mainly on a team of mini drones acting as a cooperative defensive system. In order to provide the basis for implementing such a system, we present a review of the available technologies for sensing, mitigation and command and control systems that generally comprise a CUS, focusing on their applicability and suitability in the case of mini drones. Full article

Uncrewed Aerial Vehicles (UAV) constitute a rapidly evolving technology field that is becoming more accessible and capable of supplementing, expanding, and even replacing some traditionally manual bridge inspections. Given the classification of the bridge inspection types as initial, routine, in-depth, damage, special, and fracture critical members, specific UAV mission requirements can be developed, and their suitability for UAV application examined. Results of a review of 23 applications of UAVs in bridge inspections indicate that mission sensor and payload needs dictate the UAV configuration and size, resulting in quadcopter configurations being most suitable for visual camera inspections (43% of visual inspections use quadcopters), and hexa- and octocopter configurations being more suitable for higher payload hyperspectral, multispectral, and Light Detection and Ranging (LiDAR) inspections (13%). In addition, the number of motors and size of the aircraft are the primary drivers in the cost of the vehicle. 75% of vehicles rely on GPS for navigation, and none of them are capable of contact inspections. Factors that limit the use of UAVs in bridge inspections include the UAV endurance, the capability of navigation in GPS deprived environments, the stability in confined spaces in close proximity to structural elements, and the cost. Current research trends in UAV technologies address some of these limitations, such as obstacle detection and avoidance methods, autonomous flight path planning and optimization, and UAV hardware optimization for specific mission requirements. Full article

The use of unmanned aerial vehicles (UAVs, also known as drones or RPA) in archaeology has expanded significantly over the last twenty years. Improvements in terms of the reliability, size, and manageability of these aircraft have been largely complemented by the high resolution and spectral bands provided by the sensors of the different cameras that can be incorporated into their structure. If we add to this the functionalities and improvements that photogrammetry programs have been experiencing in recent years, we can conclude that there has been a qualitative leap in the possibilities, not only of geometric documentation and in the presentation of the archaeological data, but in the incorporation of non-intrusive high-resolution analytics. The work that we present here gives a sample of the possibilities of both geometric documentation, creation of 3D models, their subsequent printing with different materials, and techniques to finally show a series of analytics from images with NGB (Nir + Green + Blue), Red Edge, and Thermographic cameras applied to various archaeological sites in which our team has been working since 2013, such as Clunia (Peñalba de Castro, Burgos), Puig Rom (Roses), Vilanera (L’Escala, Girona), and Cosa (Ansedonia, Italy). All of them correspond to different chronological periods as well as to varied geographical and morphological environments, which will lead us to propose the search for adequate solutions for each of the environments. In the discussions, we will propose the lines of research to be followed in a project of these characteristics, as well as some results that can already be viewed. Full article

Unmanned aerial system (UAS) traffic management (UTM) requires each UAS to communicate with each other and to other stakeholders involved in the operation. In practice, there are two types of wireless communication systems established in the UAS community: automatic dependent surveillance broadcast (ADS-B) and remote identification (Remote ID). In between these two systems, there is ADS-B-like communication which leverages using other types of communications available in the market for the purpose of UTM. This review aims to provide an insight into those three systems, based on the published standard documents and latest research development. It also suggests how to construct a feasible communication architecture. The integrative approach is used in this literature review. The review categorization includes definition, data format, technology used, and research applications, and any remaining issues are discussed. The similarities and differences of each system are elaborated, covering practical findings. In addition, the SWOT analysis is conducted based on the findings. Lastly, multi-channel communication for UTM is proposed as a feasible solution in the UTM operation. Full article

Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m²), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m² and 2300 m²) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and the heights measured in the LiDAR point clouds. We performed a comparison of the variables number of trees, basal area, and volume per hectare. The variables by scenarios were submitted to analysis of variance to verify if the averages are considered different or equivalent. The RMSE (%) were calculated to explain the deviation between the measured volume (filed) and estimated volume (LiDAR) values of these variables. Additionally, we calculated rRMSE, Standard error, AIC, R², Bias, and residual charts. The basal area values ranged from 7.40 m² ha⁻¹ (C1380) to 8.14 m² ha⁻¹ 281 (C2300), about −5.9% less than the real value (8.65 m² ha⁻¹). The C2300 scenario was the only one whose confidence interval (CI) limits included the basal area real. For the total stand volume, the ITD scenario was the one that presented the closer values (689.29 m³) to the real total value (683.88 m³) with the real value positioned in the CI. Our findings indicate that for the stand conditions under study, the SFI approach (C2300) that considers an area of 2300 m² is adequate to generate estimates at the same level as the ITD approach. Thus, our study should be able to assist in the selection of an optimal plot size to generate estimates with minimized errors and gain in processing time. Full article

This work presents an algorithm for an Aerial Sensor Network (ASN) composed of fixed-wing Unmanned Aerial Vehicles (UAVs) that performs surveillance and detects the early signs of a wildfire in a given territory. The main goal is to cover a given area while prioritizing areas of higher fire hazard risk. The proposed algorithm is scalable to any number of aircraft and can use any kind of fire hazard risk map as long as it contains bounded and nonnegative values. Two different dynamical models associated with the movement of fixed-wing UAVs are proposed, tested, and compared through simulations. Lastly, we propose a workflow to size the ASN in order to maximize the probability of detection of wildfires for a particular risk profile. Full article

During the last few years, a wide variety of Internet of Drones (IoD) applications have emerged with numerous heterogeneous aerial and ground network elements interconnected and equipped with advanced sensors, computation resources, and communication units. The evolution of IoD networks presupposes the mitigation of several security and privacy threats. Thus, robust authentication protocols should be implemented in order to attain secure operation within the IoD. However, owing to the inherent features of the IoD and the limitations of Unmanned Aerial Vehicles (UAVs) in terms of energy, computational, and memory resources, designing efficient and lightweight authentication solutions is a non-trivial and complicated process. Recently, the development of authentication mechanisms for the IoD has received unprecedented attention. In this paper, up-to-date research studies on authentication mechanisms for IoD networks are presented. To this end, the adoption of conventional technologies and methods, such as the widely used hash functions, Public Key Infrastructure (PKI), and Elliptic-Curve Cryptography (ECC), is discussed along with emerging technologies, including Mobile Edge Computing (MEC), Machine Learning (ML), and Blockchain. Additionally, this paper provides a review of effective hardware-based solutions for the identification and authentication of network nodes within the IoD that are based on Trusted Platform Modules (TPMs), Hardware Security Modules (HSMs), and Physically Unclonable Functions (PUFs). Finally, future directions in these relevant research topics are given, stimulating further work. Full article

The scope and scientific purpose of this paper focuses on multiscale (aerial and terrestrial) photogrammetry as a support to investigations and interpretations in a multi-component archaeological site located in the Argentinian Cordillera (Calchaquí, Salta), known as Tacuil. Due to its scarce accessibility, as well as long-term problems associated with the interpretation of the visibility of this type of settlement, the use of aerial surveying was combined with the reconstruction of structures and complex soil morphologies by resorting to modern photogrammetric approaches (3D models and orthophotos). This dataset was complemented by a terrestrial survey to obtain extremely high resolution and detailed representations of archaeological features that were integrated in a GIS database. The outcome of photogrammetric surveying was fundamental in supporting the debate on the functionality of the site and his integration in a complex, socially constructed, ancient landscape. Finally, the present paper introduces the first complete map of Tacuil. Full article

This paper proposes a novel robotic animal herding system based on a network of autonomous barking drones. The objective of such a system is to replace traditional herding methods (e.g., dogs) so that a large number (e.g., thousands) of farm animals such as sheep can be quickly collected from a sparse status and then driven to a designated location (e.g., a sheepfold). In this paper, we particularly focus on the motion control of the barking drones. To this end, a computationally efficient sliding mode based control algorithm is developed, which navigates the drones to track the moving boundary of the animals’ footprint and enables the drones to avoid collisions with others. Extensive computer simulations, where the dynamics of the animals follow Reynolds’ rules, show the effectiveness of the proposed approach. Full article

The continuous development of technical innovations provides the opportunity to create new economic markets and a wealth of new services. However, these innovations sometimes raise concerns, notably in terms of societal, safety, and environmental impacts. This is the case for services related to the operation of unmanned aerial vehicles (UAV), which are emerging rapidly. Unmanned aerial vehicles, also called drones, date back to the first third of the twentieth century in aviation industry, when they were mostly used for military purposes. Nowadays, drones of various types and sizes are used for many purposes, such as precision agriculture, search and rescue missions, aerial photography, shipping and delivery, etc. Starting to operate in areas with low population density, drones are now looking for business in urban and suburban areas, in what is called urban air mobility (UAM). However, this rapid growth of the drone industry creates psychological fear of the unknown in some parts of society. Reducing this fear will play an important role in public acceptance of drone operations in urban areas. This paper presents the main concerns of society with regard to drone operations, as already captured in some public surveys, and proposes a list of mitigation measures to reduce these concerns. The proposed list is then analyzed, and its applicability to individual, urban, very large demonstration flights is explained, using the feedback from the CORUS-XUAM project. CORUS-XUAM will organize a set of very large drone flight demonstrations across seven European countries to investigate how to safely integrate drone operations into airspace with the support of the U-space. Full article

With the increased availability of low-cost, off-the-shelf drone platforms, drone data become easy to capture and are now a key component of environmental assessments and monitoring. Once the data are collected, there are many structure-from-motion (SfM) photogrammetry software options available to pre-process the data into digital elevation models (DEMs) and orthomosaics for further environmental analysis. However, not all software packages are created equal, nor are their outputs. Here, we evaluated the workflows and output products of four desktop SfM packages (AgiSoft Metashape, Correlator3D, Pix4Dmapper, WebODM), across five input datasets representing various ecosystems. We considered the processing times, output file characteristics, colour representation of orthomosaics, geographic shift, visual artefacts, and digital surface model (DSM) elevation values. No single software package was determined the “winner” across all metrics, but we hope our results help others demystify the differences between the options, allowing users to make an informed decision about which software and parameters to select for their specific application. Our comparisons highlight some of the challenges that may arise when comparing datasets that have been processed using different parameters and different software packages, thus demonstrating a need to provide metadata associated with processing workflows. Full article

Unmanned Aerial Vehicles have advanced rapidly in the last two decades with the advances in microelectromechanical systems (MEMS) technology. It is crucial, however, to design better power supply technologies. In the last decade, lithium polymer and lithium-ion batteries have mainly been used to power multirotor UAVs. Even though batteries have been improved and are constantly being improved, they provide fairly low energy density, which limits multirotors’ UAV flight endurance. This problem is addressed and is being partially solved by using docking stations which provide an aircraft to land safely, charge (or change) the batteries and to take-off as well as being safely stored. This paper focuses on the work carried out in the last decade. Different docking stations are presented with a focus on their movement abilities. Rapid advances in computer vision systems gave birth to precise landing systems. These algorithms are the main reason that docking stations became a viable solution. The authors concluded that the docking station solution to short ranges is a viable option, and numerous extensive studies have been carried out that offer different solutions, but only some types, mainly fixed stations with storage systems, have been implemented and are being used today. This can be seen from the commercially available list of docking stations at the end of this paper. Nevertheless, it is important to be aware of the technologies being developed and implemented, which can offer solutions to a vast number of different problems. Full article

Of the recent epidemics, the impact of the COVID-19 pandemic has been particularly severe, not only putting our health at risk, but also negatively affecting our daily lives. As there are no developed algorithms for the use of drones in epidemiological situations, it is ideal to analyze the experience gained on drones so far and outline the effective methods for future good practice. The author relies on a method of analyzing widely available open information, such as images and videos available on the Internet, reports from drone users, announcements by drone manufacturers and the contents of newspaper articles. Furthermore, the author has relied on the results of the relevant literature, as well as previous experience as a drone user and fire commander. The study reveals numerous possibilities associated with drone usage in epidemic related situations, but previous applications are based on previous experience gained during a non-epidemic situation, without developed algorithms. Applications can be divided into different types of groups: drones can collect data for management and provide information to the public, perform general or special logistical tasks to support health care and disinfect to reduce the risk of spreading the epidemic. Full article

As there has been an advancement in avionic systems in recent years, the enactment of unmanned aerial vehicles (UAV) has upgraded. As compared to a single UAV system, multiple UAV systems can perform operations more inexpensively and efficiently. As a result, new technologies between user/control station and UAVs have been developed. FANET (Flying Ad-Hoc Network) is a subset of the MANET (Mobile Ad-Hoc Network) that includes UAVs. UAVs, simply called drones, are used for collecting sensitive data in real time. The security and privacy of these data are of priority importance. Therefore, to overcome the privacy and security threats problem and to make communication between the UAV and the user effective, a competent anonymous mutual authentication scheme is proposed in this work. There are several methodologies addressed in this work such as anonymous batch authentication in FANET which helps to authenticate a large group of drones at the same time, thus reducing the computational overhead. In addition, the integrity preservation technique helps to avoid message alteration during transmission. Moreover, the security investigation section discusses the resistance of the proposed work against different types of possible attacks. Finally, the proposed work is related to the prevailing schemes in terms of communication and computational cost and proves to be more efficient. Full article

This paper aims to provide an overview of the capabilities of unmanned systems to monitor and manage aquaculture farms that support precision aquaculture using the Internet of Things. The locations of aquaculture farms are diverse, which is a big challenge on accessibility. For offshore fish cages, there is a difficulty and risk in the continuous monitoring considering the presence of waves, water currents, and other underwater environmental factors. Aquaculture farm management and surveillance operations require collecting data on water quality, water pollutants, water temperature, fish behavior, and current/wave velocity, which requires tremendous labor cost, and effort. Unmanned vehicle technologies provide greater efficiency and accuracy to execute these functions. They are even capable of cage detection and illegal fishing surveillance when equipped with sensors and other technologies. Additionally, to provide a more large-scale scope, this document explores the capacity of unmanned vehicles as a communication gateway to facilitate offshore cages equipped with robust, low-cost sensors capable of underwater and in-air wireless connectivity. The capabilities of existing commercial systems, the Internet of Things, and artificial intelligence combined with drones are also presented to provide a precise aquaculture framework. Full article

Unmanned aerial vehicles (UAVs) have become a research hotspot in the field of magnetic exploration because of their unique advantages, e.g., low cost, high safety, and easy to operate. However, the lack of effective data processing and interpretation method limits their further deployment. In view of this situation, a complete workflow of UAV magnetic data processing and interpretation is proposed in this paper, which can be divided into two steps: (1) the improved variational mode decomposition (VMD) is applied to the original data to improve its signal-to-noise ratio as much as possible, and the decomposition modes number K is determined adaptively according to the mode characteristics; (2) the parameters of target position and magnetic moment are obtained by Euler deconvolution first, and then used as the prior information of the Levenberg–Marquardt (LM) algorithm to further improve its accuracy. Experiments are carried out to verify the effectiveness of the proposed method. Results show that the proposed method can significantly improve the quality of the original data; by combining the Euler deconvolution and LM algorithm, the horizontal positioning error can be reduced from 15.31 cm to 4.05 cm, and the depth estimation error can be reduced from 16.2 cm to 5.4 cm. Moreover, the proposed method can be used not only for the detection and location of near-surface targets, but also for the follow-up work, such as the clearance of targets (e.g., the unexploded ordnance). Full article

Telecommunications among unmanned aerial vehicles (UAVs) have emerged recently due to rapid improvements in wireless technology, low-cost equipment, advancement in networking communication techniques, and demand from various industries that seek to leverage aerial data to improve their business and operations. As such, UAVs have started to become extremely prevalent for a variety of civilian, commercial, and military uses over the past few years. UAVs form a flying ad hoc network (FANET) as they communicate and collaborate wirelessly. FANETs may be utilized to quickly complete complex operations. FANETs are frequently deployed in three dimensions, with a mobility model determined by the work they are to do, and hence differ between vehicular ad hoc networks (VANETs) and mobile ad hoc networks (MANETs) in terms of features and attributes. Furthermore, different flight constraints and the high dynamic topology of FANETs make the design of routing protocols difficult. This paper presents a comprehensive review covering the UAV network, the several communication links, the routing protocols, the mobility models, the important research issues, and simulation software dedicated to FANETs. A topology-based routing protocol specialized to FANETs is discussed in-depth, with detailed categorization, descriptions, and qualitatively compared analyses. In addition, the paper demonstrates open research topics and future challenge issues that need to be resolved by the researchers, before UAVs communications are expected to become a reality and practical in the industry. Full article

At present, the great progress made by the Internet of Things ($IoT$) has led to the emergence of the Internet of Drones ($IoD$). $IoD$ is an extension of the $IoT$, which is used to control and manipulate drones entering the flight area. Now, the fifth-generation mobile communication technology ($5G$) has been introduced into the $IoD$; it can transmit ultra-high-definition data, make the drones respond to ground commands faster and provide more secure data transmission in the $IoD$. However, because the drones communicate on the public channel, they are vulnerable to security attacks; furthermore, drones can be easily captured by attackers. Therefore, to solve the security problem of the $IoD$, Hussain et al. recently proposed a three-party authentication protocol in an $IoD$ environment. The protocol is applied to the supervision of smart cities and collects real-time data about the smart city through drones. However, we find that the protocol is vulnerable to drone capture attacks, privileged insider attacks and session key disclosure attacks. Based on the security of the above protocol, we designed an improved protocol. Through informal analysis, we proved that the protocol could resist known security attacks. In addition, we used the real-oracle random model and ProVerif tool to prove the security and effectiveness of the protocol. Finally, through comparison, we conclude that the protocol is secure compared with recent protocols. Full article

Here, we developed a method for detecting cyber security attacks aimed at spoofing the Global Positioning System (GPS) signal of an Unmanned Aerial Vehicle (UAV). Most methods for detecting UAV anomalies indicative of an attack use machine learning or other such methods that compare normal behavior with abnormal behavior. Such approaches require large amounts of data and significant “training” time to prepare and implement the system. Instead, we consider a new approach based on other mathematical methods for detecting UAV anomalies without the need to first collect a large amount of data and describe normal behavior patterns. Doing so can simplify the process of creating an anomaly detection system, which can further facilitate easier implementation of intrusion detection systems in UAVs. This article presents issues related to ensuring the information security of UAVs. Development of the GPS spoofing detection method for UAVs is then described, based on a preliminary study that made it possible to form a mathematical apparatus for solving the problem. We then explain the necessary analysis of parameters and methods of data normalization, and the analysis of the Kullback—Leibler divergence measure needed to detect anomalies in UAV systems. Full article

Biodiversity monitoring is crucial in tackling defaunation in the Anthropocene, particularly in tropical ecosystems. However, field surveys are often limited by habitat complexity, logistical constraints, financing and detectability. Hence, leveraging drones technology for species monitoring is required to overcome the caveats of conventional surveys. We investigated prospective methods for wildlife monitoring using drones in four ecosystems. We surveyed waterbird populations in Pulau Rambut, a community of ungulates in Baluran and endemic non-human primates in Gunung Halimun-Salak, Indonesia in 2021 using a DJI Matrice 300 RTK and DJI Mavic 2 Enterprise Dual with additional thermal sensors. We then, consecutively, implemented two survey methods at three sites to compare the efficacy of drones against traditional ground survey methods for each species. The results show that drone surveys provide advantages over ground surveys, including precise size estimation, less disturbance and broader area coverage. Moreover, heat signatures helped to detect species which were not easily spotted in the radiometric imagery, while the detailed radiometric imagery allowed for species identification. Our research also demonstrates that machine learning approaches show a relatively high performance in species detection. Our approaches prove promising for wildlife surveys using drones in different ecosystems in tropical forests. Full article

Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of damages. Numerous computer vision-based approaches have been applied to address the limitations of crack detection but they have their limitations that can be overcome by using various hybrid approaches based on artificial intelligence (AI) and machine learning (ML) techniques. The convolutional neural networks (CNNs), an application of the deep learning (DL) method, display remarkable potential for automatically detecting image features such as damages and are less sensitive to image noise. A modified deep hierarchical CNN architecture has been used in this study for crack detection and damage assessment in civil infrastructures. The proposed architecture is based on 16 convolution layers and a cycle generative adversarial network (CycleGAN). For this study, the crack images were collected using UAVs and open-source images of mid to high rise buildings (five stories and above) constructed during 2000 in Sydney, Australia. Conventionally, a CNN network only utilizes the last layer of convolution. However, our proposed network is based on the utility of multiple layers. Another important component of the proposed CNN architecture is the application of guided filtering (GF) and conditional random fields (CRFs) to refine the predicted outputs to get reliable results. Benchmarking data (600 images) of Sydney-based buildings damages was used to test the proposed architecture. The proposed deep hierarchical CNN architecture produced superior performance when evaluated using five methods: GF method, Baseline (BN) method, Deep-Crack BN, Deep-Crack GF, and SegNet. Overall, the GF method outperformed all other methods as indicated by the global accuracy (0.990), class average accuracy (0.939), mean intersection of the union overall classes (IoU) (0.879), precision (0.838), recall (0.879), and F-score (0.8581) values. Overall, the proposed CNN architecture provides the advantages of reduced noise, highly integrated supervision of features, adequate learning, and aggregation of both multi-scale and multilevel features during the training procedure along with the refinement of the overall output predictions. Full article

In recent years, the area of Unmanned Aerial Vehicles (UAVs) has seen rapid growth. There has been a trend to build and produce UAVs that can carry out planetary exploration throughout the past decade. The technology of UAVs has tremendous potential to support various successful space mission solutions. In general, different techniques for observing space objects are available, such as telescopes, probes, and flying spacecraft, orbiters, landers, and rovers. However, a detailed analysis has been carried out due to the benefits of UAVs relative to other planetary exploration techniques. The deployment of UAVs to other solar bodies has been considered by numerous space agencies worldwide, including NASA. This article contributes to investigating the types of UAVs that have been considered for various planetary explorations. This study further investigates the behaviour of UAV prototypes on Mars’ surface in particular. It has been discovered that a prototype UAV flight on Mars has a higher chance of success. In this research, a prototype UAV has been successfully simulated to fly on Mars’ surface. This article discusses the opportunities, challenges, and future scope of deploying UAVs on Mars. Full article

Drones are now widely used to study wildlife, but their application in the study of bioacoustics is limited. Drones can be used to collect data on bird vocalizations, but an ongoing concern is that noise from drones could change bird vocalization behavior. To test for behavioral impact, we conducted an experiment using 30 sound localization arrays to track the song output of 7 songbird species before, during, and after a 3 min flight of a small quadcopter drone hovering 48 m above ground level. We analyzed 8303 song bouts, of which 2285, from 184 individual birds were within 50 m of the array centers. We used linear mixed effect models to assess whether patterns in bird song output could be attributed to the drone’s presence. We found no evidence of any effect of the drone on five species: American Robin Turdus migratorius, Common Yellowthroat Geothlypis trichas, Field Sparrow Spizella pusilla, Song Sparrow Melospiza melodia, and Indigo Bunting Passerina cyanea. However, we found a substantial decrease in Yellow Warbler Setophaga petechia song detections during the 3 min drone hover; there was an 81% drop in detections in the third minute (Wald test, p < 0.001) compared with before the drone’s introduction. By contrast, the number of singing Northern Cardinal Cardinalis cardinalis increased when the drone was overhead and remained almost five-fold higher for 4 min after the drone departed (p < 0.001). Further, we found an increase in cardinal contact/alarm calls when the drone was overhead, with the elevated calling rate lasting for 2 min after the drone departed (p < 0.001). Our study suggests that the responses of songbirds to drones may be species-specific, an important consideration when proposing the use of drones in avian studies. We note that recent advances in drone technology have resulted in much quieter drones, which makes us hopeful that the impact that we detected could be greatly reduced. Full article

The use of drones in mining and geological exploration is under rapid development, especially in the field of magnetic field prospection. In part, this is related to the advantages presented for over ground surveys, allowing for high-density data acquisition with low loss of resolution, while being particularly useful in scenarios where vegetation, topography, and access are limiting factors. This work analyzes results of a drone magnetic survey acquired across the old mines of Don Jacobo, where Copper-Cobalt-Nickel stratabound mineralizations were exploited in the Estancias mountain range of the Betic Cordillera, Spain. The survey carried out used a vapor magnetometer installed on a Matrice 600 Pro Hexacopter. Twenty-four parallel survey lines were flown with a speed of 5 m/s, orthogonal to the regional strike of the geological structure, and mineralization with 50 m line separation and 20 m flight height over the ground was studied. The interpretation of the magnetic data allows us to reveal and model two high magnetic susceptibility bodies with residual magnetization, close to the old mines and surface mineral shows. These bodies could be related to potential unexploited mineralized areas whose formation may be related to a normal fault placed to the south of the survey area. Our geophysical survey provides essential data to improve the geological and mining potential of the area, allowing to design future research activities. Full article

Unmanned aerial vehicles (UAVs) are becoming integrated into a wide range of modern IoT applications. The growing number of networked IoT devices generates a large amount of data. However, processing and memorizing this massive volume of data at local nodes have been deemed critical challenges, especially when using artificial intelligence (AI) systems to extract and exploit valuable information. In this context, mobile edge computing (MEC) has emerged as a way to bring cloud computing (CC) processes within reach of users, to address computation-intensive offloading and latency issues. This paper provides a comprehensive review of the most relevant research works related to UAV technology applications in terms of enabled or assisted MEC architectures. It details the utility of UAV-enabled MEC architecture regarding emerging IoT applications and the role of both deep learning (DL) and machine learning (ML) in meeting various limitations related to latency, task offloading, energy demand, and security. Furthermore, throughout this article, the reader gains an insight into the future of UAV-enabled MEC, the advantages and the critical challenges to be tackled when using AI. Full article