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Assessing the stability of loess landslides under seismic action scientifically and reasonably is crucial for reducing earthquake disasters and ensuring the safety of people's lives and property. To study this issue, we chose the southern part of Fushan County, Shanxi Province as the research area, where loess slopes develop and earthquake impacts are strong. Based on the high-precision DEM images of the study area obtained from the unmanned aerial vehicle photogrammetry, we selected 32 representative profiles by sampling according to our principles, and used the simplified Bishop method popular in engineering for modeling and calculation to evaluate the stability of the entire slopes in the area. The calculation results were then statistically analyzed, and the stability and impact range of the slopes were evaluated by means of the graphical form. The results show that in the study area the value of slope safety coefficient (Fs) is mostly within a range of 0.7–1.35 and the value of the avoidance distance is mostly within a range of 5–20 m under a seismic force of 0.20 g, and the slope safety coefficient is mainly determined by the slope angle, and the avoidance distance is mainly determined by the slope height, which is in line with the conclusions of previous research conclusions. In the paper we make a useful attempt for quantitative evaluation of regional seismic loess landslides, and the fact proves that the approach is feasible and efficient, and it can provide quantitative data for major construction projects to avoid landslide disasters. [ABSTRACT FROM AUTHOR]

Prescribed fire is an important tool for wildfire management and land management. Simulation of prescribed fires holds great potential in supporting planning of prescribed burn events. This paper presents a simulation-based study of a prescribed fire using data from Unmanned Aircraft System (UAS)-based sensing. A systematic approach for modelling and simulating prescribed fires with dynamic ignitions is developed. The developed approach is applied to a real prescribed fire where a UAS was used to monitor and collect data about the fire. The dynamic ignition process from multiple fire setting teams is specified, and simulation results are compared to real measurement data from UAS-based sensing. The results demonstrate the effectiveness of the developed modelling approach as well as the utility of using UAS-based fire measurements for prescribed fire simulations. [ABSTRACT FROM AUTHOR]

The article deals with the issue of ensuring the adoption of an informed decision on the use of unmanned aircraft systems (UAS) when delivering small-sized cargo to hard-to-reach and especially dangerous emergency zones. In particular, the element of environment such as the estimation of delivery duration, which is random has been considered.It is shown that it is advisable to consider the duration of delivery of small-sized cargoes using UAS subordinate to the Erlang distribution. There are also presented practical recommendations for the implementation of assessment of delivery duration.

Unmanned aerial systems (UASs) offer a less invasive solution for accessing remote areas and sites, making them valuable in Architecture, Engineering, Construction, and Operation (AECO). Their ease of use, ability to reach previously inaccessible areas, and sensor integration provide new project perspectives. Augmented Reality (AR), which allows for the real-time insertion of virtual elements into physical spaces, is also being explored in the AECO industry. Recognizing the potential of these technologies, this research aims to integrate them for on-site building model visualization. This article presents the development of resources to visualize building design implementation in AR, which is supported by UASs through georeferencing. The system development process included establishing the software architecture, creating interface prototypes, and constructing the model. It was possible to visualize the building model in AR within the real world; however, limitations were identified regarding the UAS used and its Application Programming Interface, which affected the aircraft's programmed trajectory. The contribution of this paper lies in exploring the utilization of georeferenced AR enabled by UAS for visualizing architectural designs on site, detailing the steps and strategies employed to achieve this, highlighting the limitations of the chosen approach, and proposing potential solutions to the issues identified in the research. [ABSTRACT FROM AUTHOR]

This work provides an overview of the growing use of drones in both civilian and military domains, highlighting the associated risks to public safety and privacy. It discusses the development of counter-drone systems to mitigate these risks, focusing on detection, tracking, classification, and neutralization technologies. Various sensors such as passive radar, acoustic sensors, electro-optical and infrared sensors, radiofrequency analyzers, active radar, and LiDAR are detailed, along with algorithms for data fusion. Mitigation strategies include soft kill methods like spoofing and jamming, as well as hard kill approaches such as projectiles and directed energy weapons. [ABSTRACT FROM AUTHOR]

Rockfall phenomena are considered highly dangerous due to their rapid evolution and difficult prediction without applying preventive monitoring and mitigation actions. This research investigates a hazardous site in the Municipality of Vecchiano (Province of Pisa, Italy), characterized by vertical rock walls prone to instability due to heavy fracturing and karst phenomena. The presence of anthropical structures and a public road at the bottom of the slopes increases the vulnerability of the site and the site's risk. To create a comprehensive geological model of the area, Unmanned Aircraft System (UAS) photogrammetric surveys were conducted to create a 3D model useful in photointerpretation. In accessible and safe areas for personnel, engineering–geological surveys were carried out to characterize the rock mass and to define the portion of rock walls to be monitored. Results from nine multitemporal Robotic Total Station (RTS) measurement campaigns show that no monitoring prisms recorded significant displacement trends, both on the horizontal and vertical plane and in differential slope distance. Additionally, satellite Persistent Scatterer Interferometry (PSI) analysis indicates that the slopes were stable over the two years of study. The integration of these analysis techniques has proven to be an efficient solution for assessing slope stability in this specific rockfall-prone area. [ABSTRACT FROM AUTHOR]

A software tool was developed to extract the positions of features identified in individual undistorted images acquired by unmanned aircraft systems (UAS), to support operations to locate and control invasive organisms in sensitive natural habitats. The tool determines a feature's position based on selected pixels and metadata in the image. Accuracy tests were performed using test imagery obtained from different camera altitudes (30, 40 and 50 m above ground level) and orientations (0°, 15° and 30° from the vertical) for direct geolocation of features of interest. As an additional case study, the tool was integrated with a deep neural network (DNN) for simultaneous detection and geolocation of the invasive tree Miconia calvescens in natural landscapes on Hawaii Island. For vertical camera orientations, median horizontal position errors were below 5 m. Images from oblique camera views resulted in larger median errors, approaching 10 m. While numerous approaches have been used to improve locational accuracy of objects identified in aerial imagery, the presented tool can be applied directly to individual images collected with commercial off-the-shelf UAS and flight planning software, making it accessible for applications in natural resource management. Furthermore, the tool does not require accurate ground control points, so it is especially suitable for locating invasive plants in dynamic natural landscapes with extensive forest canopy. [ABSTRACT FROM AUTHOR]

The estimation of pre-harvest fruit quality and maturity is essential for growers to determine the harvest timing, storage requirements and profitability of the crop yield. In-field fruit maturity indicators are highly variable and require high spatiotemporal resolution data, which can be obtained from contemporary precision agriculture systems. Such systems exploit various state-of-the-art sensors, increasingly relying on spectrometry and imaging techniques in association with advanced Artificial Intelligence (AI) and, in particular, Machine Learning (ML) algorithms. This article presents a critical review of precision agriculture techniques for fruit maturity estimation, with a focus on destructive and non-destructive measurement approaches, and the applications of ML in the domain. A critical analysis of the advantages and disadvantages of different techniques is conducted by surveying recent articles on non-destructive methods to discern trends in performance and applicability. Advanced data-fusion methods for combining information from multiple non-destructive sensors are increasingly being used to develop more accurate representations of fruit maturity for the entire field. This is achieved by incorporating AI algorithms, such as support vector machines, k-nearest neighbour, neural networks, and clustering. Based on an extensive survey of recently published research, the review also identifies the most effective fruit maturity indices, namely: sugar content, acidity and firmness. The review concludes by highlighting the outstanding technical challenges and identifies the most promising areas for future research. Hence, this research has the potential to provide a valuable resource for the growers, allowing them to familiarize themselves with contemporary Smart Agricultural methodologies currently in use. These practices can be gradually incorporated from their perspective, taking into account the availability of non-destructive techniques and the use of efficient fruit maturity indices. [ABSTRACT FROM AUTHOR]

Assessing the stability of loess landslides under seismic action scientifically and reasonably is crucial for reducing earthquake disasters and ensuring the safety of people’s lives and property. To study this issue, we chose the southern part of Fushan County, Shanxi Province as the research area, where loess slopes develop and earthquake impacts are strong. Based on the high-precision DEM images of the study area obtained from the unmanned aerial vehicle photogrammetry, we selected 32 representative profiles by sampling according to our principles, and used the simplified Bishop method popular in engineering for modeling and calculation to evaluate the stability of the entire slopes in the area. The calculation results were then statistically analyzed, and the stability and impact range of the slopes were evaluated by means of the graphical form. The results show that in the study area the value of slope safety coefficient (Fs) is mostly within a range of 0.7–1.35 and the value of the avoidance distance is mostly within a range of 5–20 m under a seismic force of 0.20 g, and the slope safety coefficient is mainly determined by the slope angle, and the avoidance distance is mainly determined by the slope height, which is in line with the conclusions of previous research conclusions. In the paper we make a useful attempt for quantitative evaluation of regional seismic loess landslides, and the fact proves that the approach is feasible and efficient, and it can provide quantitative data for major construction projects to avoid landslide disasters.

Abstract Geodetic monitoring measurements (e.g., of terrain surfaces) are used to detect deformations. Terrestrial laser scanning (TLS) or unmanned aircraft systems (UAS) equipped with lightweight cameras are often utilized for land surveying, resulting in point clouds that represent the surface of the captured object. For image-based acquisition of the area of interest, point clouds must first be generated from overlapping images, for which the Structure-from-Motion (SfM) method is commonly used. To perform deformation analyses and derive changes from them, at least two temporally different measurement epochs of the same area are required. In this article, we present both point cloud- and feature-based models from TLS and SfM-based UAS point clouds. In addition, an image-based 2D approach using optical flow is applied as an example for landslide simulation to detect changes on object surfaces. To eliminate erroneous results in the analyses due to vegetation areas, the 3D data is filtered using the CANUPO algorithm. The results of this research study show, that the task of deformation detection has some challenges, depending on the use case and the methodology. The point cloud-based methods are suitable to detect pure changes between two point clouds. Also, the direction of these changes can be determined to distinguish between material uplift and downlift. In contrast, the feature-based descriptor (Fast Point Feature Histogram, FPFH) assigns pairs of points between two epochs based on similar geometry in both point clouds therewith individual movements can be detected. However, areas that have changed significantly cannot be assigned. Optical flow shows point changes in similar dimensions to the target deformations and allows deformation analysis with much less computational effort than with 3D point clouds. Considering these findings, point cloud-based method are suitable for determining surface-based information, while the feature-based and image-based methods are capable of extracting local changes.

The invasive morning glory, Ipomoea purpurea (Convolvulaceae), poses a mounting challenge in vineyards by hindering grape harvest and as a secondary host of disease pathogens, necessitating advanced detection and control strategies. This study introduces a novel automated image analysis framework using aerial images obtained from a small fixed-wing unmanned aircraft system (UAS) and an RGB camera for the large-scale detection of I. purpurea flowers. This study aimed to assess the sampling fidelity of aerial detection in comparison with the actual infestation measured by ground validation surveys. The UAS was systematically operated over 16 vineyard plots infested with I. purpurea and another 16 plots without I. purpurea infestation. We used a semi-supervised segmentation model incorporating a Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to detect and count I. purpurea flowers. The flower detectability of the GMM was compared with that of conventional K-means methods. The results of this study showed that the GMM detected the presence of I. purpurea flowers in all 16 infested plots with 0% for both type I and type II errors, while the K-means method had 0% and 6.3% for type I and type II errors, respectively. The GMM and K-means methods detected 76% and 65% of the flowers, respectively. These results underscore the effectiveness of the GMM-based segmentation model in accurately detecting and quantifying I. purpurea flowers compared with a conventional approach. This study demonstrated the efficiency of a fixed-wing UAS coupled with automated image analysis for I. purpurea flower detection in vineyards, achieving success without relying on data-driven deep-learning models.

Geodetic monitoring measurements (e.g., of terrain surfaces) are used to detect deformations. Terrestrial laser scanning (TLS) or unmanned aircraft systems (UAS) equipped with lightweight cameras are often utilized for land surveying, resulting in point clouds that represent the surface of the captured object. For image-based acquisition of the area of interest, point clouds must first be generated from overlapping images, for which the Structure-from-Motion (SfM) method is commonly used. To perform deformation analyses and derive changes from them, at least two temporally different measurement epochs of the same area are required. In this article, we present both point cloud- and feature-based models from TLS and SfM-based UAS point clouds. In addition, an image-based 2D approach using optical flow is applied as an example for landslide simulation to detect changes on object surfaces. To eliminate erroneous results in the analyses due to vegetation areas, the 3D data is filtered using the CANUPO algorithm. The results of this research study show, that the task of deformation detection has some challenges, depending on the use case and the methodology. The point cloud-based methods are suitable to detect pure changes between two point clouds. Also, the direction of these changes can be determined to distinguish between material uplift and downlift. In contrast, the feature-based descriptor (Fast Point Feature Histogram, FPFH) assigns pairs of points between two epochs based on similar geometry in both point clouds therewith individual movements can be detected. However, areas that have changed significantly cannot be assigned. Optical flow shows point changes in similar dimensions to the target deformations and allows deformation analysis with much less computational effort than with 3D point clouds. Considering these findings, point cloud-based method are suitable for determining surface-based information, while the feature-based and image-based methods are capable of extracting local changes. [ABSTRACT FROM AUTHOR]

The invasive morning glory, Ipomoea purpurea (Convolvulaceae), poses a mounting challenge in vineyards by hindering grape harvest and as a secondary host of disease pathogens, necessitating advanced detection and control strategies. This study introduces a novel automated image analysis framework using aerial images obtained from a small fixed-wing unmanned aircraft system (UAS) and an RGB camera for the large-scale detection of I. purpurea flowers. This study aimed to assess the sampling fidelity of aerial detection in comparison with the actual infestation measured by ground validation surveys. The UAS was systematically operated over 16 vineyard plots infested with I. purpurea and another 16 plots without I. purpurea infestation. We used a semi-supervised segmentation model incorporating a Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to detect and count I. purpurea flowers. The flower detectability of the GMM was compared with that of conventional K-means methods. The results of this study showed that the GMM detected the presence of I. purpurea flowers in all 16 infested plots with 0% for both type I and type II errors, while the K-means method had 0% and 6.3% for type I and type II errors, respectively. The GMM and K-means methods detected 76% and 65% of the flowers, respectively. These results underscore the effectiveness of the GMM-based segmentation model in accurately detecting and quantifying I. purpurea flowers compared with a conventional approach. This study demonstrated the efficiency of a fixed-wing UAS coupled with automated image analysis for I. purpurea flower detection in vineyards, achieving success without relying on data-driven deep-learning models. [ABSTRACT FROM AUTHOR]

The requirements for the air reconnaissance system using unmanned aircraft systems are being studied. Problematic aspects of the air reconnaissance data processing process are analyzed from the point of view of ensuring the required level of operational efficiency and reliability of intelligence information. The possibility of automating the process of detecting aerial reconnaissance objects on video images is being investigated. A software-hardware module for automated detection and recognition of objects of interest is being developed to increase the level of processing efficiency and reliability of air reconnaissance data. Modern models of detection and recognition of objects of interest based on algorithms of the YOLO platform are studied. The software component of the module is being developed using the YOLOv8 algorithm architecture and a set of data formed in the conditions of air reconnaissance. The analysis of the results of experimental studies shows that: the developed model allows to ensure the required level of operational efficiency of video image processing and the reliability of aerial reconnaissance data; practical implementation of the developed model at ground command and control stations is possible without the use of additional computing equipment. The hardware component of the module is being developed using Raspberry Pi components. The analysis of quantitative evaluations of the efficiency of data processing using the developed software and hardware module on board the UAV of the studied class indicate an increase in time delays compared to the implementation at the command-and-control station, but it allows to ensure the required level of reliability. Further scientific research will be aimed at investigating the peculiarities of the implementation of the conceptual foundations of the developed module for various classes of UAV in order to increase the level of processing efficiency and reliability of aerial reconnaissance data.

The authors propose a sequence of acquisition and interpretation of geospatial data obtained using different methods and means during mineral mining toward the accuracy increase in evaluation of industrially disturbed land. This approach uses the concept of stage-wise transition from the areas of industrial facilities to their individual points. The integration of data of satellite surveys, ground-based laser scanning and unmanned aerial surveillance in the digital models of land areas is described. It is highlighted that the rational combination of different methods of geospatial data acquisition and interpretation produces a high-accurate geoinformation system of digital twins of various subsoil use facilities. [ABSTRACT FROM AUTHOR]

The latest conflicts in Ukraine, Nagorno-Karabakh, Syria, and Libya demonstrated the versatility of unmanned aircraft systems (UAS), which were heavily used by all sides involved. UAS provided accurate intelligence, electronic warfare (EW) capabilities, communications, precise target acquisition (TA), close air support (CAS), air interdiction, and accurate post-strike battle damage assessment (BDA). The proliferation of UAS is constantly increasing. Nowadays, this technology is not exclusive to wealthier countries anymore since "many developing countries, unable to man aviation because of the high financial burden, rely on the UAS as an off-the-shelf air force" (Jovanov 2022, 5). UAS has low procurement and operational costs, longer endurance, and lower training costs for operators compared to modern, technologically advanced aircraft. This paper aims to give an overview of how the employment of a versatile UAS, could facilitate the commander's execution of operations and accomplish the assigned mission, thus improving the unit's capabilities through all warfighting functions. [ABSTRACT FROM AUTHOR]

Abstract This paper introduces the development of test methods for capability evaluation of Unmanned Aircraft Systems (UASs) in Global Navigation Satellite System (GNSS)-denied environments. The purpose of this development is to facilitate growth in the UAS industry. We discuss the test method’s approach and what UAS’s capability evaluation is essential for UAS use in GNSS-denied environments. As a result, we decided to adopt an approach that a test method evaluates a capability to perform a single simple task. In addition, these test methods should be along with the requirement of UAS manufacturers and users. Thus, first, we develop test methods to assess mobility in narrow spaces with obstacles shielding GNSS radio. We repeatedly have demonstrations and discussions of our test method with UAS manufacturers and users from the early stage of this development and collect their opinions for improvement to proceed with the development while building consensus with them. In addition, we evaluate several UASs by the test methods and examine whether our test method makes it possible to show the difference in UAS capability. This paper describes the approach of the test methods, the test methods for mobility in a GNSS-denied environment, the demonstrations and discussions with UAS manufacturers and users, and the results of the UAS’s performance evaluation by the test methods.

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This paper deals with the problem of increasing the energy efficiency of a hybrid vertical take-off and landing aircraft. To this end, an innovative aerial vehicle was developed, featuring a distributed electrical propulsion system with ducted-fan rotors. To compare and analyze the effectiveness of the proposed propulsion system, two configurations with a different number of ducted-fan rotors were examined: a four-rotor configuration and a six-rotor configuration. The mathematical model of the four-rotor configuration was derived using the Newton–Euler formalism, allowing the design and implementation of a control strategy for conducting model-in-the-loop simulations. These simulations enabled the evaluation and analysis of the performance of the proposed propulsion system, where the numerical results demonstrated the functionality of both designs and showed that, during the multirotor flight, the configuration with six rotors increased its energy efficiency by up to 11%, providing higher vertical lift with the same power consumption. This was achieved by distributing its weight among a higher number of engines. The incorporation of two additional ducted fans increased the weight and the drag of the six-rotor configuration, resulting in a low augmentation in power consumption of 1%. Finally, this caused a decrease in airspeed by up to 4% during the cruise speed phase. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify informative indicators and establish the level of perception of their impact on the formation of excessive psycho-emotional stress among remote pilots of unmanned aircraft systems (UASs) (class 1 “Light”) during their occupational activities. Materials and methods. 41 servicemen aged 20–35 years old, who had experience in the management of UASs (class I “Light”) and were involved in the performance of a wide range of functional duties as operators of the moving objects, were studied based on the specially developed questionnaire (100-point scale) that reflected questions about the perception of the stress factor impact during their occupational activities. Statistical analysis of data was performed by descriptive and nonparametric statistics, as well as cluster, stepwise discriminant and factor analysis using the software package STATISTICA 13.3. Results. Regarding the perceptions of “stress” factor influence, the UASs pilots (operators of moving objects) were divided into two groups: group 1 could be conventionally called “hypersusceptible”, and representatives of group 2 – “hyposusceptible”. At the same time, it was found that the resulting clustering of the data into two groups provided 99.9 % opportunity for further assignment of new operators to the designated groups using the obtained supporting discriminant model (solving rules). The hidden factors have been revealed that formed the functional state of the “hypersusceptible” UASs operators, which collectively explained 54.9 % of the data variance, and “hyposusceptible” ones, which collectively explained 62.1 % of the original data variance. Conclusions. Two groups of operators, “hypersusceptible” and “hyposusceptible” to the influence of stress factors have been distinguished. The hidden factors that formed the functional state of the “hypersusceptible” UAS operators have been highlighted (the first factor “formation of fear of death” (F1↑) had 33.2 % rate and the second hidden factor “susceptibility to sensory stimuli” (F2↑), covered 21.7 % of the analyzed data variance). Two hidden factors that formed the functional state of the “hyposusceptible” UAS operators have been identified (the first factor “concentration of attention to performance of the functional duties” (F1↓) and the second factor “increased responsibility for occupational activity results” (F2↓). It has been established that there were radically different mechanisms of their functional state regulation, and “hyposusceptible” operators were more adapted to the performance of their functional duties due to the mechanisms of harmonizing their functional state and reducing experiences from the influence of harmful stress-forming factors.

Unmanned aircraft systems (UAS), commonly referred to as drones, are an emerging technology that has changed the way many industries conduct business. Precision agriculture is one industry that has consistently been predicted to be a major locus of innovation for UAS. However, this has not been the case globally. The agricultural aircraft sector in the United States is used as a case study here to consider different metrics to evaluate UAS adoption, including a proposed metric, the normalized UAS adoption index. In aggregate, UAS operators only make up 5% of the number of agricultural aircraft operators. However, the annual number of new UAS operators exceeded that of manned aircraft operators in 2022. When used on a state-by-state basis, the normalized UAS adoption index shows that there are regional differences in UAS adoption with western and eastern states having higher UAS adoption rates while central states have significantly lower UAS adoption rates. This has implications for UAS operators, manufacturers, and regulators as this industry continues to develop at a rapid pace.

The integration of remotely piloted aircraft systems (RPAS) into shared airspace requires a thorough risk analysis. Specific operations risk assessment (SORA) is a widely adopted approach by international civil aviation authorities to guide RPAS operators in evaluating risks associated with their mission. A critical step in the SORA process is analyzing the airspace where the operation will take place, which requires knowledge of the intruder aircraft's flight characteristics as well as the airspace model. This paper proposes a methodology for developing a statistical airspace model using historical aircraft track data collected in the Winnipeg Manitoba Flight Information Region. The developed methods include data cleaning routines, Kalman filters for track smoothing, and Bayesian networks for synthetic track generation, following an approach similar to that employed by the Massachusetts Institute of Technology Lincoln Lab. Additionally, the developed methodology allows for the analysis of specific models by altitude or aircraft type. The methods presented were subsequently adjusted for a comprehensive analysis spanning across Canada's diverse airspace. The initial statistical model, derived from Canada-wide data, is currently accessible to the public via the National Research Council's GitHub repository [https://github.com/nrc-cnrc/Canadian-Airspace-Models].

Rapid estimation of above-ground biomass (AGB) with high accuracy is essential for monitoring crop growth status and predicting crop yield. Recently, remote sensing techniques using unmanned aerial systems (UASs) have exhibited great potential in obtaining structural information about crops and identifying spatial heterogeneity. However, methods of data fusion of different factors still need to be explored in order to enhance the accuracy of their estimates. Therefore, the objective of this study was to investigate the combined metrics of different variables (spectral, structural and meteorological factors) for AGB estimation of wheat using UAS multispectral data. UAS images were captured on two selected growing dates at a typical reclaimed cropland in the North China Plain. The spectral response was determined using the highly correlated vegetation index (VI). A structural metric, the canopy height model (CHM), was produced using UAS-based multispectral images. The measure of growing degree days (GDD) was selected as a meteorological proxy. Subsequently, a structurally–meteorologically weighted canopy spectral response metric (SM-CSRM) was derived by the pixel-level fusion of CHM, GDD and VI. Both correlation coefficient analysis and simple function fitting were implemented to explore the highest correlation between the measured AGB and each proposed metric. The optimal regression model was built for AGB prediction using leave-one-out cross-validation. The results showed that the proposed SM-CSRM generally improved the correlation between wheat AGB and various VIs and can be used for estimating the wheat AGB. Specifically, the combination of MERIS terrestrial chlorophyll index (MTCI), vegetation-masked CHM (mCHM) and normalized GDD (nGDD) achieved an optimal accuracy (R2 = 0.8069, RMSE = 0.1667 kg/m2, nRMSE = 19.62%) through the polynomial regression method. This improved the nRMSE by 3.44% compared to the predictor using MTCI × mCHM. Moreover, the pixel-level fusion method slightly enhanced the nRMSE by ~0.3% for predicted accuracy compared to the feature-level fusion method. In conclusion, this paper demonstrated that an SM-CSRM using pixel-level fusion with canopy spectral, structural and meteorological factors can obtain a good level of accuracy for wheat biomass prediction. This finding could benefit the assessment of reclaimed cropland or the monitoring of crop growth and field management in precision agriculture. [ABSTRACT FROM AUTHOR]

This paper introduces the development of test methods for capability evaluation of Unmanned Aircraft Systems (UASs) in Global Navigation Satellite System (GNSS)-denied environments. The purpose of this development is to facilitate growth in the UAS industry. We discuss the test method's approach and what UAS's capability evaluation is essential for UAS use in GNSS-denied environments. As a result, we decided to adopt an approach that a test method evaluates a capability to perform a single simple task. In addition, these test methods should be along with the requirement of UAS manufacturers and users. Thus, first, we develop test methods to assess mobility in narrow spaces with obstacles shielding GNSS radio. We repeatedly have demonstrations and discussions of our test method with UAS manufacturers and users from the early stage of this development and collect their opinions for improvement to proceed with the development while building consensus with them. In addition, we evaluate several UASs by the test methods and examine whether our test method makes it possible to show the difference in UAS capability. This paper describes the approach of the test methods, the test methods for mobility in a GNSS-denied environment, the demonstrations and discussions with UAS manufacturers and users, and the results of the UAS's performance evaluation by the test methods. [ABSTRACT FROM AUTHOR]

The legal regulation, and hence, the training system in the field of unmanned aircraft systems (UAS) in the Russian Federation, the European Union and the United States is based on the unmanned aerial vehicles (UAV) rating with respect to UAS maximum take-off weight (MTOW) and their purpose (method of use). In this regard, small-unmanned aircraft (sUAS) are identified – in our country up to 30 kg, in the EU and the USA up to 55 lbs (25 kg) and UAV with larger weight. In the USA and Europe, the training of remote pilots for sUAS is differentiated based on the degree of risk that UAV can represent for public safety. Thus, the training of remote pilots to use UAVs with MTOW less than 25 kg (55 lbs) in a sparsely populated area during daylight hours under the conditions of visual range is conducted in the online format, the result of which is taking tests. In the United States and Europe, the UAV application with MTOW more than 25 kg (55 lbs) or performing UAV operations, presenting a potential risk for public safety, requires more comprehensive and long-term training of remote pilots. In the Russian Federation, UAS personnel training is conducted in educational organizations according to different programs, which vary significantly depending on a specific type of aviation UAVs refers to: State, Civil or Experimental. UAS personnel training programs for various aviation types are not harmonized, which leads to the failure to credit previously received education in training to perform activities in another aviation type. The article describes the analysis results of the international and national experience, perspectives for the development of the UAS personnel training system, as well as formulates the proposals concerning further development of the national system for UAS specialists training.

Thermal infrared images can show the temperature change of objects that represent the significant characteristics, therefore they have been applied on many fields. With the development of UAS (Unmanned Aircraft Systems), the thermal infrared camera can be carried on the Helicopter UAS to collect thermal images with high resolution in a local area more efficiently. However, the ground control marks for thermal images are uneasy to be set up. In this study, the Trimble BD970 GNSS OEM board was carried on the Helicopter UAS to collect Global Positioning System (GPS) data and the bundle adjustment fbr the positioning and orientation of collected thermal images, i.e., AT (Aerial Triangulation), was supported by Virtual Base Station--Real Time Kinematic (VBS-RTK) GPS technique. In theory, the GPS antenna offset is not changed and can be viewed as constant. Therefore, in this study, the new model based on bundle adjustment was developed to solve GPS antenna offset, exterior orientation parameters and the object coordinate of tie points simultaneously by unified least squares adjustment. Besides, thermal infrared camera is a non-metric camera. In order to acquire high precision camera parameters for AT, thermal infrared camera was calibrated by field method. In this study, due to the bad accuracy of GPS observations in real data collection, the GPS antenna offset could be not solved. Although the accuracy of GPS observations was bad, the GPS observations could also be used to support the bundle adjustment for the positioning and orientation of thermal images under better block geometry and ground control points still could be reduced. [ABSTRACT FROM AUTHOR]

Numerous hectares of land are destroyed by wildfires every year, causing harm to the environment, the economy, and the ecology. More than fifty million acres have burned in several states as a result of recent forest fires in the Western United States and Australia. According to scientific predictions, as the climate warms and dries, wildfires will become more intense and frequent, as well as more dangerous. These unavoidable catastrophes emphasize how important early wildfire detection and prevention are. The energy management system described in this paper uses an unmanned aircraft system (UAS) with air quality sensors (AQSs) to monitor spot fires before they spread. The goal was to develop an efficient autonomous patrolling system that detects early wildfires while maximizing the battery life of the UAS to cover broad areas. The UAS will send real-time data (sensor readings, thermal imaging, etc.) to a nearby base station (BS) when a wildfire is discovered. An optimization model was developed to minimize the total amount of energy used by the UAS while maintaining the required levels of data quality. Finally, the simulations showed the performance of the proposed solution under different stability conditions and for different minimum data rate types. [ABSTRACT FROM AUTHOR]

Unmanned aircraft systems (UAS), commonly referred to as drones, are an emerging technology that has changed the way many industries conduct business. Precision agriculture is one industry that has consistently been predicted to be a major locus of innovation for UAS. However, this has not been the case globally. The agricultural aircraft sector in the United States is used as a case study here to consider different metrics to evaluate UAS adoption, including a proposed metric, the normalized UAS adoption index. In aggregate, UAS operators only make up 5% of the number of agricultural aircraft operators. However, the annual number of new UAS operators exceeded that of manned aircraft operators in 2022. When used on a state-by-state basis, the normalized UAS adoption index shows that there are regional differences in UAS adoption with western and eastern states having higher UAS adoption rates while central states have significantly lower UAS adoption rates. This has implications for UAS operators, manufacturers, and regulators as this industry continues to develop at a rapid pace. [ABSTRACT FROM AUTHOR]

Background: Small unoccupied aircraft systems (UAS) are replacing or supplementing occupied aircraft and ground-based surveys in animal monitoring due to improved sensors, efficiency, costs, and logistical benefits. Numerous UAS and sensors are available and have been used in various methods. However, justification for selection or methods used are not typically offered in published literature. Furthermore, existing reviews do not adequately cover past and current UAS applications for animal monitoring, nor their associated UAS/sensor characteristics and environmental considerations. We present a systematic map that collects and consolidates evidence pertaining to UAS monitoring of animals. Methods: We investigated the current state of knowledge on UAS applications in terrestrial animal monitoring by using an accurate, comprehensive, and repeatable systematic map approach. We searched relevant peer-reviewed and grey literature, as well as dissertations and theses, using online publication databases, Google Scholar, and by request through a professional network of collaborators and publicly available websites. We used a tiered approach to article exclusion with eligible studies being those that monitor (i.e., identify, count, estimate, etc.) terrestrial vertebrate animals. Extracted metadata concerning UAS, sensors, animals, methodology, and results were recorded in Microsoft Access. We queried and catalogued evidence in the final database to produce tables, figures, and geographic maps to accompany this full narrative review, answering our primary and secondary questions. Review findings: We found 5539 articles from our literature searches of which 216 were included with extracted metadata categories in our database and narrative review. Studies exhibited exponential growth over time but have levelled off between 2019 and 2021 and were primarily conducted in North America, Australia, and Antarctica. Each metadata category had major clusters and gaps, which are described in the narrative review. Conclusions: Our systematic map provides a useful synthesis of current applications of UAS-animal related studies and identifies major knowledge clusters (well-represented subtopics that are amenable to full synthesis by a systematic review) and gaps (unreported or underrepresented topics that warrant additional primary research) that guide future research directions and UAS applications. The literature for the use of UAS to conduct animal surveys has expanded intensely since its inception in 2006 but is still in its infancy. Since 2015, technological improvements and subsequent cost reductions facilitated widespread research, often to validate UAS technology to survey single species with application of descriptive statistics over limited spatial and temporal scales. Studies since the 2015 expansion have still generally focused on large birds or mammals in open landscapes of 4 countries, but regulations, such as maximum altitude and line-of-sight limitations, remain barriers to improved animal surveys with UAS. Critical knowledge gaps include the lack of (1) best practices for using UAS to conduct standardized surveys in general, (2) best practices to survey whole wildlife communities in delineated areas, and (3) data on factors affecting bias in counting animals from UAS images. Promising advances include the use of thermal sensors in forested environments or nocturnal surveys and the development of automated or semi-automated machine-learning algorithms to accurately detect, identify, and count animals from UAS images. [ABSTRACT FROM AUTHOR]

The usage of unmanned aircraft systems to complete routine, commercial, and industrial tasks has increased throughout the world, evidencing better profitability and reducing risks for operators. However, in some countries, there is a low implementation of unmanned aircraft systems, particularly in the electrical sector, due to a lack of appropriation or adaptation of technology to the local environment. Therefore, this paper presents an analysis of the uses of unmanned aircraft systems in the electrical industry worldwide and its possible application to a local context to identify how the expansion of unmanned aerial vehicles is helping various industries. The contribution of this paper is to show how the employment of unmanned aerial vehicles can help in any particular task in the electrical sector and the appropriation of these technologies in a country, showing a possible categorization of unmanned aerial vehicles based on future applications and current regulations. The analysis was carried out in the Colombian context, considering the current regulation and the impact of its use. This research considers safety, security, and privacy implications, including the reduction of personal harm with low operation costs. In addition, the importance of future implementations in Colombia is discussed as a topic of interest for any electrical company, researchers, and government entities. [ABSTRACT FROM AUTHOR]

Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points. Yet, most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single‐time‐point data. Here, we used time‐series phenotypic data collected with an unmanned aircraft system for a large panel of soybean (Glycine max (L.) Merr.) varieties to identify previously uncharacterized loci. Specifically, we focused on the dissection of canopy coverage (CC) variation from this rich data set. We also inferred the speed of canopy closure, an additional dimension of CC, from the time‐series data, as it may represent an important trait for weed control. Genome‐wide association studies (GWASs) identified 35 loci exhibiting dynamic associations with CC across developmental stages. The time‐series data enabled the identification of 10 known flowering time and plant height quantitative trait loci (QTLs) detected in previous studies of adult plants and the identification of novel QTLs influencing CC. These novel QTLs were disproportionately likely to act earlier in development, which may explain why they were missed in previous single‐time‐point studies. Moreover, this time‐series data set contributed to the high accuracy of the GWASs, which we evaluated by permutation tests, as evidenced by the repeated identification of loci across multiple time points. Two novel loci showed evidence of adaptive selection during domestication, with different genotypes/haplotypes favored in different geographic regions. In summary, the time‐series data, with soybean CC as an example, improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves. [ABSTRACT FROM AUTHOR]

In this paper we introduce a bi-objective location-allocation problem for Unmanned Aircraft Systems (UASs) operating in a hostile environment. The objective is to find the locations to deploy UASs and assign Unmanned Aerial Vehicles to regions for surveillance. One of the objectives is to maximize search effectiveness, while the second is the minimization of the threats posed to the UASs. These two objectives are in conflict, because they are affected differently by the proximity between the UAS locations and the target regions. First, we have formulated this problem as a mixed integer nonlinear program. Next, we have developed its linearization which can be solved by a commercial optimizer for small-scale problem instances. To solve large-scale problems, we have adopted a well-known metaheuristic for multi-objective problems, namely the elitist non-dominated sorting genetic algorithm. We have also developed a hybrid approach, which has proven to be more effective than each approach alone. [ABSTRACT FROM AUTHOR]

One of the challenges of Unmanned Aircraft System (UAS) operations is to operate an unmanned aircraft with minimal risk to people on the ground. The purpose of this study is to define and measure such risks as population risk, by incorporating spatiotemporal changes in population density. Unlike previous studies, we use high-resolution de facto population data instead of residential population data to reflect the spatiotemporal characteristics of population distribution. Furthermore, we analyze the impact of mitigation measures based on population risk in the context of airspace management. We set a restricted airspace by using population risk and an acceptable level of safety. Scenario analysis of the study area in Seoul, South Korea provides a richer set of findings regarding spatiotemporal differences in restricted airspace. During the daytime, there are many restricted airspaces around commercial areas, but few around residential areas. Additionally, we observe the difference between restricting airspace based on population risk derived from the residential population and from the de facto population. These findings confirm the importance of accurately considering population density when assessing and mitigating the population risk associated with UAS operations. Sensitivity analysis also reveals the need to precisely estimate population density when estimating population risk with combinations of multiple parameter values. The proposed approach captures spatiotemporal characteristics of population distribution when assessing the population risk associated with UAS.

Uncrewed aerial vehicles (UAVs), commonly known as drones, have emerged as transformative tools in the healthcare sector, offering the potential to revolutionize medical logistics, emergency response, and patient care. This scoping review provides a comprehensive exploration of the diverse applications of drones in healthcare, addressing critical gaps in existing literature. While previous reviews have primarily focused on specific facets of drone technology within the medical field, this study offers a holistic perspective, encompassing a wide range of potential healthcare applications. The review categorizes and analyzes the literature according to key domains, including the transport of biomedical goods, automated external defibrillator (AED) delivery, healthcare logistics, air ambulance services, and various other medical applications. It also examines public acceptance and the regulatory framework surrounding medical drone services. Despite advancements, critical knowledge gaps persist, particularly in understanding the intricate interplay between technological challenges, the existing regulatory framework, and societal acceptance. This review highlights the need for the extensive validation of cost-effective business cases, the development of control techniques that can address time and resource savings within the constraints of real-life scenarios, the design of crash-protected containers, and the establishment of corresponding tests and standards to demonstrate their conformity.

UAS-based commercial services such as urban parcel delivery are expected to grow in the upcoming years and may lead to a large volume of UAS operations in urban areas. These flights may pose safety risks to persons and property on the ground, which are referred to as third-party risks. Path-planning methods have been developed to generate a nominal flight path for each UAS flight that poses relative low third-party risks by passing over less risky areas, e.g., areas with low-density unsheltered populations. However, it is not clear if risk minimization per flight works well in a commercial UAS operation that involves a large number of annual flights in an urban area. Recently, it has been shown that when using shortest flight path planning, a UAS-based parcel delivery service in an urban area can lead to society-critical third-party risk levels. The aim of this paper is to evaluate the mitigating effect of state-of-the-art risk-aware path planning on these society-critical third-party risk levels. To accomplish this, a third-party risk simulation using the shortest paths is extended with a state-of-the-art risk-aware path-planning method, and the societal effects on third-party risk levels have been assessed and compared to those obtained using shortest paths. The results show that state-of-the-art risk-aware path planning can reduce the total number of fatalities in an area, but at the cost of a critical increase in safety risks for persons living in areas that are favored by a state-of-the-art risk-aware path-planning method. [ABSTRACT FROM AUTHOR]

Rapid estimation of above-ground biomass (AGB) with high accuracy is essential for monitoring crop growth status and predicting crop yield. Recently, remote sensing techniques using unmanned aerial systems (UASs) have exhibited great potential in obtaining structural information about crops and identifying spatial heterogeneity. However, methods of data fusion of different factors still need to be explored in order to enhance the accuracy of their estimates. Therefore, the objective of this study was to investigate the combined metrics of different variables (spectral, structural and meteorological factors) for AGB estimation of wheat using UAS multispectral data. UAS images were captured on two selected growing dates at a typical reclaimed cropland in the North China Plain. The spectral response was determined using the highly correlated vegetation index (VI). A structural metric, the canopy height model (CHM), was produced using UAS-based multispectral images. The measure of growing degree days (GDD) was selected as a meteorological proxy. Subsequently, a structurally–meteorologically weighted canopy spectral response metric (SM-CSRM) was derived by the pixel-level fusion of CHM, GDD and VI. Both correlation coefficient analysis and simple function fitting were implemented to explore the highest correlation between the measured AGB and each proposed metric. The optimal regression model was built for AGB prediction using leave-one-out cross-validation. The results showed that the proposed SM-CSRM generally improved the correlation between wheat AGB and various VIs and can be used for estimating the wheat AGB. Specifically, the combination of MERIS terrestrial chlorophyll index (MTCI), vegetation-masked CHM (mCHM) and normalized GDD (nGDD) achieved an optimal accuracy (R2 = 0.8069, RMSE = 0.1667 kg/m2, nRMSE = 19.62%) through the polynomial regression method. This improved the nRMSE by 3.44% compared to the predictor using MTCI × mCHM. Moreover, the pixel-level fusion method slightly enhanced the nRMSE by ~0.3% for predicted accuracy compared to the feature-level fusion method. In conclusion, this paper demonstrated that an SM-CSRM using pixel-level fusion with canopy spectral, structural and meteorological factors can obtain a good level of accuracy for wheat biomass prediction. This finding could benefit the assessment of reclaimed cropland or the monitoring of crop growth and field management in precision agriculture.

This paper deals with the problem of increasing the energy efficiency of a hybrid vertical take-off and landing aircraft. To this end, an innovative aerial vehicle was developed, featuring a distributed electrical propulsion system with ducted-fan rotors. To compare and analyze the effectiveness of the proposed propulsion system, two configurations with a different number of ducted-fan rotors were examined: a four-rotor configuration and a six-rotor configuration. The mathematical model of the four-rotor configuration was derived using the Newton–Euler formalism, allowing the design and implementation of a control strategy for conducting model-in-the-loop simulations. These simulations enabled the evaluation and analysis of the performance of the proposed propulsion system, where the numerical results demonstrated the functionality of both designs and showed that, during the multirotor flight, the configuration with six rotors increased its energy efficiency by up to 11%, providing higher vertical lift with the same power consumption. This was achieved by distributing its weight among a higher number of engines. The incorporation of two additional ducted fans increased the weight and the drag of the six-rotor configuration, resulting in a low augmentation in power consumption of 1%. Finally, this caused a decrease in airspeed by up to 4% during the cruise speed phase.

The possibility of using unmanned aircraft systems (UAS) for cadastral survey purposes was investigated in this research. A study site consisting of 26 ground control points (GCP) and checkpoints (CP) was established. The study site was first measured by the classical methods of geodetic surveying, i.e., by the polar method using a total station. After that, all points were additionally measured by the Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) method. The GNSS RTK method was used to determine the coordinates of all points in the official map projection of Croatia, HTRS96/TM, while the polar method was used to increase the positional "strength" of points in all directions, i.e., to improve the relative accuracy between them. Using UASs with different image sensor characteristics, the study site was measured by an aerial photogrammetry method at different flight heights with the purpose of obtaining a high-quality digital orthophoto plan (DOF). The absolute orientation of the model was performed using the external orientation data of each digital image based on GNSS and Inertial Measurement Unit (IMU) UAS's sensors, as well as using GCPs. Achieved precision of obtained DOF, as well as accuracy analysis of aerial photogrammetry was performed by considering the adjusted survey data collected by classical and GNSS RTK methods as true values and comparing them with the coordinates obtained by the aerial photogrammetry method from DOFs. Based on the achieved results and conclusions obtained from the study site, the second field test was performed above a small settlement which served as an area for cadastral survey using the UAS and GNSS RTK method. Again, precision and accuracy were determined, based on which we derived recommendations and conclusions for using UASs for cadastral survey purposes. [ABSTRACT FROM AUTHOR]

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