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The objective of this paper is a study regarding the design of an unmanned aerial vehicle to ensure security by monitoring and collecting data from the atmosphere. Flight autonomy is realized using a propeller and additional for sustentation, thrust and orientation it uses the well Coandă effect. Atmospheric parameters are monitored by special gadgets, maintained by the ground' operated vehicle. Flow dynamics study on the vehicle's cap during the auto-sustainable process is realized using Fluent 6.3 software. [ABSTRACT FROM AUTHOR]

Aerial surveillance could be a useful tool for early detection and quantification of plant diseases, however, there are often confounding effects of other types of plant stress. Stemphylium leaf blight (SLB), caused by the fungus Stemphylium vesicarium, is a damaging foliar disease of onion. Studies were conducted to determine if near-infrared photographic images could be used to accurately assess SLB severity in onion research trials in the Holland Marsh in Ontario, Canada. The site was selected for its uniform soil and level topography. Aerial photographs were taken in 2015 and 2016 using an Xnite-Canon SX230NDVI with a near-infrared filter, mounted on a modified Cine Star—8 MK Heavy Lift RTF octocopter UAV. Images were taken at 15–20 m above the ground, providing an average of 0.5 cm/pixel and a field of view of 15 × 20 m. Photography and ground assessments of disease were carried out on the same day. NDVI (normalized difference vegetation index), green NDVI, chlorophyll index and plant senescence reflective index (PSRI) were calculated from the images. There were differences in SLB incidence and severity in the field plots and differences in the vegetative indices among the treatments, but there were no correlations between disease assessments and any of the indices. [ABSTRACT FROM AUTHOR]

In recent years, small-scale Unmanned Aerial Vehicles (UAVs) have been used in many video surveillance applications, such as vehicle tracking, border control, dangerous object detection, and many others. Anomaly detection can represent a prerequisite of many of these applications thanks to its ability to identify areas and/or objects of interest without knowing them a priori. In this paper, a One-Class Support Vector Machine (OC-SVM) anomaly detector based on customized Haralick textural features for aerial video surveillance at low-altitude is presented. The use of a One-Class SVM, which is notoriously a lightweight and fast classifier, enables the implementation of real-time systems even when these are embedded in low-computational small-scale UAVs. At the same time, the use of textural features allows a vision-based system to detect micro and macro structures of an analyzed surface, thus allowing the identification of small and large anomalies, respectively. The latter aspect plays a key role in aerial video surveillance at low-altitude, i.e., 6 to 15 m, where the detection of common items, e.g., cars, is as important as the detection of little and undefined objects, e.g., Improvised Explosive Devices (IEDs). Experiments obtained on the UAV Mosaicking and Change Detection (UMCD) dataset show the effectiveness of the proposed system in terms of accuracy, precision, recall, and F1-score, where the model achieves a 100% precision, i.e., never misses an anomaly, but at the expense of a reasonable trade-off in its recall, which still manages to reach up to a 71.23% score. Moreover, when compared to classical Haralick textural features, the model obtains significantly higher performances, i.e., ≈20% on all metrics, further demonstrating the approach effectiveness. [ABSTRACT FROM AUTHOR]

This paper examines the use of exploratory spatial analysis for identifying hotspots of shipping-based oil pollution in the Pacific Region of Canada’s Exclusive Economic Zone. It makes use of data collected from fiscal years 1997/1998 to 2005/2006 by the National Aerial Surveillance Program, the primary tool for monitoring and enforcing the provisions imposed by MARPOL 73/78. First, we present oil spill data as points in a “dot map” relative to coastlines, harbors and the aerial surveillance distribution. Then, we explore the intensity of oil spill events using the Quadrat Count method, and the Kernel Density Estimation methods with both fixed and adaptive bandwidths. We found that oil spill hotspots where more clearly defined using Kernel Density Estimation with an adaptive bandwidth, probably because of the “clustered” distribution of oil spill occurrences. Finally, we discuss the importance of standardizing oil spill data by controlling for surveillance effort to provide a better understanding of the distribution of illegal oil spills, and how these results can ultimately benefit a monitoring program. [Copyright &y& Elsevier]

Abstract: This paper examines the use of aerial surveillance activities conducted in the North Sea region of Europe since 1986 to assess trends in levels of oil inputs into the marine environment, both across the whole region and within the waters of the different coastal states. It makes use of data collected under the aegis of the 1969 Bonn Agreement through its Annual Reports on Aerial Surveillance and examines developments in surveillance methods and technology which have led to improvements in the detection of oil spills, even during the hours of darkness. The paper then examines country specific data for the eight North Sea contracting parties to the Agreement to assess trends in oil spills in the region. [Copyright &y& Elsevier]

In this paper, we aim to open up new perspectives in the field of autonomous aerial surveillance and target tracking systems, by exploring an alternative that, surprisingly, and to the best of the authors' knowledge, has not been addressed in that context by the research community thus far. It can be summarized by the following two questions. Under the scope of such applications, what are the implications and possibilities offered by mounting several steerable cameras onboard of each aerial agent? Second, how can optimization algorithms benefit from this new framework, in their attempt to provide more efficient and cost-effective solutions on these areas? The paper presents the idea as an additional degree of freedom to be exploited, which can enable more efficient alternatives in the deployment of such applications. As an initial approach, the problem of the optimal positioning with respect to a set of targets of one single agent, equipped with several onboard tracking cameras with different or variable focal lengths, is addressed. As a consequence of this allowed heterogeneity in focal lengths, the notion of distance needs to be adapted into a notion of optical range , as the agent can trade longer Euclidean distances for correspondingly longer focal lengths. Moreover, the proposed optimization indices try to balance, in an optimal way, the verticality of the viewpoints along with the optical range to the targets. Under these premises, several positioning strategies are proposed and comparatively evaluated.

Paddy field farms are being converted to provide both grain and aquatic products. Presently, China has 1.11 million ha of rice–crayfish farming system which is growing. Conversion from paddy field-based farming to rice–crayfish coculture systems requires field engineering to excavate ditches for breeding. However, the net ecological benefits of this conversion remain unknown. To address this gap, this study used unmanned aerial vehicle surveillance to monitor the proportion of area of different types of ditches (circle, U-shaped, L-shaped, and I-shaped ditch) of the total field area in 140 rice–crayfish fields in the Hung-tse Lake area. Then the ecosystem service (ES) values were evaluated after the conversion to rice–crayfish coculture. The results showed that the average proportions of area covered by circular, U-shaped, L-shaped, and I-shaped ditches of the total field area were 26.29%, 17.38%, 9.63%, and 1.92%, respectively. Conversion to rice–crayfish coculture increases the net ES value by 145.3–176.9%, but also negatively impacts the ecological environment which should not be ignored. Indeed, with a decrease in the proportion of the ditch, the net ES value increased. Overall, the study suggested that rice–crayfish coculture fields with L- and I-shaped ditches produce substantial crayfish yields and stable rice yields, whereas those with circular and U-shaped ditches squeeze the rice planting area and reduce rice yield. The study provides vital information to avoid the blind expansion and development of rice–crayfish farming from the perspective of the ecological environment, and provides policy suggestions for sustainable development. • Ecosystem service (ES) values of rice–crayfish coculture were evaluated. • The proportions of different ditches area of the fields were from 1.92 to 26.29%. • Rice–crayfish coculture fields with L- and I-shaped ditches were sustainable. • Rice–crayfish coculture fields with circular and U-shaped ditches were not recommended. [ABSTRACT FROM AUTHOR]

To overcome the limitation in flight time and enable unmanned aerial vehicles (UAVs) to survey remote sites of interest, this paper investigates an approach involving the collaboration with public transportation vehicles (PTVs) and the deployment of charging stations. In particular, the focus of this paper is on the deployment of charging stations. In this approach, a UAV first travels with some PTVs, and then flies through some charging stations to reach remote sites. While the travel time with PTVs can be estimated by the Monte Carlo method to accommodate various uncertainties, we propose a new coverage model to compute the travel time taken for UAVs to reach the sites. With this model, we formulate the optimal deployment problem with the goal of minimising the average travel time of UAVs from the depot to the sites, which can be regarded as a reflection of the quality of surveillance (QoS) (the shorter the better). We then propose an iterative algorithm to place the charging stations. We show that this algorithm ensures that any movement of a charging station leads to a decrease in the average travel time of UAVs. To demonstrate the effectiveness of the proposed method, we make a comparison with a baseline method. The results show that the proposed model can more accurately estimate the travel time than the most commonly used model, and the proposed algorithm can relocate the charging stations to achieve a lower flight distance than the baseline method. [ABSTRACT FROM AUTHOR]

As a typical cyber-physical system, networked unmanned aerial vehicles (UAVs) have received much attention in recent years. Emerging communication technologies and high-performance control methods enable networked UAVs to operate as aerial sensor networks to collect more complete and consistent information with significantly improved mobility and flexibility than traditional sensing platforms. One of the main applications of networked UAVs is surveillance and monitoring, which constitute essential components of a well-functioning public safety system and many industrial applications. Although the existing literature on surveillance and monitoring UAVs is extensive, a comprehensive survey on this topic is lacking. This article classifies publications on networked UAVs for surveillance and monitoring using the targets of interest and analyzes several typical problems on this topic, including the control, navigation, and deployment optimization of UAVs. The related research gaps and future directions are also presented. [ABSTRACT FROM AUTHOR]

Very large marine protected areas are in danger of becoming 'paper parks'. This paper uses an interdisciplinary team to investigate the use of remote sensing technologies to provide sufficient evidence for effective fisheries management. It uses the intended marine protected area around Ascension Island as a case study. Satellite technology provides opportunities to detect the presence of fishing vessels but because of difficulties with data interpretation, it is unlikely to be a sole source of evidence for prosecutions. Developing drone technology and traditional over-flights by aerial surveillance may supplement satellite technology with 'eyewitness' evidence. Well-crafted regulations will be able to make some use of this data, but the evidential requirements of criminal courts make prosecutions difficult to pursue. There is some scope to expand management opportunities through vesting the fishery in a public body and pursuing offenders through civil law, this approach having a different suite of remedies. Other opportunities lie in giving very large marine protected areas legal personality which has similar advantages and additional reputational benefits. Using remote sensing data in the civil court poses evidential problems. An alternative approach is to collate data around frequent infringers and, by negatively impacting on their reputation, restrict their ability to obtain insurance, finance, access to fisheries and market access. This is exemplified in port state measures by fisheries authorities and chain of custody requirements by labelling bodies. Data sharing raises challenges with intellectual property and coordination. The paper demonstrates that there are opportunities to make VLMPAs work more effectively. [ABSTRACT FROM AUTHOR]

This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing in a distributed manner. For realizing UAV-assisted aerial surveillance or flexible mobile cellular services, robust wireless charging mechanisms are essential for delivering energy sources from charging towers (i.e., charging infrastructure) to their associated UAVs for seamless operations of autonomous UAVs in the sky. In order to actively and intelligently manage the energy resources in charging towers, a MADRL-based coordinated energy management system is desired and proposed for energy resource sharing among charging towers. When the required energy for charging UAVs is not enough in charging towers, the energy purchase from utility company (i.e., energy source provider in local energy market) is desired, which takes high costs. Therefore, the main objective of our proposed coordinated MADRL-based energy sharing learning algorithm is minimizing energy purchase from external utility companies to minimize system-operational costs. Finally, our performance evaluation results verify that the proposed coordinated MADRL-based algorithm achieves desired performance improvements. [ABSTRACT FROM AUTHOR]

This paper presents the T-RexNet approach to detect small moving objects in videos by using a deep neural network. T-RexNet combines the advantages of Single-Shot-Detectors with a specific feature-extraction network, thus overcoming the known shortcomings of Single-Shot-Detectors in detecting small objects. The deep convolutional neural network includes two parallel paths: the first path processes both the original picture, in gray-scale format, and differences between consecutive frames; in the second path, differences between a set of three consecutive frames is only handled. As compared with generic object detectors, the method limits the depth of the convolutional network to make it less sensible to high-level features and easier to train on small objects. The simple, Hardware-efficient architecture attains its highest accuracy in the presence of videos with static framing. Deploying our architecture on the NVIDIA Jetson Nano edge-device shows its suitability to embedded systems. To prove the effectiveness and general applicability of the approach, real-world tests assessed the method performances in different scenarios, namely, aerial surveillance with the WPAFB 2009 dataset, civilian surveillance using the Chinese University of Hong Kong (CUHK) Square dataset, and fast tennis-ball tracking, involving a custom dataset. Experimental results prove that T-RexNet is a valid, general solution to detect small moving objects, which outperforms in this task generic existing object-detection approaches. The method also compares favourably with application-specific approaches in terms of the accuracy vs. speed trade-off. [ABSTRACT FROM AUTHOR]

Recent technology advances in CMOS image sensors (CIS) enable their utilization in the most demanding of surveillance fields, especially visual surveillance and intrusion detection in intelligent surveillance systems, aerial surveillance in war zones, Earth environmental surveillance by satellites in space monitoring, agricultural monitoring using wireless sensor networks and internet of things and driver assistance in automotive fields. This paper presents an overview of CMOS image sensor-based surveillance applications over the last decade by tabulating the design characteristics related to image quality such as resolution, frame rate, dynamic range, signal-to-noise ratio, and also processing technology. Different models of CMOS image sensors used in all applications have been surveyed and tabulated for every year and application. [ABSTRACT FROM AUTHOR]

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The paper presents the concept of planning the optimal trajectory of fixed-wing unmanned aerial vehicle (UAV) of a short-range tactical class, whose task is to recognize a set of ground objects as a part of a reconnaissance mission. Tasks carried out by such systems are mainly associated with an aerial reconnaissance using Electro-Optical/Infrared (EO/IR) systems and Synthetic Aperture Radars (SARs) to support military operations. Execution of a professional reconnaissance of the indicated objects requires determining the UAV flight trajectory in the close neighborhood of the target, in order to collect as much interesting information as possible. The paper describes the algorithm for determining UAV flight trajectories, which is tasked with identifying the indicated objectives using the sensors specified in the order. The presence of UAV threatening objects is taken into account. The task of determining the UAV flight trajectory for recognition of the target is a component of the planning process of the tactical class UAV mission, which is also presented in the article. The problem of determining the optimal UAV trajectory has been decomposed into several subproblems: determining the reconnaissance flight method in the vicinity of the currently recognized target depending on the sensor used and the required parameters of the recognition product (photo, film, or SAR scan), determining the initial possible flight trajectory that takes into account potential UAV threats, and planning detailed flight trajectory considering the parameters of the air platform based on the maneuver planning algorithm designed for tactical class platforms. UAV route planning algorithms with time constraints imposed on the implementation of individual tasks were used to solve the task of determining UAV flight trajectories. The problem was formulated in the form of a Mixed Integer Linear Problem (MILP) model. For determining the flight path in the neighborhood of the target, the optimal control algorithm was also presented in the form of a MILP model. The determined trajectory is then corrected based on the construction algorithm for determining real UAV flight segments based on Dubin curves. [ABSTRACT FROM AUTHOR]

The increasing frequency of flooding worldwide has driven research to improve near real-time flood mapping from remote-sensing data. Improved automation and processing speed to map both open water and vegetated area flooding have resulted from these research efforts. Despite these achievements, flood mapping in urban areas where a significant number of overall impacts are felt remains a challenge. Near real-time data availability, shadowing caused by manmade infrastructure, spatial resolution, and cloud cover inhibiting optical transmission, are all factors that complicate detailed urban flood mapping needed to inform response efforts. This paper uses numerous data sources collected during two major flood events that impacted the same region of Eastern Canada in 2017 and 2019 to test different urban flood mapping approaches presented as case studies in three separate urban boroughs. Cloud-free high-resolution 3 m PlanetLab optical data acquired near peak-flood in 2019 were used to generate a maximum flood extent product for that year. Approaches using new Lidar Digital Elevation Models (DEM)s and water height estimated from nineteen RADARSAT-2 flood maps, point-based flood perimeter observations from citizen geographic information, and simulated traffic camera or other urban sensor network data were tested and verified using independent data. Coherent change detection (CCD) using multi-temporal Interferometric Wide (IW) Sentinel-1 data was also tested. Results indicate that while clear-sky high-resolution optical imagery represents the current gold standard, its availability is not guaranteed due to timely coverage and cloud cover. Water height estimated from 8 to 12.5 m resolution RADARSAT-2 flood perimeters were not sufficiently accurate to flood adjacent urban areas using a Lidar DEM in near real-time, but all nineteen scenes combined captured boroughs that flooded at least once in both flood years. CCD identified flooded boroughs and roughly captured their flood extents, but lacked timeliness and sufficient detail to inform street-level decision-making in near real-time. Point-based flood perimeter observation, whether from in-situ sensors or high-resolution optical satellites combined with Lidar DEMs, can generate accurate full flood extents under certain conditions. Observed point-based flood perimeters on manmade features with low topographic variation produced the most accurate flood extents due to reliable water height estimation from these points. [ABSTRACT FROM AUTHOR]

• Reducing oilfield methane emissions is among the fastest ways to slow climate change. • Main technical challenge to reduce those emissions is locating emission sources. • Here we describe a monitoring program based on aerial LiDAR surveillance. • Solve vehicle routing problem to optimize routes covered by airborne sensors. • Potential to reduce both methane emissions and inspection costs simultaneously. Methane is a short-lived climate pollutant responsible for approximately 20% of anthropogenic global warming, and reducing methane emissions from the oil and gas (O&G) industry is considered among the most urgent and actionable measures to mitigate climate change. Recent reports suggest a large fraction of upstream O&G methane emissions result from a small number of super-emitter facilities, emphasizing the value of novel methods that inspect O&G facilities with greater frequency than is practical using existing techniques. Here we describe an optimized method wherein O&G facilities are inspected for emissions at high frequency and high sensitivity using active laser (LiDAR) sensors mounted to aircraft. The method relies on a hierarchical clustering and routing procedure to establish optimal routes to be flown by aircraft departing from local airports and equipped with LiDAR methane sensors. Routes are optimized to inspect all well sites subject to emissions regulation in three O&G intensive regions: the Permian basin, the state of Colorado, and the state of Pennsylvania. While some cost estimates require additional field data, these modeling results suggest the optimized inspections can be performed with comparable effectiveness and up to a factor of six lower cost per inspection compared to current detection methods. The cost per inspection required to achieve equivalent emissions reduction depends on factors such as the weather conditions during inspection (which impacts the limit of detection and therefore the inspection frequency required to achieve equivalency) and the well density (which impacts the flying distance), and the advantage of this program over traditional inspection will be reduced under unfavorable conditions. These modeling results suggest that optimized routing may enable frequent inspection of upstream O&G facilities at large scale and potentially lead to a substantial decrease in both oilfield methane emissions and compliance costs borne by industry. [ABSTRACT FROM AUTHOR]

This work proposes a learnheuristic approach (combination of heuristics with machine learning) to solve an aerial-drone team orienteering problem. The goal is to maximise the total reward collected from information gathering or surveillance observations of a set of known targets within a fixed amount of time. The aerial drone team orienteering problem has the complicating feature that the travel times between targets depend on a drone's flight path between previous targets. This path-dependence is caused by the aerial surveillance drones flying under the influence of air-resistance, gravity, and the laws of motion. Sharp turns slow drones down and the angle of ascent and air-resistance influence the acceleration a drone is capable of. The route dependence of inter-target travel times motivates the consideration of a learnheuristic approach, in which the prediction of travel times is outsourced to a machine learning algorithm. This work proposes an instance-based learning algorithm with interpolated predictions as the learning module. We show that a learnheuristic approach can lead to higher quality solutions in a shorter amount of time than those generated from an equivalent metaheuristic algorithm, an effect attributed to the search-diversity enhancing consequence of the online learning process. • Introduction to the team orienteering problem for aerial drones with path dependent travel times. • Learnheuristic solution approach integrates metaheuristics and machine learning. • Learnheuristic approach is compared to metaheuristic and a priori learning methods. • Learnheuristic solution reduces solution time by two orders of magnitude. • Learnheuristic simultaneous learning and optimisation gives rise to emergent beneficial search diversity. [ABSTRACT FROM AUTHOR]

The consequences of natural or man-made catastrophes can be devastating. To minimize its impact, it is crucial to carry out a rapid analysis of the affected environment in the moments after they occur, especially from the perspective of alert notification or crisis management. In this context, the use of UAVs, understood as the technological basis on which intelligent systems capable of providing support to rescue teams is built, has positively contributed to face this challenge. In this article the design of a multi-agent architecture which enables the deployment of systems made up of intelligent agents that can monitor environments affected by a catastrophe and provide support to human staff in the decision-making process is proposed. These environments, known in advance, are characterized through a set of points of interests that are critical from the point of view of aerial surveillance and monitoring. To conduct an intelligent information analysis, a formal model of normality analysis is employed, which makes possible the definition of surveillance components. These represent the knowledge bases of the agents responsible for monitoring environments. Likewise, the architecture envisages communication and cooperation mechanisms between the different agents, as the basis for fusing information to assess the overall level of risk of the monitored environment. A case study is presented in which the spread of toxic smoke in an industrial complex which has just suffered a hypothetical earthquake is monitored. [ABSTRACT FROM AUTHOR]

Detection of moving objects by unmanned aerial vehicles (UAVs) is an important application in the aerial transportation system. However, there are many problems to be handled such as high-frequency jitter from UAVs, small size objects, low-quality images, computation time reduction, and detection correctness. This paper considers the problem of the detection and recognition of moving objects in a sequence of images captured from a UAV. A new and efficient technique is proposed to achieve the above objective in real time and in real environment. First, the feature points between two successive frames are found for estimating the camera movement to stabilize sequence of images. Then, region of interest (ROI) of the objects are detected as the moving object candidate (foreground). Furthermore, static and dynamic objects are classified based on the most motion vectors that occur in the foreground and background. Based on the experiment results, the proposed method achieves a precision rate of 94% and the computation time of 47.08 frames per second (fps). In comparison to other methods, the performance of the proposed method surpasses those of existing methods. [ABSTRACT FROM AUTHOR]

The article discusses increasing concerns about drones and other aerial surveillance vehicle options and that airborne vehicle may have beneficial aspect. Topics discussed include large areas could be inspected by aerial surveillance vehicle following natural disasters like storms, earthquakes and hurricanes, need of aerial device have high-resolution cameras and sound detection and communications between a single machine and a human operator.