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The necessity of dynamic management of a large number of equipment and personnel performing the construction, repair, prevention and screening measures on pipelines makes the task of «snap» of moving object geographical coordinates to the linear part of the main pipeline urgent. The main aim of the study is to develop the linear mapping technique of the moving object geographical coordinates to the linear part of the main pipeline, based on the data on a discrete set of its specific geographical origin. The methods used in the study: vector algebra, geometry of the Earth spheroid, arithmetic error, heuristic methods of direct search methods of data compression, linear interpolation functions. The results. The authors have stated the problem of linear mapping coordinates of a moving object to the linear part of the main pipeline. The paper describes the methods of solving this problem based on the data on a discrete set of geographical coordinates of kilometer stretch of the linear part of the main pipeline and introduces the results of using the method for converting the data of aerovisual observation of the site of the linear part of the main pipeline «Aleksandrovsky - Anzhero-Sudzhensk»; considers the interpolation properties of the technique with a lack of information on the geographical coordinates of kilometer stretch of the linear part of the main pipeline. The authors estimated the error of the method based on these assumptions, which does not take into account the elliptical shape of the earth in the calculation of the distance between two points on the earth's surface. The estimation error for the data of aerovisual observation site of the linear part of the main pipeline «Aleksandrovsky - Anzhero-Sudzhensk» was calculated. The authors proposed the method for processing information on a set of geographical coordinates of the mobile object based on the limit value of the object distance from the axis of the linear part of the main pipeline for the given configuration of pipelines security zones. It is shown that the sharing of the developed technique of linear mappings and data processing method makes it possible to compress the data on the trajectory of the moving object after converting them into a variety of areas surveyed of the linear part of the main pipeline.

Vehicle detection in aerial images is a crucial image processing step for many applications such as screening of large areas as used for surveillance, reconnaissance, or rescue tasks. In recent years, several deep learning-based frameworks have been proposed for object detection. However, these detectors were developed for data sets that considerably differ from aerial images. In this paper, we systematically investigate the potential of fast R-CNN and faster R-CNN for aerial images, which achieve top performing results on common detection benchmark data sets. Therefore, the applicability of eight state-of-the-art object proposal methods used to generate a set of candidate regions and of both detectors is examined. Relevant adaptations to account for the characteristics of the aerial images are provided. To overcome the shortcomings of the original approach in the case of handling small instances, we further propose our own networks that clearly outperform state-of-the-art methods for vehicle detection in aerial images. Furthermore, we analyze the impact of the different adaptations with respect to various ground sampling distances to provide a guideline for detecting small objects in aerial images. All experiments are performed on two publicly available data sets to account for differing characteristics such as varying object sizes, number of objects per image, and varying backgrounds. [ABSTRACT FROM AUTHOR]

A framework is introduced for planning unmanned aerial vehicle (UAV) flight paths for visual surveillance of ground targets, each having particular viewing requirements. Specifically, the framework is designed for instances in which each target is associated with a set of imaging parameters, including a desired: (i) tilt angle, (ii) azimuth, with the option of a 360deg view, and (iii) dwell-time. Tours are sought to image the targets, while minimizing both the total mission time and the time required to reach the initial target. An -constraint scalarization is used to pose the multi-objective problem as a constrained optimization, which, through careful discretization, can be approximated as a discrete graph-search. It is shown that, in many cases, this approximation is equivalent to a generalized traveling salesperson problem (GTSP). A heuristic procedure for solving the discrete approximation and recovering solutions to the full routing problem is presented and illustrated through numerical studies. [ABSTRACT FROM AUTHOR]

Wireless communication platforms of unmanned aerial vehicles (UAVs), commonly known as drones, usually operate in dynamic conditions with highly fluctuating capacities and erratic wireless access to the network resources. Effective design for adequate, robust, and consistent communications from the drones to the control center (aka server) is thus necessary to facilitate the implementation of the UAV system. Multipath TCP has the potential to exploit heterogeneous wireless paths and achieve robust bandwidth by controlling the dynamics of the convoy of drones. In this paper, we make use of fluid approach for proposing a generic system architecture toward a control mechanism for coordinating the convoy of drones. Moreover, we model multipath TCP and study their applications for the control scenario of a convoy of drones with three different communication interfaces, representing the head, tail, and the middle of the convoy. The access is through a satellite and several WiFi access points (APs) communicating to the server with time-variant wireless channel, subject to the dynamic mobility and distance of APs from the convoy's head and tail. The traffic on different paths governed by the flow control based on the movement and position of the convoy enable us to investigate the performance and convergence of communication system dynamics. In particular, Lyapunov theory is adopted to derive the stability conditions of the dynamics over the mobility of the convoy. [ABSTRACT FROM AUTHOR]

Achieving the autonomous deployment of aerial robots in unknown outdoor environments using only onboard computation is a challenging task. In this study, we have developed a solution to demonstrate the feasibility of autonomously deploying drones in unknown outdoor environments, with the main capability of providing an obstacle map of the area of interest in a short period of time. We focus on use cases where no obstacle maps are available beforehand, for instance, in search and rescue scenarios, and on increasing the autonomy of drones in such situations. Our vision‐based mapping approach consists of two separate steps. First, the drone performs an overview flight at a safe altitude acquiring overlapping nadir images, while creating a high‐quality sparse map of the environment by using a state‐of‐the‐art photogrammetry method. Second, this map is georeferenced, densified by fitting a mesh model and converted into an Octomap obstacle map, which can be continuously updated while performing a task of interest near the ground or in the vicinity of objects. The generation of the overview obstacle map is performed in almost real time on the onboard computer of the drone, a map of size 100m×75m is created in ≈2.75min, therefore, with enough time remaining for the drone to execute other tasks inside the area of interest during the same flight. We evaluate quantitatively the accuracy of the acquired map and the characteristics of the planned trajectories. We further demonstrate experimentally the safe navigation of the drone in an area mapped with our proposed approach. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle in surveillance and reconnaissance operations are well known with its flight operations confining to the lateral and longitudinal plane where the altitude is maintained constant. The objective in these operations is to have a wide field of view when camera is mounted underneath the aircraft. However, in obstacle detection algorithms as well as in weapon-target assignments by an unmanned combat air vehicle, the sensors such as cameras and radars are mounted along the X direction of the body fixed frame. The unmanned vehicles with such sensor locations are expected to offer obstacle-camera and weapon-target line-of-sight engagements. These operations by the unmanned vehicles are referred as targeting tasks. In this paper, a procedure to use unmanned vehicles as targeting systems in pitch plane is presented with respect to a fixed order controller at the inner loop. The remote pilot's inputs at the outer loop is disengaged and to mimic these inputs, various flight path angles are scheduled thus depicting autonomous characteristics. Illustrations to depict targeting tasks are provided. [ABSTRACT FROM AUTHOR]

Strava, a popular social media platform and mobile app like Facebook but specifically designed for athletes, posts a "heatmap" with consensually-obtained details about users' workouts and geolocation. Strava's heatmap depicts aggregated data of user location and movement by synthesizing GPS satellite data points and movement data from users' smart devices together with satellite imagery. In January of 2018, a 20- year-old student tweeted that Strava's heatmap revealed U.S. forward operating bases. The tweet revealed a significant national security issue and flagged substantial privacy and civil liberty concerns. Smart devices, software applications, and social media platforms aggregate consumer data from multiple data collection sources, including device-embedded sensors, cameras, software, and GPS chips, as well as from consumer activities like social media posts, pictures, texts, email, and contacts. These devices and apps utilize satellite data, including GPS, as a fundamental component of their data collection arsenal. We call this little understood, across-device, across-platform, and multi-sourced data aggregation the satellite-smart device information nexus. Given the nature of the technology and data aggregation, no one escapes the satellite and smart device information nexus. We explain the technology behind both satellites and smart devices, and we examine how the satellite-smart device information nexus works. We also address how private industry's aggregation of data through this nexus poses a threat to individual privacy, civil liberties, and national security. In so doing, we work to fill a marked gap in the privacy and cyberrelated legal literature when it comes to analyzing the technology, surveillance capabilities, law, and regulation behind government and commercial satellites together with private industry's aggregation, use, and dissemination of geolocation and other data from the satellite-smart device information nexus. This lack of awareness about the satellite-smart device information nexus has adverse consequences on individual privacy, civil liberties, and the security of nation states; it impedes informed legislation; and it leaves courts in the dark. A contributing factor to the lack of awareness is that commercial remote sensing and government satellites are regulated by a byzantine scheme of international laws, treaties, organizations, and domestic nation states' laws that combine to control access to satellite data, sharing of satellite data, licensing, ownership, positioning in space, technical requirements, technical restrictions, and liability for harm caused by satellites. Although the satellite-smart device information nexus involves staggering quantities of personal information, we examine how the nexus falls outside the U.S. electronic surveillance and data legislative scheme and why it is unimpeded by privacy decisions due to a disconnect in U.S. Supreme Court decisions treating aerial surveillance differently than location tracking. We breakdown the complex yet opaque regulatory structure governing commercial remote sensing and government satellites. We examine why the Strava event and others like it are--and will continue to be--the new norm, absent significant legislative and regulatory change. We conclude by providing a suggested roadmap for that legislative and regulatory change. [ABSTRACT FROM AUTHOR]

Recently, drone technology has developed rapidly for various purposes. A drone is very useful for aerial surveillance due to its remote sensing capability. Multiple target detection and tracking are essential to recognize any harmful threats in advance, however the image captured at a distance is easily degraded due to blurring and noise as well as low resolution. This paper addresses the detection and tracking of moving vehicles with drone imaging. A drone captures video sequences of multiple moving vehicles from a distance. Cars and buses are the objects of interests driving on urban roads. The detection step consists of frame difference followed by thresholding and morphological operation considering the size of region of interest (ROI). The centroids of the ROI's are considered measurements for tracking. Tracking is performed with interacting multiple model (IMM) filtering, which estimate the state of vectors and covariance matrices using multiple modes of Kalman filtering. The measurements in the validation region are associated with established tracks by the nearest neighbor rule. In the experiment, total seven moving cars and buses are captured at a long distance by a drone. It will be shown that the proposed method well detects the moving vehicles and achieves a good accuracy in estimating their locations. [ABSTRACT FROM AUTHOR]

Abstract Agricultural land now exceeds forests as the dominant global biome. Because of their global dominance, and potential expansion or loss, methods to estimate biomass and carbon in agricultural areas are necessary for monitoring global terrestrial carbon stocks and predicting carbon dynamics. Agricultural areas in the tropics have substantial tree cover and associated above ground biomass (AGB) and carbon. Active remote sensing data, such as airborne LiDAR (light detection and ranging), can provide accurate estimates of biomass stocks, but common plot-based methods may not be suitable for agricultural areas with dispersed and heterogeneous tree cover. The objectives of this research are to quantify AGB of a tropical agricultural landscape using a tree-based method that directly incorporates the size of individual trees, and to understand how landscape estimates of AGB from a tree-based method compare to estimates from a plot-based method. We use high-resolution (1.12 m) airborne LiDAR data collected on a 9280-ha region of the Azuero Peninsula of Panama. We model individual tree AGB with canopy dimensions from the LiDAR data. We apply the model to individual tree crown polygons and aggregate AGB estimates to compare with previously developed plot-based estimates. We find that agricultural trees are a distinct and dominant part of our study site. The tree-based approach estimates greater AGB in pixels with low forest cover than the plot-based approach, resulting a 2-fold difference in landscape AGB estimates between the methods for non-forested areas. Additionally, one third of the total landscape AGB exists in areas having <10% cover, based on a global tree cover product. Our study supports the continued use and development of allometric models to predict individual tree biomass from LiDAR-derived canopy dimensions and demonstrates the potential for spatial information from high-resolution data, such as relative isolation of canopies, to improve allometric models. Graphical abstract Unlabelled Image Highlights • Substantial tree biomass exists in areas with zero to little forest cover. • Landscape estimates from a tree-based method capture biomass of trees outside forests. • The plot-based method underestimates biomass in low forest areas relative to tree-based estimates. • The degree of tree canopy isolation influences the allometry of tree size with biomass. [ABSTRACT FROM AUTHOR]

Abstract This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45 % of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then aggregated to Plant Functional Types (PFTs). Next, the spatial abundance of PFTs at MODIS resolution (500 m) is calculated using Landsat data (30 m). Based on these PFT abundances, representative trait values are calculated for MODIS pixels with nearby trait data. Finally, different regression algorithms are applied to globally predict trait estimates from these MODIS pixels using remote sensing and climate data. The methods were compared in terms of precision, robustness and efficiency. The best model (random forests regression) shows good precision (normalized RMSE≤ 20 %) and goodness of fit (averaged Pearson's correlation R = 0.78) in any considered trait. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales. The new data products – the gap-filled trait matrix, a global map of PFT abundance per MODIS gridcells and the high-resolution global leaf trait maps – are complementary to existing large-scale observations of the land surface and we therefore anticipate substantial contributions to advances in quantifying, understanding and prediction of the Earth system. Highlights • Presented a modular process chain for plant trait mapping including local effects • High-resolution global maps of leaf traits by fusing measured trait data, LANDSAT and MODIS • Scope for testing and parameterizing trait-enabled Earth System models • Implications for land management and Earth system science applications [ABSTRACT FROM AUTHOR]

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.

• A new dataset is introduced for human action recognition in aerial videos. • Proposed dataset is annotated with 13 different actions with a total of 55,477 frames and almost 400,000 annotations. • A modified ResNeXt101 (M-ResNext101) is proposed for recognizing the human actions from aerial videos. Human actions being diverse in nature cannot be generalized, thus making it quite difficult to train a machine to recognize such diversified actions. This challenge is further compounded by the lack of availability of datasets for aerial surveillance, as collecting and annotating a large dataset is a formidable task. This paper aims to solve the problem of data scarcity by introducing a new dataset, Aeriform in-action for recognizing human actions from aerial videos. The proposed dataset consists of 32 high-resolution videos containing 13 action classes with 55,477 frames (without augmentation) and almost 400,000 annotations. It includes complex and aggressive actions such as kicking and punching, as well as drone signaling actions like waving and handshaking. The dataset also includes human-object interactions like carrying and reading. In addition to the dataset, this paper also presents a two-step deep learning framework for recognizing human actions based on the integration of human detection and action recognition module. The action recognition module adopts a modified version of the ResNeXt101 architecture (M-ResNext101) to recognize human actions in aerial videos. The performance of the proposed M-ResNext101 model is compared with 13 other deep learning models, and it outperforms all of them with an accuracy of 76.44% on the test data. The proposed dataset for human action recognition in aerial videos is available on https://surbhi-31.github.io/Aeriform-in-action/. [ABSTRACT FROM AUTHOR]

JNIM said that on July 8th in its first statement that militants clashed with "Burkinabe militias" in Tenkodogo commune, Boulgou province and that seven enemy fighters were killed. JNIM also claimed in an earlier statement the same day to have killed 15 Burkinabe militiamen following clashes around the town of Sangha, near the border between Burkina Faso and B Togo b on June 26th. B Traore is bullish in defending a key plank of counterinsurgency operations. b Interim President Capt. B Ibrahim Traore b defended on July 28th the use of state-backed self-defence militia Volunteers for Defence of Fatherland ( B VDP b s) in counterinsurgency operations, Burkinabe news site I Sidwaya i reported. [Extracted from the article]

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: Underwater archaeological work at Soli‐Pompeiopolis in 2017 has challenged previously published descriptions of the harbour works. Excavation, sonar survey, and aerial drone survey have provided an asymmetric plan of the harbour moles with an entrance to the east, which reflects local knowledge of the wind and wave conditions. The presence of beachrock blocking the harbour is explained by Beaufort's depiction in 1811–1812 of a defunct sluice, no longer visible on the site. [ABSTRACT FROM AUTHOR]

The article reports that monitoring of colonial waterbird breeding at a large spatial scale has traditionally been done using aerial surveys. It notes that data collection takes time, affected by limitations on the conditions for collection of useful UAV data. It adds that data from UAVs are not a panacea for surveys, often requiring on-ground supportive data, to ensure that advice for management is not misplaced and affects conservation outcomes.

Much of the world's temperate grasslands have been converted to croplands, yet these trends can reverse in some regions. This is the case for the steppes of northern Kazakhstan, where the breakdown of the Soviet Union led to widespread cropland abandonment, creating restoration opportunities. Understanding when abandonment happened and whether it persists is important for making use of these opportunities. We developed a trajectory-based change detection approach to identify cropland abandonment between 1988 and 2013 and recultivation between 1991 and 2013. Our approach is based on annual time series of cropland probabilities derived from Landsat imagery and resulted in reliable maps (89% overall accuracy), with abandonment being detected more accurately (user's accuracy of 93%) than recultivation (73%). Most of the remaining uncertainty in our maps was due to low image availability during the mid-1990s, leading to abandonment in the 1990s sometimes only being detected in the 2000s. Our results suggest that of the ~4.7 million ha of cropland in our study area in 1985, roughly 40% had been abandoned by 2013. Knowing the timing of abandonment allowed for deeper insights into what drives these dynamics: recultivation after 2007 happened preferentially on those lands that had been abandoned most recently, suggesting that the most productive croplands were abandoned last and recultivated first. Likewise, knowing the timing of abandonment allowed for more precise estimates of the environmental impacts of abandonment (e.g., soil organic carbon sequestration estimated at 16.3 Mt. C compared to 24.0 Mt. C when assuming all abandonment happened right after the breakdown of the Soviet Union, with the uncertainty around emission estimates decreasing by 63%). Overall, our study emphasizes the value of the Landsat archive for understanding agricultural land-use dynamics, and the opportunities of trajectory-based approaches for mapping these dynamics. [ABSTRACT FROM AUTHOR]

Change detection using multi-sensor remote sensing images, such as synthetic aperture radar (SAR) and optical images, is poorly researched and thus remains a challenging task. In this study, we address this problem by proposing a novel automatic change detection method. Different sensors have completely different physical principles. Thus, the resulting multi-sensor images have completely different radiometric values. First, we introduce a sorted histogram concept that sorts the bins in descending order, noticing that multi-sensor images with absence of change have the same combination of objects, and each object in different images has the same proportions and a unique range of grey values. The sorted histogram discards the visual property correspondence between images and is capable of capturing the local internal image layout. Then, various histogram-based distances are employed to estimate the distance between each sorted histogram pair. After the whole image has been analysed, we obtain a divergence index map. The sorted histogram not only has the theoretical advantage of robustness in the intensity variations in multi-sensor images but also the practical advantage of low computational complexity compared with existing methods. Experiments on SAR and optical datasets with different resolutions show promising results in terms of detection capability and run time. [ABSTRACT FROM AUTHOR]

This study is aimed at demonstrating the application of vegetation spectral techniques for detection and monitoring of the impact of oil spills on vegetation. Vegetation spectral reflectance from Landsat 8 data were used in the calculation of five vegetation indices (normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), adjusted resistant vegetation index 2 (ARVI2), green-infrared index (G-NIR) and green-shortwave infrared (G-SWIR) from the spill sites (SS) and non-spill sites (NSS) in 2013 (pre-oil spill), 2014 (oil spill date) and 2015 (post-oil spill) for statistical comparison. The result shows that NDVI, SAVI, ARVI2, G-NIR and G-SWIR indicated a certain level of significant difference between vegetation condition at the SS and the NSS in December 2013. In December 2014 vegetation conditions indicated higher level of significant difference between the vegetation at the SS and NSS as follows where NDVI, SAVI and ARVI2 with p-value 0.005, G-NIR - p-value 0.01 and G-SWIR p-value 0.05. Similarly, in January 2015 a very significant difference with p-value <0.005. Three indices NDVI, ARVI2 and G-NIR indicated highly significant difference in vegetation conditions with p-value <0.005 between December 2013 and December 2014 at the same sites. Post-spill analysis shows that NDVI and ARVI2 indicated low level of significance difference p-value <0.05 suggesting subtle change in vegetation conditions between December 2014 and January 2015. This technique may help with the real time detection, response and monitoring of oil spills from pipelines for mitigation of pollution at the affected sites in mangrove forests. [ABSTRACT FROM AUTHOR]

Identifying and mapping tropical trees at the species level from space can support an improved assessment of forest composition, forest carbon uptake, tree species distribution and preferred habitat as well as a better understanding of the response of forests to climate change. In this study, the development of a validated data and image-processing schema demonstrated the capability of current metre-scale satellite technology (WorldView-3) to identify specific tree species within an unmanaged tropical forest. The experimental site, La Selva Biological Station in Costa Rica, provided access for field validation and spectral data acquisition of individual tree canopies from established canopy towers. It is also a representative biome of diverse lowland Atlantic tropical forests in Central America. The process defined in this paper calibrated and corrected field-acquired ASD field spectra for ten tree species and corrected WorldView-3 image data for viewing and illumination geometry. In addition, assessments of three current atmospheric compensation methods for correcting recent WorldView-3 satellite imagery established the most accurate compensation process for a tropical forest setting. Corrected reflectance in the satellite data matched the spectrometer data to ±0.25% for visible bands and ±0.5% for near-infrared bands. This study shows that spectral data from the satellite and field spectrometer data are nearly equivalent when applying the appropriate atmospheric compensation, band response emulation, and viewing correction processes established in this study. [ABSTRACT FROM AUTHOR]

Spatially distributed vegetation coefficients () data with high temporal resolution are in demand for actual evapotranspiration estimation, crop condition assessment, irrigation scheduling, etc. Traditional remotely sensed based data application gets hindered because of two main reasons i.e 1) spectral reflectance based accounts only for transpiration factor, but fails to account for total evapotranspitration. 2) required optical spectral reflectances are available only during clear sky conditions, which creates gaps in the data. Hence there is a necessity of a model which accounts for both transpiration and evpaoration factors and also for a gap filling method, which can produce accurate continuous quantification of values. Therefore, in this study, different combinations of enhanced vegetation index (EVI), global vegetation moisture index (GVMI) and temperature vegetation dryness index (TVDI) have been employed in linear and non linear regression techniques to obtain the best model. To fill the gaps in the data, initially, temporal fitting of values have been examined using Savitsky-Goley (SG) filter for 3 years of data (2012-2014), but this fails when sufficient high quality values are unavailable. In this regard, three gap filling techniques namely regression, artificial neural networks (ANN) and interpolation techniques have been employed over Cauvery basin. Microwave polarization difference index (MPDI) has been employed in ANN technique to estimate values under cloudy sky conditions. The results revealed that the combination of GVMI and TVDI using linear regression technique performed better than other combinations with correlation coefficient (r) and root mean square error (RMSE) values of 0.824 and 0.204 respectively. Furthermore, the results indicated that SG filter can be used for temporal fitting and for gap filling regression technique performed better than other techniques with the r and RMSE values of 0.68 and 0.25 for Berambadi station. [ABSTRACT FROM AUTHOR]

FRONTEX has highlighted Remotely Piloted Aircraft Systems (RPAS) as affordable and efficient capabilities for securing the EU’s vast frontiers in order to further upgrade them into smart technological borders. In this regard, this article examines the EU’s strategy and rationalisations to develop dual-use technologies such as aerial surveillance drones for border management. By drawing on critical security and technology studies and by focusing on their functional technological efficiency, the article argues that drones are being normalised in a technological regime of exclusion at the border-zone. It further contends that high-end technologies such as drones introduce a military bias as security enablers in border surveillance and as a panacea for the consequences of failed policies to manage irregular migration. A closer examination of several EU-endorsed drone projects reveals a pragmatic and industry-driven approach to border security, underlining the evolving homogenisation between internal and external security and the imminent “dronisation” of European borders. [ABSTRACT FROM AUTHOR]

The development, benchmarking and validation of aerial Persistent Surveillance (PS) algorithms requires access to specialist Wide Area Aerial Surveillance (WAAS) datasets. Such datasets are difficult to obtain and are often extremely large both in spatial resolution and temporal duration. This paper outlines an approach to the simulation of complex urban environments and demonstrates the viability of using this approach for the generation of simulated sensor data, corresponding to the use of wide area imaging systems for surveillance and reconnaissance applications. This provides a cost-effective method to generate datasets for vehicle tracking algorithms and anomaly detection methods. The system fuses the Simulation of Urban Mobility (SUMO) traffic simulator with a MATLAB controller and an image generator to create scenes containing uninterrupted door-to-door journeys across large areas of the urban environment. This ‘pattern-of-life’ approach provides three-dimensional visual information with natural movement and traffic flows. This can then be used to provide simulated sensor measurements (e.g. visual band and infrared video imagery) and automatic access to ground-truth data for the evaluation of multi-target tracking systems. [ABSTRACT FROM AUTHOR]

Fixed-wing unmanned aerial vehicles (UAVs) can be an essential tool for low-cost aerial surveillance and mapping applications in remote regions. There is, however, a key limitation, which is the fact that low-cost UAVs have limited fuel capacity and, hence, require periodic refueling to accomplish a mission. Moreover, the usual mechanism of commanding the UAV to return to a stationary base station for refueling can result in the fuel wastage and inefficient mission operation time. Alternatively, one strategy could be the use of an unmanned ground vehicle (UGV) as a mobile refueling unit, where the UAV will rendezvous with the UGV for refueling. In order to accurately perform this task in the presence of wind disturbances, we need to determine an optimal trajectory in three-dimensional taking UAV and UGV dynamics and kinematics into account. In this paper, we propose an optimal control formulation to generate a tunable UAV trajectory for rendezvous on a moving UGV that also addresses the possibility of the presence of wind disturbances. By a suitable choice of the value of an aggressiveness index that we introduce in our problem setting, we are able to control the UAV rendezvous behavior. Several numerical results are presented to illustrate the reliability and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In the surveillance of interested regions by unmanned aerial vehicle (UAV), systemperformance relies greatly on the motion control strategy of the UAV and the operation characteristics of the onboard sensors. This paper investigates the 2D path planning problem for the lightweight UAV synthetic aperture radar (SAR) system in an environment of multiple regions of interest (ROIs), the sizes of which are comparable to the radar swath width. Taking into account the special requirements of the SAR system on the motion of the platform, we model path planning for UAV SAR as a constrained multiobjective optimization problem (MOP). Based on the fact that the UAV route can be designed in the map image, an image-based path planner is proposed in this paper. First, the neighboring ROIs are merged by the morphological operation. Then, the parts of routes for data collection of the ROIs can be located according to the geometric features of the ROIs and the observation geometry of UAV SAR. Lastly, the route segments for ROIs surveillance are connected by a path planning algorithm named the sampling-based sparse A* search (SSAS) algorithm. Simulation experiments in real scenarios demonstrate that the proposed sensor-oriented path planner can improve the reconnaissance performance of lightweight UAV SAR greatly compared with the conventional zigzag path planner. [ABSTRACT FROM AUTHOR]

If Big Brother made movies, persistent aerial surveillance would be its masterpiece. Small airplanes are rigged with high-tech cameras that can continuously transmit real-time images to the ground. The aircraft is able to monitor an area of thirty square miles for ten hours at a time. This technology allows video analysts to zoom in and track the location of vehicles, and even people. It was originally designed for military use during the Iraq War, but since then, it has been adapted for civilian applications. In 2016, the Baltimore Police Department contracted with Persistent Surveillance Systems to carry out a trial run of aerial surveillance over the city. The public was not informed that they were being watched every day. The Supreme Court has long held that aerial surveillance itself does not constitute a search for the purposes of the Fourth Amendment. The persistency of this new kind of reconnaissance changes the calculus. Specialized airplanes enable law enforcement agencies to survey sizable regions for hours on end. It is precisely this power that makes persistent aerial surveillance more like constant GPS monitoring, which the Court has already considered a search. These modes of long-term observation are intrusive and violate a reasonable expectation of privacy. Police action must be analyzed over time as a collective sequence of steps, not just an individual instance. The aggregate search can qualify under the Fourth Amendment, even if the individual steps did not. This is because prolonged surveillance reveals privacies and intimate details of life that short-term surveillance does not. Repetition, indeed habit, are cornerstones of personality and identity. People may reasonably expect some form of surveillance. People may also expect those observations to remain disconnected and nondescript. This technology presents intriguing opportunities for law enforcement departments, in the investigation of crime, the presentation of evidence at trial, deterrence and crime reduction, exonerating the wrongfully convicted, and even traffic management and highway control. These advantages are not enough--and will never be enough--to avoid the command of the Constitution. Over time, the public has become more accepting of the surveillance state. The Constitution remains a counter-majoritarian check on the government. Absent probable cause and a warrant, persistent aerial surveillance is an unreasonable search that violates the Fourth Amendment. [ABSTRACT FROM AUTHOR]

Compensating for distortions in pushbroom satellite imagery has a bearing on subsequent earth observation applications. Traditional distortion correction methods usually depend on ground control points (GCPs) acquired from a high-accuracy geometric calibration field (GCF). Due to the high construction costs and site constraints of GCF, it is difficult to perform distortion detection regularly. To solve this problem, distortion detection methods without using GCPs have been proposed, but their application is restricted by rigorous conditions, such as demanding a large amount of calculation or good satellite agility which are not met by most remote sensing satellites. This paper proposes a novel method to correct interior distortions of satellite imagery independent of GCPs. First, a classic geometric calibration method for pushbroom satellite is built and at least three images with overlapping areas are collected, then the forward intersection residual between corresponding points in the images are used to calculate interior distortions. Experiments using the Gaofen-1 (GF-1) wide-field view-1 (WFV-1) sensor demonstrate that the proposed method can increase the level of orientation accuracy from several pixels to within one pixel, thereby almost eliminating interior distortions. Compared with the orientation accuracy by classic GCF method, there exists maximum difference of approximately 0.4 pixel, and the reasons for this discrepancy are analyzed. Generally, this method could be a supplementary method to conventional methods to detect and correct the interior distortion. [ABSTRACT FROM AUTHOR]