Cleaning Algorithm for Space-Air-Ground Integration Rescue Data

Quantifying the extent of soil erosion at a fine spatial resolution can be time consuming and costly; however, proximal remote sensing approaches to collect topographic data present an emerging alternative for quantifying soil volumes lost via erosion. Herein we compare terrestrial laser scanning (TLS), and both unmanned aerial vehicle (UAV) and ground photography (GP) structure‐from‐motion (SfM) derived topography. We compare the cost‐effectiveness and accuracy of both SfM techniques to TLS for erosion gully surveying in upland landscapes, treating TLS as a benchmark. Further, we quantify volumetric soil loss estimates from upland gullies using digital surface models derived by each technique and subtracted from an interpolated pre‐erosion surface. Soil loss estimates from UAV and GP SfM reconstructions were comparable to those from TLS, whereby the slopes of the relationship between all three techniques were not significantly different from 1:1 line. Only for the TLS to GP comparison was the intercept significantly different from zero, showing that GP is more capable of measuring the volumes of very small erosion features. In terms of cost‐effectiveness in data collection and processing time, both UAV and GP were comparable with the TLS on a per‐site basis (13.4 and 8.2 person‐hours versus 13.4 for TLS); however, GP was less suitable for surveying larger areas (127 person‐hours per ha−1 versus 4.5 for UAV and 3.9 for TLS). Annual repeat surveys using GP were capable of detecting mean vertical erosion change on peaty soils. These first published estimates of whole gully erosion rates (0.077 m a−1) suggest that combined erosion rates on gully floors and walls are around three times the value of previous estimates, which largely characterize wind and rainsplash erosion of gully walls. Copyright © 2017 John Wiley & Sons, Ltd.

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs’ service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud).

In recent years, unmanned aerial vehicle (UAV) technology has shown great potential for application in hyperspectral image (HSI) classification tasks due to its advantages of flexible scheduling and fast response. However, existing deep learning-based classification algorithms have not thoroughly studied the problems caused by the higher spatial resolution of UAV HSIs. Such as more severe intra-class variation and higher computational resource consumption. These issues limit classification performance. To address these challenges, this paper proposes a lightweight spatial-spectral deformable convolutional neural network (LS<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>DCNet) for UAV HSI classification. This network reduces computational resource usage and data processing time, meeting the fast-response application requirements while ensuring classification performance. First, spatial-spectral deformable convolution (S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>DConv) is designed to construct a lightweight feature extraction network. This not only enhances the adaptive extraction of fine features but also reduces computational resource consumption, and improves response speed. Second, a dynamic labeling-based joint loss (DL-JLoss) is designed to dynamically learn the distribution relationship between data classes. This improves the network’s generalization performance. A more realistic experimental validation was conducted on three UAV HSI datasets using regionally divided training samples, and inference comparisons were performed on embedded devices. The results show that the proposed LS<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>DCNet exhibits a better overall performance in terms of classification accuracy and inference speed.The relevant code can be found at https://github.com/niuroushu/A-Lightweight-Spatial-Spectral-Deformable-CNN-for-UAV-Hyperspectral-Image-Classification.

When solving the path planning problem for Unmanned Aerial Vehicle (UAV) in a three-dimensional complex environment, traditional algorithms often face issues like falling into local optimum easily, insufficient global search ability, poor efficiency and defective optimization result. To address these issues, a three-dimensional path planning method is proposed based on the Starfish Optimization Algorithm (SFOA). This algorithm, inspired by the exploration, preying, and regeneration behaviors of starfish, balances global search and local exploitation, enhancing UAV trajectory planning in complex environments. The study constructs a complex three-dimensional environment model and designs a comprehensive optimization objective by covering constraints like trajectory length, safety, flight height, and smoothness. The trajectory planning framework proposed in this study is designed for pre-mission planning, generating UAV paths offline based on known static terrain and threat information. Comparative experimental results with Ant Colony Optimization and Particle Swarm Optimization show that the SFOA-based UAV trajectory planning achieves significant improvements in comprehensive cost and convergence speed, demonstrating superior global optimization performance. This offers an innovative solution for UAV efficiently and safe trajectory planning in complex environments.

Advancements in phenotyping technology have enabled plant science researchers to gather large volumes of information from their experiments, especially those that evaluate multiple genotypes. To fully leverage these complex and often heterogeneous data sets (i.e. those that differ in format and structure), scientists must invest considerable time in data processing, and data management has emerged as a considerable barrier for downstream application. Here, we propose a pipeline to enhance data collection, processing, and management from plant science studies comprising of two newly developed open-source programs. The first, called AgTC, is a series of programming functions that generates comma-separated values file templates to collect data in a standard format using either a lab-based computer or a mobile device. The second series of functions, AgETL, executes steps for an Extract-Transform-Load (ETL) data integration process where data are extracted from heterogeneously formatted files, transformed to meet standard criteria, and loaded into a database. There, data are stored and can be accessed for data analysis-related processes, including dynamic data visualization through web-based tools. Both AgTC and AgETL are flexible for application across plant science experiments without programming knowledge on the part of the domain scientist, and their functions are executed on Jupyter Notebook, a browser-based interactive development environment. Additionally, all parameters are easily customized from central configuration files written in the human-readable YAML format. Using three experiments from research laboratories in university and non-government organization (NGO) settings as test cases, we demonstrate the utility of AgTC and AgETL to streamline critical steps from data collection to analysis in the plant sciences.

The traditional leader-follower Unmanned Aerial Vehicle (UAV) formation cooperative positioning (CP) algorithm, based on relative ranging, requires at least four leader UAV positions to be known accurately, using relative distance with leader UAVs to achieve the unknown position follower UAV’s high-precision positioning. When the number of the known position leader UAVs is limited, the traditional CP algorithm is not applicable. Aiming at the minimum cooperative unit, which consists of a known position leader UAV and an unknown position follower UAV, this paper proposes a CP method based on the follower UAV’s moving vector. Considering the follower UAV can only acquire the single distance with the leader UAV at each distance-sampling period, it is difficult to determine the follower UAV’s spatial location. The follower UAV’s moving vector is used to construct position observation of the follower UAV’s inertial navigation system (INS). High-precision positioning is achieved by combining the follower UAV’s moving vector. In the process of CP, the leader UAV obtains a high-precision position by an INS/Global Positioning System (GPS) loosely integrated navigation system and transmits its position information to the follower UAV. Based on accurate modeling of the follower UAV’s INS, the position, velocity and heading observation equation of the follower UAV’s INS are constructed. The improved extended Kalman filtering is designed to estimate the state vector to improve the follower UAV’s positioning accuracy. In addition, considering that the datalink system based on radio signals may be interfered with by the external environment, it is difficult for the follower UAV to obtain relative distance information from the leader UAV in real time. In this paper, the availability of the relative distance information is judged by a two-state Markov chain. Finally, a real flight test is conducted to validate the performance of the proposed algorithm.

Nowadays, due to the rapid development of lightweight unmanned aerial vehicles (UAV), remote sensing systems of ultra-high resolution have become available to many researchers. Conventional ground-based measurements for assessing tree stand attributes can be expensive, as well as time- and labor-consuming. Here, we assess whether remote sensing measurements with lightweight UAV can be more effective in comparison to ground survey methods in the case of temperate mixed forests. The study was carried out at the Prioksko-Terrasny Biosphere Nature Reserve (Moscow region, Russia). This area belongs to a coniferous-broad-leaved forest zone. Our field works were carried out on the permanent sampling plot of 1 ha (100×100 m) established in 2016. The coordinates of the plot center are N 54.88876°, E 37.56273° in the WGS 84 datum. All trees with DBH (diameter at breast height) of at least 6 cm (779 trees) were mapped and measured during the ground survey in 2016 (See Fig. 1 and Table 1). Mapping was performed with Laser Technology TruPulse 360B angle and a distance meter. First, polar coordinates of each tree trunk were measured, and then, after conversion to the cartesian coordinates, the scheme of the stand was validated onsite. Species and DBH were determined for each tree. For each living tree, we detected a social status class (according to Kraft). Also for living trees, we measured the tree height and the radii of the crown horizontal projection in four cardinal directions. A lightweight UAV Phantom 4 (DJI-Innovations, Shenzhen, China) equipped with an integrated camera of 12Mp sensor was used for aerial photography in this study. Technical parameters of the camera are available in Table 2. The aerial photography was conducted on October 12, 2017, from an altitude of 68 m. The commonly used mosaic flight mode was used with 90% overlapping both for side and front directions. We applied Agisoft Metashape software for orthophoto mosaic image and dense point cloud building. The canopy height model (CHM) was generated with lidR package in R. We used lasground() function and cloth simulation filter for classification of ground points. To create a normalized dataset with the ground at 0, we used spatial interpolation algorithm tin based on a Delaunay triangulation, which performs a linear interpolation within each triangle, implemented in the lasnormilise() function. CHM was generated according to the pit-free algorithm based on the computation of a set of classical triangulations at different heights. The location and height of individual trees were automatically detected by the function FindTreesCHM() from the package rLIDAR in R. The algorithm implemented in this function is local maximum with fixed window size. Accuracy assessment of automatically detected trees (in QGIS software) was performed through visual interpretation of orthophoto mosaic and comparison with ground survey data. The number of correctly detected trees, omitted by the algorithm and not existing but detected trees were counted. As a result of aerial photography, 501 images were obtained. During these data processing with the Metashape, dense point cloud of 163.7 points / m2 was generated. CHM with 0.5 m resolution was calculated. According to the individual-tree detection algorithm, 241 trees were found automatically (See Fig. 2A). The total accuracy of individual tree detection was 73.9%. Coniferous trees (Pinus sylvestris and Picea abies) were successfully detected (86.0% and 100%, respectively), while results for birch (Betula spp.) required additional treatment. The algorithm correctly detected only 58.2% of birch trees due to false-positive trees (See Fig. 2B and Table 3). These results confirm the published literature data obtained for managed tree stands. Tree heights retrieved from the UAV were well-matched to ground-based method results. The mean tree heights retrieved from the UAV and ground surveys were 25.0±4.8 m (min 8.2 m, max 32.9 m) and 25.3±5.2 m (min 5.9 m, max 34.0 m), respectively (no significant difference, p-value=0.049). Linear regression confirmed a strong relationship between the estimated and measured heights (y=k*x, R2 =0.99, k=0.98) (See Fig. 3A). Slightly larger differences in heights estimated by the two methods were found for birch and pine; for spruce, the differences were smaller (See Fig. 3B and Table 4). We believe that ground measurements of birch and pine height are less accurate than for spruce due to different crown shapes of these trees. So, our results suggested that UAV data can be used for tree stand attributes estimation, but automatically obtained data require validation.

Estimating the transformation between two images from the same scene is a fundamental step for image registration, image stitching and 3D reconstruction. State-of-the-art methods are mainly based on sorted residual for generating hypotheses. This scheme has acquired encouraging results in many remote sensing applications. Unfortunately, mainstream residual based methods may fail in estimating the transform between Unmanned Aerial Vehicle (UAV) low altitude remote sensing images, due to the fact that UAV images always have repetitive patterns and severe viewpoint changes, which produce lower inlier rate and higher pseudo outlier rate than other tasks. We performed extensive experiments and found the main reason is that these methods compute feature pair similarity within a fixed window, making them sensitive to the size of residual window. To solve this problem, three schemes that based on the distribution of residuals are proposed, which are called Relational Window (RW), Sliding Window (SW), Reverse Residual Order (RRO), respectively. Specially, RW employs a relaxation residual window size to evaluate the highest similarity within a relaxation model length. SW fixes the number of overlap models while varying the length of window size. RRO takes the permutation of residual values into consideration to measure similarity, not only including the number of overlap structures, but also giving penalty to reverse number within the overlap structures. Experimental results conducted on our own built UAV high resolution remote sensing images show that the proposed three strategies all outperform traditional methods in the presence of severe perspective distortion due to viewpoint change.

Unmanned aerial vehicles (UAVs) with auto-pilot capabilities are often used for surveillance and patrol. Pilots set the flight points on a map in order to navigate to the imaging point where surveillance or patrolling is required. However, there is the limit denoting the information such as absolute altitudes and angles. Therefore, it is required to set the information accurately. This paper hereby proposes a method to construct environmental symmetric big data using an unmanned aerial vehicle (UAV) during flight by designating the imaging and non-imaging points for surveillance and patrols. The K-Means-based algorithm proposed in this paper is then employed to divide the imaging points, which is set by the pilot, into K clusters, and K imaging points are determined using these clusters. Flight data are then used to set the points to which the UAV will fly. In our experiment, flight records were gathered through an UAV in order to monitor a stadium and the imaging and non-imaging points were set using the proposed method and compared with the points determined by a traditional K-Means algorithm. Through the proposed method, the cluster centroids and cumulative distance of its members were reduced by 87.57% more than with the traditional K-Means algorithm. With the traditional K-Means algorithm, imaging points were not created in the five points desired by the pilot, and two incorrect points were obtained. However, with the proposed method, two incorrect imaging points were obtained. Due to these two incorrect imaging points, the two points desired by the pilot were not generated.

This study firstly presents a survey on basic classifiers namely Minimum Distance Classifier (MDC), Vector Quantization (VQ), Principal Component Analysis (PCA), Nearest Neighbor (NN) and K-Nearest Neighbor (KNN). Then Vector Quantized Principal Component Analysis (VQPCA) which is generally used for representation purposes is considered for performing classification tasks. Some classifiers achieve high classification accuracy but their data storage requirement and processing time are severely expensive. On the other hand some methods for which storage and processing time are economical do not provide sufficient levels of classification accuracy. In both the cases the performance is poor. By considering the limitations involved in the classifiers we have developed Linear Combined Distance (LCD) classifier which is the combination of VQ and VQPCA techniques. The proposed technique is effective and outperforms all the other techniques in terms of getting high classification accuracy at very low data storage requirement and processing time. This would allow an object to be accurately classified as quickly as possible using very low data storage capacity.

Low-cost drones are an emerging technological area that open the horizon for intelligent new Internet-of-Things (IoT) and a host of other applications. Cloud-based online processing of unmanned aerial vehicle (UAV) captured data is an attractive and interesting option toward real-time data processing, as it accommodates viewing and analyzing data from a variety of sources, thereby making data accessibility across devices significantly smooth and easier. This results in a more effective and seamless professional collaborations. Cloud-based solutions also make information technology (IT) management easier by automatically installing software updates, storing backups and eliminating the need for high-specification and expensive computers for data processing and storage. The ability to upload more than one just data-set at a time also provides greater flexibility, while security matters are handled using log-in credentials. In the present work, we discuss a comparative study between two data processing approaches: in one case the data captured by an UAV has been processed using an on-board low-cost single-board computer; in the other case, the captured data has been off-loaded to the cloud for further processing. Processing times between these two approaches are compared, statistical analysis applied, in order to confirm the superiority of the cloud-based processing approach. Challenges inherent with cloud-based processing have also been identified, which will be the subject of future research work.

Goal: Load balancing policies often map workloads on virtual machines, and are being sought to achieve their goals by creating an almost equal level of workload on any virtual machine. In this research, a hybrid load balancing algorithm is proposed with the aim of reducing response time and processing time.&#x0D; Design / Methodology / Approach: The proposed algorithm performs load balancing using a table including the status indicators of virtual machines and the task list allocated to each virtual machine. The evaluation results of response time and processing time in data centers from four algorithms, ESCE, Throttled, Round Robin and the proposed algorithm is done.&#x0D; Results: The overall response time and data processing time in the proposed algorithm data center are shorter than other algorithms and improve the response time and data processing time in the data center. The results of the overall response time for all algorithms show that the response time of the proposed algorithm is 12.28%, compared to the Round Robin algorithm, 9.1% compared to the Throttled algorithm, and 4.86% of the ESCE algorithm.&#x0D; Limitations of the investigation: Due to time and technical limitations, load balancing has not been achieved with more goals, such as lowering costs and increasing productivity.&#x0D; Practical implications: The implementation of a hybrid load factor policy can improve the response time and processing time. The use of load balancing will cause the traffic load between virtual machines to be properly distributed and prevent bottlenecks. This will be effective in increasing customer responsiveness. And finally, improving response time increases the satisfaction of cloud users and increases the productivity of computing resources.&#x0D; Originality/Value: This research can be effective in optimizing the existing algorithms and will take a step towards further research in this regard.

This study explores the application of Particle Swarm Optimization (PSO) in Unmanned Aerial Vehicle (UAV) path planning within a simulated three-dimensional environment. UAVs, increasingly prevalent across various sectors, demand efficient navigation solutions that account for dynamic and unpredictable elements. Traditional pathfinding algorithms often fall short in complex scenarios, hence the shift towards PSO, a bio-inspired algorithm recognized for its adaptability and robustness. We developed a Python-based framework to simulate the UAV path planning scenario. The PSO algorithm was tasked to navigate a UAV from a starting point to a predetermined destination while avoiding spherical obstacles. The environment was set within a 3D grid with a series of waypoints, marking the UAV's trajectory, generated by the PSO to ensure obstacle avoidance and path optimization. The PSO parameters were meticulously tuned to balance the exploration and exploitation of the search space, with an emphasis on computational efficiency. A cost function penalizing proximity to obstacles guided the PSO in real-time decision-making, resulting in a collision-free and optimized path. The UAV's trajectory was visualized in both 2D and 3D perspectives, with the analysis focusing on the path's smoothness, length, and adherence to spatial constraints. The results affirm the PSO's effectiveness in UAV path planning, successfully avoiding obstacles and minimizing path length. The findings highlight PSO's potential for practical UAV applications, emphasizing the importance of parameter optimization. This research contributes to the advancement of autonomous UAV navigation, indicating PSO as a viable solution for real-world path planning challenges.

Recently, the unmanned aerial vehicles (UAVs) have been widely used in real-time sensing applications over cellular networks. The performance of a UAV is determined by both its sensing and transmission processes, which are influenced by the trajectory of the UAV. However, it is challenging for the UAV to determine its trajectory, since it works in a dynamic environment, where other UAVs determine their trajectories dynamically and compete for the limited spectrum resources in the same time. To tackle this challenge, we adopt the reinforcement learning to solve the UAV trajectory design problem in a decentralized manner. To coordinate multiple UAVs performing real-time sensing tasks, we first propose a sense-and-send protocol, and analyze the probability for successful valid data transmission using nested Markov chains. Then, we propose an enhanced multi-UAV Q-learning algorithm to solve the decentralized UAV trajectory design problem. Simulation results show that the proposed algorithm converges faster and achieves higher utilities for the UAVs, compared to traditional singleand multi-agent Q-learning algorithms.