Integration of modern remote sensing technologies for faster utility mapping and data extraction

This article aimed to determine the effectiveness of (Machine Learning, Artificial Neural Networks, Pattern Recognition and Data Analysis, and Fire Dynamics Simulations) in improving the results of arson investigations in the United Arab Emirates (UAE). The research relied on the descriptive method. The study community consisted of all members of the teams specialized in fire investigations. The research sample was taken using a simple random method that included (74) individuals from the research community. The researcher relied on the questionnaire as the main instrument for collecting data from the study sample. The results of the study showed that artificial intelligence techniques (Machine Learning, Artificial Neural Networks, Pattern Recognition and Data Analysis, and Fire Dynamics Simulations) can be effective in improving the results of arson investigations. The study recommended providing training programs to investigators on how to make full use of AI techniques in the investigation process.

Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV)

With the wide application of unmanned aerial vehicles (UAVs), UAV thermal imaging technology has been brought to the attention of numerous experts and scholars. At present, the application of UAV thermal imaging technology is limited by the problem of low resolution of the images captured by the thermal imaging system itself. In order to solve this problem, this paper proposes a UAV thermal image super-resolution reconstruction method based on bimodal image feature fusion, which improves the UAV thermal image super-resolution characterization capability by making use of the feature information provided by the UAV visible image to compensate for the lack of texture and edge information perception in thermal images. The proposed methodology consists of two tasks: The first task involves a bimodal image feature fusion network that extracts texture and edge information from visible images. This addresses the lack of detailed feature information in thermal images with a differential complementary modal feature fusion module, ultimately producing a fused image with high-quality feature information. The second task entails the integration of a gated multilevel feature fusion module into the UAV thermal image reconstruction network as an attention mechanism, thereby improving the use of fused image feature information for generating high-resolution UAV thermal images. Finally, a joint adaptive training strategy is utilized to further enhance the super-resolution effect of UAV thermal images. The experimental results show that the proposed method can effectively extract the visible image information to compensate for the characterization information in the UAV thermal image super-resolution, and achieves the optimal values of 31.53 dB and 0.9116 in PSNR and SSIM metrics, respectively, providing a new solution for the thermal image super-resolution of UAVs.

This study presents an advanced approach to modeling Unmanned Aerial Vehicle (UAV) dynamics by integrating Artificial Neural Networks (ANNs) and the Output Error Method (OEM). Moreover, the research analyzes the longitudinal flight characteristics of the Multiplex FunCub R/C, using data from designed flight tests complemented by simulations incorporating sensor noise and drift effects. Furthermore, the study captures a comprehensive range of aerodynamic responses essential for precise system identification by employing a multistep elevator input signal. In addition, the OEM approach, a traditional parameter estimation method, offers robust statistical estimation by minimizing the discrepancies between measured and predicted outputs. However, due to the nonlinear complexities inherent in UAV flight dynamics, the study also explores ANNs, leveraging their capability to model intricate nonlinear behaviors without requiring predefined aerodynamic parameters. Subsequently, the performance of both methodologies is critically evaluated against measured aerodynamic coefficients, revealing ANNs’ superior adaptability in accurately predicting complex aerodynamic interactions compared to OEM. Consequently, results indicate notable reductions in relative error, particularly in challenging aerodynamic coefficients. Overall, this research not only highlights the comparative advantages of ANNs in UAV system identification but also lays an initial structure for future advancements in UAV modeling and control systems.

Unmanned aerial vehicles (UAVs) provide rich thermal imagery data crucial for vision-related tasks such as feature extraction, image enhancement, and data synthesis. Acquiring high-resolution (HR) thermal images for UAVs remains challenging due to sensor hardware constraints. Guided super-resolution (SR) methods leverage HR visible images to enhance thermal images reconstruction from low-resolution (LR) inputs. However, these methods demonstrate limitations in restoring fine grained textural details. To solve the problem, we propose a hierarchical cross-modal mamba with multi-head reconstruction network (HCM-Mamba) for UAV thermal images SR. The framework introduces a residual state-space block (RSSB) that directly models long-range spatial dependencies, enabling robust contextual correlation learning for sparse, small targets in complex aerial scenes. To alleviate the problem of modal differences between thermal and visible images, we propose a cross-modal integration framework, which includes a hierarchical cross-modal bridging encoder (HCB-Encoder) (interaction, refinement, and enhancement). The complementary features are hierarchically aligned and integrated through a dual attention mechanism and multi-scale aggregation. In order to address the problems of structure distortion and detail loss in traditional end-to-end architectures, we propose a multi head collaborative reconstruction mechanism that ensures coherent optimization of global structure and local texture while decoupling the feature learning process. Extensive experiments have shown that HCM-Mamba has achieved significant improvements in multiple evaluation metrics, maintaining structure integrity, and restoring perceived real details under real-world UAV imaging conditions.

High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard

In case of a carbonate reservoir, conventional techniques such as artificial neural networks and pattern recognition may not perform well in predicting the reservoir potential zones. This paper proposes a new implementation to highlight the anomalous zones of a carbonate reservoir using grey theory. The input is a 3D seismic volume for a square area which contains eight drilled wells of varying producing quality. The grey analysis is carried out for the zone of interest on a trace-by-trace basis. The result is validated on the reservoir main producing layer by utilizing the wells production classification information.

Radar surveillance of unmanned aerial vehicles (UAVs) is actively developing area of scientific research. This article provides a review and analysis of publications in recent years devoted to the methods and radar systems of detection and recognition of classes and types of UAVs. It is noted that the most difficult targets for radar detection are low-sized, low-speed small UAVs (drones) flying at low and extremely low altitudes. If large and medium-sized UAVs can be detected by modern radar systems, then for the detection of small UAVs it is advisable to create specialized highly efficient, highly mobile, portable and inexpensive active UAV detection radars. The technical requirements for such radars are defined and recommendations for their implementation are provided. High-performance protection systems based on adaptive lattice filters are offered to protect UAV detection radars from noise jamming and passive interference. It is shown that the research on the methods of recognizing UAV classes and types is a development of the existing theory and technology of radar recognition of air targets.

Abstract. Early stress detection is critical for proactive field management and terminal yield prediction, and can aid policy making for improved food security in the context of climate change and population growth. Field surveys for crop monitoring are destructive, labor-intensive, time-consuming and not ideal for large-scale spatial and temporal monitoring. Recent technological advances in Unmanned Aerial Vehicle (UAV) and high-resolution satellite imaging with frequent revisit time have proliferated the applications of this emerging new technology in precision agriculture to address food security challenges from regional to global scales. In this paper, we present a concept of UAV and satellite virtual constellation to demonstrate the power of multi-scale imaging for crop monitoring. Low-cost sensors integrated on a UAV were used to collect RGB, multispectral, and thermal images during the growing season in a test site established near Columbia, Missouri, USA. WorldView-3 multispectral data were pan-sharpened, atmospherically corrected to reflectance and combined with UAV data for temporal monitoring of early stress. UAV thermal and multispectral data were calibrated to canopy temperature and reflectance following a rigorous georeferencing and ortho-correction. The results show that early stress can be effectively detected using multi-temporal and multi-scale UAV and satellite observation; the limitations of satellite remote sensing data in field-level crop monitoring can be overcome by using low altitude UAV observations addressing not just mixed pixel issues but also filling the temporal gap in satellite data availability enabling capture of early stress. The concept developed in this paper also provides a framework for accurate and robust estimation of plant traits and grain yield and delivers valuable insight for high spatial precision in high-throughput phenotyping and farm field management.

The cooperative aerial and device-to-device (D2D) networks employing non-orthogonal multiple access (NOMA) are expected to play an essential role in next-generation wireless networks. Moreover, machine learning (ML) techniques, such as artificial neural networks (ANN), can significantly enhance network performance and efficiency in fifth-generation (5G) wireless networks and beyond. This paper studies an ANN-based unmanned aerial vehicle (UAV) placement scheme to enhance an integrated UAV-D2D NOMA cooperative network.The proposed placement scheme selection (PSS) method for integrating the UAV into the cooperative network combines supervised and unsupervised ML techniques. Specifically, a supervised classification approach is employed utilizing a two-hidden layered ANN with 63 neurons evenly distributed among the layers. The output class of the ANN is utilized to determine the appropriate unsupervised learning method-either k-means or k-medoids-to be employed. This specific ANN layout has been observed to exhibit an accuracy of 94.12%, the highest accuracy among the ANN models evaluated, making it highly recommended for accurate PSS predictions in urban locations. Furthermore, the proposed cooperative scheme allows pairs of users to be simultaneously served through NOMA from the UAV, which acts as an aerial base station. At the same time, the D2D cooperative transmission for each NOMA pair is activated to improve the overall communication quality. Comparisons with conventional orthogonal multiple access (OMA) and alternative unsupervised machine-learning based-UAV-D2D NOMA cooperative networks show that significant sum rate and spectral efficiency gains can be harvested through the proposed method under varying D2D bandwidth allocations.

Small unmanned aerial vehicles (UAVs) are becoming more and more popular and also a challenge for civilian and military security. A UAV has to be detected first, but due to environmental condition (e.g. night or fog) the detection is impeded. To assess the threat of a possible hostile UAV, identification is helpful. If the type of UAV can be determined, information about size, payload, velocity and range can be given and countermeasures can be considered. Identification of UAVs can be more accurate using multiple spectral ranges at the same time.
\nWe present a systematic approach for acquisition of multispectral signatures in the field and in the lab, structured storage in a database and composition of partially synthetic images as training data for identification in an artificial neural network. We set up a multispectral camera system comprised of three imagers, in the visible spectrum, SWIR and MWIR. The cameras are externally triggered. This allows an image acquisition in the field with a synchronized video stream. In addition to that, high resolution images are made in the lab from different angles all around the micro UAV. A specific background is chosen, so it will be masked and with a given real world background image a partially synthetic image can be generated. These can be validated with data that was gathered in the field. Both are stored in a database, along with metadata, to allow access to particular data when needed. Synthetic images and signatures from the field can be used as multispectral training data for an artificial neural network to enable identification of a UAV.

Aerial inspection of solar modules is becoming increasingly popular in automatizing operations and maintenance in large-scale photovoltaic power plants. Current practices are typically time-consuming as they make use of manual acquisitions and analysis of thousands of images to scan for faults and anomalies in the modules. In this paper, we explore and evaluate the use of computer vision and deep learning methods for automating the analysis of fault detection and classification in large scale photovoltaic module installations. We use convolutional neural networks to analyze thermal and visible color images acquired by cameras mounted on unmanned aerial vehicles. We generate composite images by overlaying the thermal and visible images to investigate improvements in detection accuracy of faint features related to faults on modules. Our main goal is to evaluate whether image processing with multi-wavelength composite images can improve both the detection and the classification performance compared to using thermal images alone. The hypothesis is that fusion of images acquired at different wavelengths (i.e., thermal infrared, red, green, and blue visible ranges) would enhance the multi-wavelength representation of faults and thus their histogram feature signatures. The results showed a successful detection and localization of faint fault features using composite images. However, the classification of the fault categories did not show significant improvements and needs continued investigation. This research represents a step towards the design of robust automated methods to improve fault detection from airborne images. Further work is still necessary to reach a classification accuracy comparable with the performance of human experts.

The article addresses the issue of diagnosing the power plant of unmanned aerial vehicles (UAVs) based on electric drives using intelligent data analysis technologies. In the context of modern challenges caused by military aggression against Ukraine, the relevance of this research is determined by the need to ensure the safe operation of autonomous aerial systems in regions with damaged infrastructure and limited telecommunication functionality. It has been established that the efficiency of UAV operation largely depends on the condition of its power unit, and therefore, on the timeliness and accuracy of diagnosing electric drives during flight. The main focus is placed on the development of conceptual foundations for constructing a monitoring system of the technical condition of the electric drive using artificial neural networks (ANN) within the TensorFlow environment. The study analyzes current trends in deep learning technologies that enable the recognition of nonlinear dependencies between the operating parameters of the power plant and its diagnostic indicators. The structure of a typical neural network, including input, hidden, and output layers that perform nonlinear transformations of input signals, is described. Optimization algorithms for the training process (including stochastic gradient descent, Adam, RMSProp) and loss functions applied for the classification of the technical condition of electric motors during operation are characterized. The research substantiates the feasibility of using the TensorFlow Keras API for the construction, training, and testing of diagnostic models for UAV power plants. It is shown that the proposed approach allows for high-accuracy determination of the operational state of electric motors in real time, detection of deviations from nominal operating modes, and prediction of potential failures. Simulation results confirm that the developed model can classify the power unit condition into three categories—operational, conditionally operational, and critical—with an accuracy exceeding 95%. The process of constructing the neural network model is described in detail, including the selection of hyperparameters, the number of hidden layers and neurons, activation functions (ReLU, Softmax), and training parameters. The importance of hardware acceleration (GPU, TPU) is emphasized for ensuring real-time processing of large volumes of diagnostic data. The results of the analysis of the influence of neural network architecture on the convergence rate of the training algorithm and resistance to overfitting are presented. It is concluded that the proposed diagnostic method for power plants based on electric drives using artificial neural networks is an effective tool for enhancing the reliability of unmanned aerial vehicle operation, particularly under conditions of limited telecommunication support. The practical implementation of such systems enables autonomous monitoring of the technical state of power elements, timely detection of faults, and prevention of emergency situations during flight. The obtained results have significant scientific and practical potential for further development of integrated diagnostic systems incorporated into UAV autopilot architectures.

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into restricted or private areas. A UAV detection and identification system is one of the ways of detecting and identifying the presence of a UAV in an area. UAV detection and identification systems employ different sensing techniques such as radio frequency (RF) signals, video, sounds, and thermal imaging for detecting an intruding UAV. Because of the passive nature (stealth) of RF sensing techniques, the ability to exploit RF sensing for identification of UAV flight mode (i.e., flying, hovering, videoing, etc.), and the capability to detect a UAV at beyond visual line-of-sight (BVLOS) or marginal line-of-sight makes RF sensing techniques promising for UAV detection and identification. More so, there is constant communication between a UAV and its ground station (i.e., flight controller). The RF signals emitting from a UAV or UAV flight controller can be exploited for UAV detection and identification. Hence, in this work, an RF-based UAV detection and identification system is proposed and investigated. In RF signal fingerprinting research, the transient and steady state of the RF signals can be used to extract a unique signature. The first part of this work is to use two different wavelet analytic transforms (i.e., continuous wavelet transform and wavelet scattering transform) to investigate and analyze the characteristics or impacts of using either state for UAV detection and identification. Coefficient-based and image-based signatures are proposed for each of the wavelet analysis transforms to detect and identify a UAV. One of the challenges of using RF sensing is that a UAV's communication links operate at the industrial, scientific, and medical (ISM) band. Several devices such as Bluetooth and WiFi operate at the ISM band as well, so discriminating UAVs from other ISM devices is not a trivial task. A semi-supervised anomaly detection approach is explored and proposed in this research to differentiate UAVs from Bluetooth

Intelligent ground vehicles require automated steering control systems. The complexity of the automated steering systems has increased as the concurrent data receiving, processing, decision-making and monitoring all happen simultaneously in super-fast sampling rates. The research reported in this paper focuses on the neural network training and machine learning approach for an automated steering system. The essential information of the steering controller was trained using artificial neural network (ANN) and pattern recognition algorithm approaches. The objective is to investigate the design of ANN training and deep machine learning on collected data from sensors of steering wheel angular position, speed, steering column torque, and ego vehicle speed and generating acceptable steering commands for the vehicle. The results of the research showed that the proposed network control system had trained and validated more than 96.5% steering system behavior patterns and adapted large random disturbances of the steering controller commands. It is, therefore, necessary to develop artificial intelligence methodologies in automated steering systems of autonomous vehicles with neural network representing the main topology blocks of the control system architecture and utilize ANN abstraction in the control system of autonomous vehicle steering control system.