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Automated low-altitude air delivery : towards autonomous cargo transportation with drones
\"This book investigates Unmanned Aircraft Systems (UAS) with a payload capacity of one metric ton for transportation. The authors provide a large variety of perspectivesfrom economics to technical realization. With the focus on such heavy-lift cargo UAS, the authors consider recently established methods for approval and certification, which they expect to be disruptive for unmanned aviation. In particular, the Specific Operations Risk Assessment (SORA) and its impact on the presented technological solutions and operational concepts are studied. Starting with the assumption of an operation over sparsely populated areas and below common air traffic, diverse measures to further reduce operational risks are proposed. Operational concepts derived from logistics use-cases set the context for an in-depth analysis including aircraft and system design, safe autonomy as well as airspace integration and datalinks. Results from simulations and technology demonstrations are presented as a proof of concept for solutions proposed in this book.\"-- Back cover.
Book
A Review of Unmanned Aerial Vehicle Low-Altitude Remote Sensing (UAV-LARS) Use in Agricultural Monitoring in China
Precision agriculture relies on the rapid acquisition and analysis of agricultural information. An emerging method of agricultural monitoring is unmanned aerial vehicle low-altitude remote sensing (UAV-LARS), which possesses significant advantages of simple construction, strong mobility, and high spatial-temporal resolution with synchronously obtained image and spatial information. UAV-LARS could provide a high degree of overlap between X and Y during key crop growth periods that is currently lacking in satellite and remote sensing data. Simultaneously, UAV-LARS overcomes the limitations such as small scope of ground platform monitoring. Overall, UAV-LARS has demonstrated great potential as a tool for monitoring agriculture at fine- and regional-scales. Here, we systematically summarize the history and current application of UAV-LARS in Chinese agriculture. Specifically, we outline the technical characteristics and sensor payload of the available types of unmanned aerial vehicles and discuss their advantages and limitations. Finally, we provide suggestions for overcoming current limitations of UAV-LARS and directions for future work.
Journal Article
Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model
Real-time analysis of UAV low-altitude remote sensing images at airborne terminals facilitates the timely monitoring of weeds in the farmland. Aiming at the real-time identification of rice weeds by UAV low-altitude remote sensing, two improved identification models, MobileNetV2-UNet and FFB-BiSeNetV2, were proposed based on the semantic segmentation models U-Net and BiSeNetV2, respectively. The MobileNetV2-UNet model focuses on reducing the amount of calculation of the original model parameters, and the FFB-BiSeNetV2 model focuses on improving the segmentation accuracy of the original model. In this study, we first tested and compared the segmentation accuracy and operating efficiency of the models before and after the improvement on the computer platform, and then transplanted the improved models to the embedded hardware platform Jetson AGX Xavier, and used TensorRT to optimize the model structure to improve the inference speed. Finally, the real-time segmentation effect of the two improved models on rice weeds was further verified through the collected low-altitude remote sensing video data. The results show that on the computer platform, the MobileNetV2-UNet model reduced the amount of network parameters, model size, and floating point calculations by 89.12%, 86.16%, and 92.6%, and the inference speed also increased by 2.77 times, when compared with the U-Net model. The FFB-BiSeNetV2 model improved the segmentation accuracy compared with the BiSeNetV2 model and achieved the highest pixel accuracy and mean Intersection over Union ratio of 93.09% and 80.28%. On the embedded hardware platform, the optimized MobileNetV2-UNet model and FFB-BiSeNetV2 model inferred 45.05 FPS and 40.16 FPS for a single image under the weight accuracy of FP16, respectively, both meeting the performance requirements of real-time identification. The two methods proposed in this study realize the real-time identification of rice weeds under low-altitude remote sensing by UAV, which provide a reference for the subsequent integrated operation of plant protection drones in real-time rice weed identification and precision spraying.
Journal Article
The Substorms of 26 February 2008: A Data‐Mining Perspective
Reconstruction of the magnetospheric magnetic field using swarms of virtual spacecraft provided by data mining confirms seminal in situ evidence (Angelopoulos et al., 2008, https://doi.org/10.1126/science.1160495) that on 26 February 2008 an X‐line emerged in the region between two distant Time History of Events and Macroscale Interactions during Substorms probes at the time of the substorm activation in the magnetotail. It also shows that the X‐line formation was preceded by rapid current decay that happened 15 min earlier. The current was built up earthward of the pre‐existing X‐line formed prior to the previous substorm activation 45 min before. The most pronounced effect of the tail reconfiguration at the moments of two substorm activations and the current disruption is the rapid earthward redistribution of the magnetic flux. Comparison of low‐altitude mapping of the magnetotail structure with all‐sky imager data shows that these rapid reconfigurations might be triggered by plasma flows whose source was farther from the Earth than the resolved X‐lines.
Journal Article
Assessment of Multi-Image Unmanned Aerial Vehicle Based High-Throughput Field Phenotyping of Canopy Temperature
Canopy temperature (CT) has been related to water-use and yield formation in crops. However, constantly (e.g., sun illumination angle, ambient temperature) as well as rapidly (e.g., clouds) changing environmental conditions make it difficult to compare measurements taken even at short time intervals. This poses a great challenge for high-throughput field phenotyping (HTFP). The aim of this study was to i) set up a workflow for unmanned aerial vehicles (UAV) based HTFP of CT, ii) investigate different data processing procedures to combine information from multiple images into orthomosaics, iii) investigate the repeatability of the resulting CT by means of heritability, and iv) investigate the optimal timing for thermography measurements. Additionally, the approach was v) compared with other methods for HTFP of CT. The study was carried out in a winter wheat field trial with 354 genotypes planted in two replications in a temperate climate, where a UAV captured CT in a time series of 24 flights during 6 weeks of the grain-filling phase. Custom-made thermal ground control points enabled accurate georeferencing of the data. The generated thermal orthomosaics had a high spatial accuracy (mean ground sampling distance of 5.03 cm/pixel) and position accuracy [mean root-mean-square deviation (RMSE) = 4.79 cm] over all time points. An analysis on the impact of the measurement geometry revealed a gradient of apparent CT in parallel to the principle plane of the sun and a hotspot around nadir. Averaging information from all available images (and all measurement geometries) for an area of interest provided the best results by means of heritability. Correcting for spatial in-field heterogeneity as well as slight environmental changes during the measurements were performed with the R package SpATS. CT heritability ranged from 0.36 to 0.74. Highest heritability values were found in the early afternoon. Since senescence was found to influence the results, it is recommended to measure CT in wheat after flowering and before the onset of senescence. Overall, low-altitude and high-resolution remote sensing proved suitable to assess the CT of crop genotypes in a large number of small field plots as is required in crop breeding and variety testing experiments.
Journal Article
A Novel GAN-Based Anomaly Detection and Localization Method for Aerial Video Surveillance at Low Altitude
The last two decades have seen an incessant growth in the use of Unmanned Aerial Vehicles (UAVs) equipped with HD cameras for developing aerial vision-based systems to support civilian and military tasks, including land monitoring, change detection, and object classification. To perform most of these tasks, the artificial intelligence algorithms usually need to know, a priori, what to look for, identify. or recognize. Actually, in most operational scenarios, such as war zones or post-disaster situations, areas and objects of interest are not decidable a priori since their shape and visual features may have been altered by events or even intentionally disguised (e.g., improvised explosive devices (IEDs)). For these reasons, in recent years, more and more research groups are investigating the design of original anomaly detection methods, which, in short, are focused on detecting samples that differ from the others in terms of visual appearance and occurrences with respect to a given environment. In this paper, we present a novel two-branch Generative Adversarial Network (GAN)-based method for low-altitude RGB aerial video surveillance to detect and localize anomalies. We have chosen to focus on the low-altitude sequences as we are interested in complex operational scenarios where even a small object or device can represent a reason for danger or attention. The proposed model was tested on the UAV Mosaicking and Change Detection (UMCD) dataset, a one-of-a-kind collection of challenging videos whose sequences were acquired between 6 and 15 m above sea level on three types of ground (i.e., urban, dirt, and countryside). Results demonstrated the effectiveness of the model in terms of Area Under the Receiving Operating Curve (AUROC) and Structural Similarity Index (SSIM), achieving an average of 97.2% and 95.7%, respectively, thus suggesting that the system can be deployed in real-world applications.
Journal Article
eVTOL Dispatch Cost Optimization Under Time-Varying Low-Altitude Delivery Demand
In the emerging paradigm of embodied intelligence, eVTOL technology holds significant potential to transform the low-altitude economy, particularly in short-distance emergency logistics and urban distribution. Companies like Meituan and Shunfeng (SF) are pioneering fixed low-altitude routes to reduce reliance on human delivery. We first investigate the performance and routing of Meituan’s eVTOL system, focusing on the dynamic optimization of eVTOL reserves and total costs at distribution stations under fluctuating order surges and charging constraints. An iterative algorithm is constructed, supported by numerical examples and Monte Carlo simulations. Our results reveal that cost parameters and demand characteristics jointly shape eVTOL incremental decision-making and its economic performance. To optimize costs, strategies like multi-period decentralized scheduling or low-frequency centralized decision-making are proposed. Future research will address limitations such as 2C charging effects and joint battery-eVTOL replenishment to further advance urban logistics and low-altitude economy development.
Journal Article
UAV Low-Altitude Remote Sensing for Precision Weed Management
Precision weed management, an application of precision agriculture, accounts for within-field variability of weed infestation and herbicide damage. Unmanned aerial vehicles (UAVs) provide a unique platform for remote sensing of field crops. They are more efficient and flexible than manned agricultural airplanes in acquiring high-resolution images at low altitudes and low speeds. UAVs are more universal than agricultural aircraft, because the latter are used only in specific regions. We have developed and used UAV systems for red-green-blue digital and color-infrared imaging over crop fields to identify weed species, determine crop injury from dicamba at different doses, and detect naturally grown glyphosate-resistant weeds. This article presents remote sensing technologies for weed management and focuses on development and application of UAV-based low-altitude remote sensing technology for precision weed management. In particular, this article futher discusses the potential application of UAV-based plant-sensing systems for mapping the distributions of glyphosate-resistant and glyphosate-susceptible weeds in crop fields. Nomenclature: Dicamba; glyphosate
Journal Article
UAV Localization Method with Keypoints on the Edges of Semantic Objects for Low-Altitude Economy
The low-altitude economy heavily relies on new carriers represented by unmanned aerial vehicles (UAVs). The localization accuracy of UAVs highly relies on the Global Navigation Satellite System (GNSS), which can be easily affected in low-altitude urban environments, making it difficult to maintain effective localization accuracy. To solve this problem, this paper proposes a UAV autonomous localization method with keypoints on the edges of semantic objects (KESO). Firstly, semantic objects within the working area are selected, and then the latitude, longitude, and altitude of these semantic objects’ keypoints are measured to construct a database. By identifying the semantic objects from aerial images and detecting the edge of the semantic objects, the keypoints of the semantic objects are obtained. Finally, by matching the detected keypoints in the aerial images with the keypoints in the database, the UAV’s position can achieve a high-precision position when satellite signals are blocked in low-altitude urban environments. As verified by real flight data, the results show that the localization error is less than 5 m, and the edges of objects can obtain more accurate keypoints to help UAVs locate more accurately. This paper can provide a reference for UAV localization in the urban environments of the low-altitude economy.
Journal Article
Internet of Low-Altitude UAVs (IoLoUA): a methodical modeling on integration of Internet of “Things” with “UAV” possibilities and tests
Evidence of the IoT is expanding the number of connected devices, including UAVs. UAVs overcome the flaws in the physical IoT infrastructure already in place. Low-altitude views are expected to be dominant swiftly in urban areas. In a short period of time, they are able to cover a large area and distribute goods and information around the globe. Additionally, how to provide a safe and secure UAV operation in high-level traffic circumstances is also a topic of investigation. When operating an UAV in a limited area, the IoLoUA system is used to maintain order. Additionally, it aids with node exploration. Basic principles that can be used to create new structural designs are analysed for both networks (IoLoUA). There has been an explanation of the IoLoUA strategy’s approach to implementation so far. Among the issues covered in this article are UAV-generated IoT data collection and delivery, security threats, and typical workflow approaches. This work presents a theoretical model of future design evolution.
Journal Article