Real-time Network Research Articles

Mamba-UAV-SegNet: A Multi-Scale Adaptive Feature Fusion Network for Real-Time Semantic Segmentation of UAV Aerial Imagery

Accurate semantic segmentation of high-resolution images captured by unmanned aerial vehicles (UAVs) is crucial for applications in environmental monitoring, urban planning, and precision agriculture. However, challenges such as class imbalance, small-object detection, and intricate boundary details complicate the analysis of UAV imagery. To address these issues, we propose Mamba-UAV-SegNet, a novel real-time semantic segmentation network specifically designed for UAV images. The network integrates a Multi-Head Mamba Block (MH-Mamba Block) for enhanced multi-scale feature representation, an Adaptive Boundary Enhancement Fusion Module (ABEFM) for improved boundary-aware feature fusion, and an edge-detail auxiliary training branch to capture fine-grained details. The practical utility of our method is demonstrated through its application to farmland segmentation. Extensive experiments on the UAV-City, VDD, and UAVid datasets show that our model outperforms state-of-the-art methods, achieving mean Intersection over Union (mIoU) scores of 71.2%, 77.5%, and 69.3%, respectively. Ablation studies confirm the effectiveness of each component and their combined contributions to overall performance. The proposed method balances segmentation accuracy and computational efficiency, maintaining real-time inference speeds suitable for practical UAV applications.

DESENet: a bilateral network with detail-enhanced semantic encoder for real-time semantic segmentation

Abstract Real-time semantic segmentation is widely used in various domains such as autonomous driving. Most real-time semantic segmentation networks adopt an encoder-decoder structure. During encoding, feature maps undergo multiple downsampling stages to enhance computational efficiency and enlarge the receptive field. However, this process may lead to the loss of detailed information, which may not be fully recovered during upsampling. Moreover, upsampling semantic features may cause blurring due to the lack of spatial detail information. To mitigate these issues, we use spatially guided cross-resolution self-attention to improve the upsampling of the semantic features by supplementing detailed information from the spatial branch. Furthermore, we incorporate an inductive bias into the cross-resolution attention mechanism to enhance its ability to learn generalized features. Additionally, we design a semantic feature extraction block, and a spatial feature extraction branch to construct a lightweight backbone. The results on Cityscapes and CamVid show that the proposed model achieves a good balance between accuracy and efficiency. Specifically, it obtains 74.2 and 71.5 mIoU on the two test datasets with 1.31M parameters, respectively. Code is available on https://github.com/clearwater753/DECENet.

Exploring hybrid models for identifying locations for active mobility pathways using real-time spatial Delphi and GANs

The spatial planning process is considered an extremely complex system, as it comprises different variables that interrelate and interact with each other. Effectively addressing this spatial complexity necessitates a multidisciplinary approach, as unified methodologies may prove insufficient. Specifically, in urban planning, it is increasingly crucial to prioritize bike lanes, bike stations, and pedestrian zones, for functional transportation infrastructures. This approach can enhance cities by improving air quality, reducing emissions, and boosting public health and safety through physical activity and accident prevention. However, implementing these changes requires careful planning, community engagement, and stakeholder collaboration. This paper proposes a hybrid model for identifying optimal locations for bike lanes, bike stations, and pedestrian zones adopting Real-Time Spatial Delphi and Generative Adversarial Networks (GANs). The Real-Time Spatial Delphi is a modified version of the traditional Delphi method that incorporates real-time feedback and visualization of group response in real-time, aiming to achieve a convergence of opinions among experts on the territory. Nevertheless, these judgments are a spatial representation not visible in reality, and with the spread of artificial intelligence models, different implementations can support the planning process, such as the use of GANs. In this case, GANs can be exploited by adopting pre-existing location images resulting from experts’ judgments to illustrate the proposed intervention’s visual impact. To demonstrate the effectiveness of our hybrid model, we apply it to the city of Dublin. The results showcased how the method helps stakeholders, policymakers, and citizens in visualizing the proposed changes and gauging their potential impact with greater precision.

Empirical Evaluation and Simulation of the Impact of Global Navigation Satellite System Solutions on Uncrewed Aircraft System–Structure from Motion for Shoreline Mapping and Charting

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with R2 values reaching up to 0.97.

SARM: A network State-Aware Adaptive Routing Mutation method for power IoT

With the rapid development of Power Internet of Things (PIoT), the application of Internet of Things technology in smart grids is becoming increasingly widespread, but it also enlarges the attack surface of the system. Against this backdrop, traditional defense methods are limited by the asymmetry of network attack and defense, which makes it difficult to effectively resist the ievolving sniffing attacks and link flooding attacks. In order to improve the security of PIoT, this paper proposes an Adaptive Route Mutation based on Network State Awareness (SARM). It generates a route mutation space using a path state matrix and optimizes the time complexity of mutation space generation with a backtracking method. Furthermore, the SARM can dynamically adjust the route mutation strategy according to the real-time network state to realize self-adaptive defense. In conclusion, SARM is evaluated through simulations conducted with Mininet. Compared to Random Routing Mutation (RRM), it enhances defense against Sniffing and Distributed Denial of Service attacks by approximately 30% and 35% respectively. Additionally, in various example topologies, SARM consistently outperforms RRM.

Deriving water quality index using site-specific water quality parameter guidelines for real-time water quality stations

ABSTRACT The Newfoundland and Labrador real-time water quality (RTWQ) monitoring program operates a network of surface water monitoring stations, measuring physical water quality parameters in near real time. The network generates vast amounts of data that are too complex for many stakeholders to interpret. While the Canadian Council of Ministers of the Environment water quality index (WQI) has been a valuable tool to simplify and summarize the ambient quality of water, its usage in RTWQ is virtually non-existent. This is largely due to the lack of alignment/congruency between the limited number of parameters from real-time networks and the availability of aquatic water quality guidelines. This paper is the first attempt to outline a method of utilizing real-time water quality data to generate site-specific water quality guidelines using the background concentration method. Using these guidelines, we compute weekly WQI scores for select monitoring stations and examine various influences on the scores, including precipitation. The influence of each parameter on the WQI score is also plotted in the computation of the RTWQ WQI. Comparison is made between the yearly scores of the Canadian Environmental Sustainability Indicators WQI and RTWQ WQI. The paper also discusses the challenges and benefits of using this methodology as well as the sensitivity of WQI scoring at stations with various anthropogenic influences.

Lightweight and efficient deep learning models for fruit detection in orchards

The accurate recognition of apples in complex orchard environments is a fundamental aspect of the operation of automated picking equipment. This paper aims to investigate the influence of dense targets, occlusion, and the natural environment in practical application scenarios. To this end, it constructs a fruit dataset containing different scenarios and proposes a real-time lightweight detection network, ELD(Efficient Lightweight object Detector). The EGSS(Efficient Ghost-shuffle Slim module) module and MCAttention(Mix channel Attention) are proposed as innovative solutions to the problems of feature extraction and classification. The attention mechanism is employed to construct a novel feature extraction network, which effectively utilizes the low-latitude feature information, significantly enhances the fine-grained feature information and gradient flow of the model, and improves the model’s anti-interference ability. Eliminate redundant channels with SlimPAN to further compress the network and optimise functionality. The network as a whole employs the Shape-IOU loss function, which considers the influence of the bounding box itself, thereby enhancing the robustness of the model. Finally, the target detection accuracy is enhanced through the transfer of knowledge from the teacher’s network through knowledge distillation, while ensuring that the overall network is sufficiently lightweight. The experimental results demonstrate that the ELD network, designed for fruit detection, achieves an accuracy of 87.4%. It has a relatively low number of parameters (4.3×105\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4.3 \ imes 10^5$$\\end{document}), a GLOPs of only 1.7, and a high FPS of 156. This network can achieve high accuracy while consuming fewer computational resources and performing better than other networks.

DFRDRL: a dynamic fuzzy routing algorithm based on deep reinforcement learning with guaranteed latency and bandwidth for software-defined networks

Traditional routing algorithms have several limitations that become increasingly significant in the context of Software-Defined Networking (SDN). Firstly, these algorithms often have limited support for Quality of Service (QoS) requirements, making them less suitable for handling diverse traffic types efficiently. Moreover, current SDN controllers leverage a default shortest-path routing approach, which does not allow for more dynamic and flexible traffic management based on real-time network policies. To address these issues, this paper introduces a Dynamic Fuzzy Routing algorithm based on Deep Reinforcement Learning (DRL) for SDN that provides guaranteed latency and bandwidth (DFRDRL). DFRDRL provides intelligent and efficient routing that adapts to dynamic traffic by considering path-state criteria and using DRL. Fuzzy logic mainly performs online routing between an ingress-egress pair. To adapt to dynamic traffic changes, DFRDRL can predict the traffic matrix in real time. Meanwhile, DFRDRL reduces the routing reliance on network topology by weighting the network based on critical nodes. Additionally, when the network experiences congestion based on the traffic matrix, a deferral mechanism is applied to prioritize requests with lower resource demands. This mechanism helps ensure efficient resource allocation by temporarily postponing or queuing higher-demand requests, thereby optimizing overall network performance during periods of congestion. The simulation results show that the performance of DFRDRL is better than the equivalent algorithms in terms of latency and throughput. Also, DFRDRL is about 2.5% more efficient than the best existing algorithm in terms of admission rate of routing requests.

Lifelong QoT prediction: an adaptation to real-world optical networks

Predicting the quality of transmission (QoT) is a critical task in the management and optimization of modern fiber-optic networks. Traditional machine learning (ML) QoT prediction models, typically trained on pre-collected datasets, are designed to make long-term predictions once deployed. However, this static training strategy often falls short in the face of time-dependent network evolution and variations. We identify the root cause of these shortcomings as shifts in data distribution, which are not accounted for in conventional static models. In response to these challenges, we propose an online continual learning pipeline that is specifically designed for stable QoT prediction in optical networks. This pipeline directly addresses the problem of distribution shifts by continuously updating the prediction model in response to real-time network data. We explore and compare various strategies within this framework and demonstrate that the integration of the adaptive retraining strategy and the regularized online continual learning algorithm (OCL-REG) significantly enhances the QoT prediction stability while optimizing the resource efficiency. OCL-REG demonstrates superior adaptability and stability, achieving an average cumulative mean squared error (C-MSE) of 0.19 on a testbench with a data distribution shift sequence containing 1000 batches. Moreover, the OCL-REG model requires fewer samples for adaptation, averaging around 107 samples, compared to the conventional retraining strategy, which requires an average of 253 samples. Our approach presents a paradigm shift in QoT prediction, moving from a static to a dynamic, lifelong learning model that is more attuned to the evolving realities of real fiber-optic networks.

A multi-objective indirect neural adaptive processes control design for minimization of energy consumption: An experimental validation on a transesterification reactor

Nowadays, energy management environment is a very important issue that technologies have focused on in order to save costs and minimize energy waste. This objective can be achieved by means of an energy resource management approach through an appropriate optimization technique. However, energy savings can conflict with other objective functions, to solve the problem a multi-objective optimization that considers the minimization of the control energy can be adopted. In this paper, we propose to use a multi-objective indirect neural adaptive control concept for nonlinear systems with unknown dynamics. The control scheme consists of an adaptive instantaneous neural emulator and neural controller built on fully connected real-time recurrent learning networks (RTRL). The multi-objective particle swarm optimization (MOPSO) algorithm is used as a mechanism to find NE and NC adaptive learning rates that optimize the proposed objective functions: control energy minimization and closed-loop preservation. Comparative studies and experimental validation on a semi-batch reactor, used to generate cleaner biofuels, are performed to confirm the effectiveness of the proposed strategy.

LDF-BNN: A Real-Time and High-Accuracy Binary Neural Network Accelerator Based on the Improved BNext.

Significant progress has been made in industrial defect detection due to the powerful feature extraction capabilities of deep neural networks (DNNs). However, the high computational cost and memory requirement of DNNs pose a great challenge to the deployment of industrial edge-side devices. Although traditional binary neural networks (BNNs) have the advantages of small storage space requirements, high parallel computing capability, and low power consumption, the problem of significant accuracy degradation cannot be ignored. To tackle these challenges, this paper constructs a BNN with layered data fusion mechanism (LDF-BNN) based on BNext. By introducing the above mechanism, it strives to minimize the bandwidth pressure while reducing the loss of accuracy. Furthermore, we have designed an efficient hardware accelerator architecture based on this mechanism, enhancing the performance of high-accuracy BNN models with complex network structures. Additionally, the introduction of multi-storage parallelism alleviates the limitations imposed by the internal transfer rate, thus improving the overall computational efficiency. The experimental results show that our proposed LDF-BNN outperforms other methods in the comprehensive comparison, achieving a high accuracy of 72.23%, an image processing rate of 72.6 frames per second (FPS), and 1826 giga operations per second (GOPs) on the ImageNet dataset. Meanwhile, LDF-BNN can also be well applied to defect detection dataset Mixed WM-38, achieving a high accuracy of 98.70%.

Enhancing real-time urban drainage network modeling through Crossformer algorithm and online continual learning

This study presents an innovative approach to real-time modeling of urban drainage networks, leveraging a highly accurate coupled one- and two-dimensional hydrodynamic model to generate a training dataset for node water levels. By employing global states inferred from monitoring points as model inputs, this study overcomes the limitations imposed by the scarcity of monitoring data and the challenge of capturing all node levels. The Crossformer algorithm, which simultaneously accounts for correlation at both temporal and feature scales, is applied to enhance the precision of simultaneous water level predictions across the network. Comparative analysis of different prediction patterns reveals that extending predictions based on a high-accuracy infrastructure offers more benefits than direct modifications to the algorithm's structure. In addition, this paper pioneered the application of online continuous learning concepts to update the prediction model in real time, achieving a balanced integration of measured and simulated data. Consequently, this paper establishes a complete monitoring-predicting-updating real-time simulation system for urban drainage networks.

A Weighted AIMD Congestion Control Algorithm for Device-to-Device Communication in 5G Network

ABSTRACT Device-to-Device (D2D) communication offers promising benefits in 5G networks in terms of improved throughput and reduced latency. However, efficient congestion control remains a critical challenge to ensure optimal performance and resource utilization. Existing congestion control mechanisms often struggle with adapting to dynamic network conditions, leading to inefficiencies, such as underutilization of network resources or excessive packet loss. This paper addresses these challenges by proposing a novel Weighted AIMD congestion control algorithm tailored specifically for D2D communication in 5G networks (WACC-D2DC-5GN). Here, the Weighted AIMD Congestion Control Algorithm is proposed for estimating available bandwidth and Hunger Games Search Optimization to optimize the weight parameter of WACC. The proposed algorithm integrates weighted factors that dynamically adjust congestion window sizes based on real-time network feedback. Simulations are run on the NS3-mmWave module, NS3 LTE-A module. The simulation outcomes show that the proposed WACC-D2DC-5GN method attains 27.26%, 20.41%, and 23.26% higher SNR and 16.20%, 26.97%, and 30.34% higher Throughput when analysed with existing techniques like NexGen D-TCP: next generation dynamic TCP congestion control approach (NGD-TCP-CCA), user-centric channel allocation system for 5G higher-speed users by using ML to decrease handover rate (UCC-HSU-MLA), and efficient with reliable hybrid deep learning-allowed method for congestion control in 5G/6G networks (HDL-EMC-CN) respectively.

A real-time network traffic classification system using cost-sensitive deep neural networks

Due to the continuous growth of Internet and online applications, network traffic classification is not only becoming one of the most crucial disciplines in network management but is also becoming quintessential for providing advanced tasks such as Quality of Service and network security. Moreover, even though many studies have been undertaken in recent years, real-time encrypted traffic classification continues to be an important challenge in the field of network traffic classification. Therefore, in this paper a real-time network traffic classification system is proposed together with five new models. The real-time classification system classified each incoming real-time packets into appropriate classes of interest and the five new models make use of a cost-sensitive learning strategy to deal with the unbalanced data problem during the training phase. The proposed models, which are called Cost-Sensitive Long-Short Term Memory (CSLSTM), Cost-Sensitive Gated Recurrent Unit (CSGRU), Cost-Sensitive Convolution Neural Network (CSCNN), CSNN with LSTM and CSCNN with GRU, can handle both traffic categorization and application identification. These proposed models were compared with prominent methods in this field and the proposed CSCNN was observed to outperform the researched deep learning models by at least 4% to 16% in correctly classifying packets from the ISCX VPN-nonVPN dataset.

Assessing 7-year heat-stress exposures and adaptation strategies for children using a real-time monitoring network in Taiwan

Wet-bulb globe temperature (WBGT) serves as a suitable heat-stress indicator not only for outdoor workers but also for the general public. However, studies on WBGT exposure among the general population are scarce. This research represents the first attempt to assess WBGT exposure of school-aged children. Utilizing a real-time monitoring network in Taiwan, WBGT exposure of school-aged children (7–15 years) were estimated during May to October from 2016 to 2022. Important determinants and spatiotemporal variability of WBGT levels were explored, with hot spots and peak hours of WBGT identified. Macro- and micro-scale adaptation strategies applicable at schools were also evaluated for their effectiveness in reducing heat stress for students. Results showed that the mean daily maximum WBGT (WBGTmax) was 33.1 ± 3.8 °C at 20 stations across Taiwan but could reach/exceed 36 °C (threshold of the dangerous category) at certain hot spots for 42.3–52.0 % of days between May and October. Local geographic features sometimes outweigh the latitude in explaining the spatial variations. Contrary to temperature, WBGT peaked during 10 am to noon rather than from noon to 1:59 pm in most schools, due to clouds blocking solar radiation in the afternoon. This finding has significant implications for scheduling outdoor physical classes/activities to reduce children's heat-health risks. Setting up on-site WBGT monitoring on surfaces that children mostly encounter at schools or utilizing data from nearby weather stations could provide a near real-time heat-health warning. Moreover, providing shades outdoors, relocating outdoor classes indoors, and using air-conditioning would reduce WBGT by 2.1–5.8, 3.7–7.3, and 2.5–5.9 °C, respectively; and would significantly decrease the percentages of WBGT ≥34 °C, which is associated with increased heat-related emergency visits among children in Taiwan. The methodology applied serves as a useful reference for assessing WBGT exposure and adaptation strategies, providing the scientific foundation for heat-health adaptation measures.

CiC-NET: a real-time semantic segmentation network for dam surface crack detection

CiC-NET: a real-time semantic segmentation network for dam surface crack detection

Revolutionizing MANET Route Discovery with INTSM: An Innovative Load Balancing Approach

Communication challenges in ad hoc networks arise due to the mobility of nodes, causing frequent changes in connections and locations. Maintaining network equilibrium to prevent node overload and underutilization is crucial. However, imposing static behaviors on nodes to improve performance can lead to delays, especially in core nodes. Addressing these issues, this research proposes the Intermediate Node Traffic Sharing Model (INTSM) for ad hoc networks. INTSM prioritizes congestion control and load balancing during route discovery, aiming to optimize network resource utilization and traffic distribution, thereby reducing packet delays. The model employs dynamic traffic sharing algorithms that consider real-time network conditions, enabling nodes to adjust their behaviour adaptively. This approach minimizes congestion by distributing traffic loads more evenly across the network, preventing bottlenecks at central nodes. Additionally, INTSM incorporates predictive analysis to foresee potential congestion points and reroute traffic proactively, enhancing overall network stability and performance. Extensive simulations demonstrate that INTSM significantly reduces average packet delay and improves throughput compared to traditional routing protocols. The results highlight the model's efficacy in diverse scenarios, including high mobility and varying traffic loads, proving its robustness and scalability. The primary objective of this study is to enhance navigation and equilibrium mechanisms to improve the performance of ad hoc networks, contributing to more reliable and efficient wireless communication systems. The findings of this research have significant implications for the design of future ad hoc networks, particularly in applications requiring high reliability and quick adaptation to changing network conditions, such as disaster recovery, military operations, and mobile sensor networks. By addressing the critical challenges of congestion control and load balancing, INTSM offers a promising solution to enhance the resilience and efficiency of ad hoc networks.

Urban traffic tiny object detection via attention and multi-scale feature driven in UAV-vision

The unmanned aerial vehicle (UAV) city patrol is of great significance in ensuring the safety of residents’ lives and properties, as well as maintaining the normal operation of the city. However, the detection of UAV images faces challenges such as numerous small-scale objects, complex backgrounds, and high requirements for detection speed. In response to these issues, we introduce a Real-time Small Object Detection network in UAV-vision (RTS-Net), tailored for UAV patrols. Initially, we introduce a multiscale feature fusion module (MFFM) designed to augment the expressiveness of features across scales, thereby enhancing the detection of smaller objects. Subsequently, leveraging attention mechanisms, we present the coordinated attention detection module (CADM), which bolsters the detection model’s ability to accurately segregate objects from the background in expansive, complex scenarios. Lastly, a lightweight real-time feature extraction module (RFEM) is crafted to diminish model computational complexity and boost inference speed. On the UAV road patrol image dataset we constructed, our proposed method attains a detection accuracy of 89.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} mAP, breaking previous records. It surpasses all prevailing detection methods, particularly for small-scale objects. Simultaneously, it achieves an inference speed of 163.9 FPS. The experimental results show that RTS-Net can satisfy the accurate and efficient detection of ground objects by various different UAV platforms in different complex scenarios.

Channel Allocation in mobile multimedia network using artificial neural networks

Capital asset control systems may enhance network performance, reduce congestion, and optimise resource allocation with the use of artificial intelligence. Using machine learning models like decision trees and neural networks allows for more informed and adaptive admission control options. These models can precisely predict how the network's quality of service (QoS) will be affected by accepting a new call. Exploring AI-enhanced CAC schemes requires a thorough analysis of various machine learning methods and their potential applications to real-time network management. To ensure that AI-based CAC implementations are actually possible in resource-limited mobile situations, it is crucial to consider their computational complexity and resource requirements. Accuracy and computational efficiency are two competing goals, and this study examines both. Finding a happy medium that can meet the stringent requirements of mobile multimedia networks is the driving force behind this research. Also covered in this study is the possibility of mixing AI-powered CAC with cutting-edge tech like 5G networks and cloud computing. Collectively, these technology and artificial intelligence (AI) have to open up new opportunities for situationally-conscious, dynamic admission manipulate. As a result, mobile multimedia networks might be a lot more efficient and adaptable. An exploration of CAC schemes is provided in this article, which draws attention to the potential game-changing impact of AI-based methods for mobile multimedia network optimisation. The increasing demand for multimedia services is creating new challenges, yet these challenges may be amenable to artificial intelligence integration into CAC systems. More adaptive and intelligent network management solutions would be possible as a result.

A New Precise Point Positioning with Ambiguity Resolution (PPP-AR) Approach for Ground Control Point Positioning for Photogrammetric Generation with Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are now widely preferred systems that are capable of rapid mapping and generating topographic models with relatively high positional accuracy. Since the integrated GNSS receivers of UAVs do not allow for sufficiently accurate outcomes either horizontally or vertically, a conventional method is to use ground control points (GCPs) to perform bundle block adjustment (BBA) of the outcomes. Since the number of GCPs to be installed limits the process in UAV operations, there is an important research question whether the precise point positioning (PPP) method can be an alternative when the real-time kinematic (RTK), network RTK, and post-process kinematic (PPK) techniques cannot be used to measure GCPs. This study introduces a novel approach using precise point positioning with ambiguity resolution (PPP-AR) for ground control point (GCP) positioning in UAV photogrammetry. For this purpose, the results are evaluated by comparing the horizontal and vertical coordinates obtained from the 24 h GNSS sessions of six calibration pillars in the field and the horizontal length differences obtained by electronic distance measurement (EDM). Bartlett’s test is applied to statistically determine the accuracy of the results. The results indicate that the coordinates obtained from a two-hour PPP-AR session show no significant difference from those acquired in a 30 min session, demonstrating PPP-AR to be a viable alternative for GCP positioning. Therefore, the PPP technique can be used for the BBA of GCPs to be established for UAVs in large-scale map generation. However, the number of GCPs to be selected should be four or more, which should be homogeneously distributed over the study area.