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Drones
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Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition
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Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 8 November 2025 | Viewed by 7655

Special Issue Editors

Dr. Higinio González Jorge

E-Mail Website
Guest Editor
Engineering Physics Group, School of Aerospace Engineering, University of Vigo, Campus Ourense, 32004 Ourense, Spain
Interests: infrastructure maintenance; NDT; UAV; geospatial technology
Special Issues, Collections and Topics in MDPI journals
Dr. Fernando Veiga López

E-Mail Website
Guest Editor
Aerospace & Transportation Systems Laboratory (AEROLAB), School of Aerospace Engineering, University of Vigo, Ourense, Spain
Interests: drones; avionics; fluid dynicamcs
Enrique Aldao Pensado

E-Mail Website
Guest Editor Assistant
Aerospace & Transportation Systems Laboratory (AEROLAB), School of Aerospace Engineering, University of Vigo, Ourense, Spain
Interests: drones; navigation systems; artificial intelligence
Gabriel Fontenla-Carrera

E-Mail Website
Guest Editor Assistant
Aerospace & Transportation Systems Laboratory (AEROLAB), School of Aerospace Engineering, University of Vigo, Ourense, Spain
Interests: drones; navigation systems; artificial intelligence

Special Issue Information

Dear Colleagues,

Drones have emerged over the past decade as a fundamental tool in various civil applications including, among other uses, the inspection of complex structures such as viaducts or wind turbines, professional image and video operations, the monitoring of agricultural fields and forest masses, controlling pollution in bodies of water, serving as logistical tools in remote locations, enabling topographical operations, and contributing to search and rescue missions. There are a multitude of aircraft designs, including fixed-wing or rotary-wing, various types of payloads, propulsion systems, etc. Additionally, there are many instances where unmanned aircraft collaborate with other aircraft, swarms, or other unmanned vehicles, whether land- or sea-based. The regulatory aspects and their evolution over the past years have also proven to be crucial in this context, accompanying the development of the sector.

The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights about varios applications across a broad spectrum of unmanned aicraft in the civil sector. This Special Issue will welcome manuscripts that link the following themes:

- Drone applications in infrastructure monitoring;

- Drone design, aerodynamics, propulsion, payloads, guidance, navigation, and control;

- Drone applications in agriculture management;

- Drone applications in marine environment;

- Drone applications in forestry management;

- Drone applications in surveying;

- Drone in logistics;

- Drone regulation;

- Drone swarms;

- Drones working in collaboration with sea-based and land-based unmanned systems;

- Drone mitigation from the civil side (e.g., critical infrastructure such as airports);

- Drones in education.

We look forward to receiving your original research articles and reviews.

Dr. Higinio González Jorge
Dr. Fernando Veiga López
Guest Editors

Eng. Enrique Aldao Pensado
Eng. Gabriel Fontenla-Carrera
Guest Editorial Assistants

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • drones
  • surveying
  • unmanned aircraft systems
  • regulations
  • swarm
  • photogrammetry
  • remote sensing
  • earth observation
  • guidance, navigation, and control
  • aerodynamics

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (7 papers)

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Research

30 pages, 8862 KiB  
Article
PISCFF-LNet: A Method for Autonomous Flight of UAVs Based on Lightweight Road Extraction
by Yuanxu Zhu, Tianze Zhang, Aiying Wu and Gang Shi
Drones 2025, 9(3), 226; https://doi.org/10.3390/drones9030226 - 20 Mar 2025
Viewed by 171
Abstract
Currently, autonomous flight control for unmanned aerial vehicles (UAVs) has become increasingly critical in remote-sensing applications, such as high-resolution data acquisition and road disease detection. However, this task also faces significant challenges, particularly the weak GNSS signals in flight areas and the complex [...] Read more.
Currently, autonomous flight control for unmanned aerial vehicles (UAVs) has become increasingly critical in remote-sensing applications, such as high-resolution data acquisition and road disease detection. However, this task also faces significant challenges, particularly the weak GNSS signals in flight areas and the complex flight environment. Furthermore, many existing autonomous-flight-control algorithms for UAVs are computationally demanding, which limits their deployment on embedded devices with constrained memory and processing power, thereby affecting both operational efficiency and the safety of UAV missions. To address these issues, we propose PISCFF-LNet, a lightweight road-extraction network that integrates prior knowledge and spatial contextual features. The network employs a dual-branch encoder architecture to separately extract spatial and contextual features, thus obtaining multi-dimensional feature representations. In addition, to enhance the integration of different features and improve the overall feature representation, we also introduce a feature-fusion module. To further enhance UAV performance, we introduce an improved ray-based eight neighborhood algorithm (RENA), which efficiently extracts road-edge information with a remarkably low latency of just 7 ms, providing accurate flight guidance and reducing misidentification. To provide a comprehensive evaluation of the model’s performance, we have developed a new drone remote-sensing road-semantic-segmentation dataset, DRS Road, which includes approximately 2600 ultra-high-resolution remote-sensing images across six scene categories. The experimental results demonstrate that PISCFF-LNet achieves improvements of 1.06% in Intersection over Union (IoU) and 0.83% in F1-Score on the DeepGlobe Road dataset, and 1.03% in IoU and 0.57% in F1-Score on the DRS Road dataset, compared to existing methods. Finally, we applied the algorithm to a UAV, using a PID-based flight-control algorithm. The results show that drones employing our algorithm exhibit superior flight performance in both simulated and real-world environments. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition)
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29 pages, 15780 KiB  
Article
Assessing Lightweight Folding UAV Reliability Through a Photogrammetric Case Study: Extracting Urban Village’s Buildings Using Object-Based Image Analysis (OBIA) Method
by Junyu Kuang, Yingbiao Chen, Zhenxiang Ling, Xianxin Meng, Wentao Chen and Zihao Zheng
Drones 2025, 9(2), 101; https://doi.org/10.3390/drones9020101 - 29 Jan 2025
Viewed by 678
Abstract
With the rapid advancement of drone technology, modern drones have achieved high levels of functional integration, alongside structural improvements that include lightweight, compact designs with foldable features, greatly enhancing their flexibility and applicability in photogrammetric applications. Nevertheless, limited research currently explores data collected [...] Read more.
With the rapid advancement of drone technology, modern drones have achieved high levels of functional integration, alongside structural improvements that include lightweight, compact designs with foldable features, greatly enhancing their flexibility and applicability in photogrammetric applications. Nevertheless, limited research currently explores data collected by such compact UAVs, and whether they can balance a small form factor with high data quality remains uncertain. To address this challenge, this study acquired the remote sensing data of a peri-urban area using the DJI Mavic 3 Enterprise and applied Object-Based Image Analysis (OBIA) to extract high-density buildings. It was found that this drone offers high portability, a low operational threshold, and minimal regulatory constraints in practical applications, while its captured imagery provides rich textural details that clearly depict the complex surface features in urban villages. To assess the accuracy of the extraction results, the visual comparison between the segmentation outputs and airborne LiDAR point clouds captured by the DJI M300 RTK was performed, and classification performance was evaluated based on confusion matrix metrics. The results indicate that the boundaries of the segmented objects align well with the building edges in the LiDAR point cloud. The classification accuracy of the three selected algorithms exceeded 80%, with the KNN classifier achieving an accuracy of 91% and a Kappa coefficient of 0.87, which robustly demonstrate the reliability of the UAV data and validate the feasibility of the proposed approach in complex cases. As a practical case reference, this study is expected to promote the wider application of lightweight UAVs across various fields. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition)
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30 pages, 13740 KiB  
Article
Accurate Tracking of Agile Trajectories for a Tail-Sitter UAV Under Wind Disturbances Environments
by Xu Zou, Zhenbao Liu, Zhen Jia and Baodong Wang
Drones 2025, 9(2), 83; https://doi.org/10.3390/drones9020083 - 22 Jan 2025
Viewed by 730
Abstract
To achieve more robust and accurate tracking control of high maneuvering trajectories for a tail-sitter fixed-wing unmanned aerial vehicle (UAV) operating within its full envelope in outdoor environments, a novel control approach is proposed. Firstly, the study rigorously demonstrates the differential flatness property [...] Read more.
To achieve more robust and accurate tracking control of high maneuvering trajectories for a tail-sitter fixed-wing unmanned aerial vehicle (UAV) operating within its full envelope in outdoor environments, a novel control approach is proposed. Firstly, the study rigorously demonstrates the differential flatness property of tail-sitter fixed-wing UAV dynamics using a comprehensive aerodynamics model, which incorporates wind effects without simplification. Then, utilizing the derived flatness functions and the treatments for singularity, the study presents a complete process of the differential flatness transform. This transformation maps the desired maneuver trajectory to a state-input trajectory, facilitating control design. Leveraging an existing controller from the reference literature, trajectory tracking is implemented. Subsequently, a low-cost wind estimation method operating during all flight phases is proposed to estimate the wind effects involved in the model. The wind estimation method involves generating a virtual wind measurement utilizing a low-fidelity tail-sitter model. The virtual wind measurement is integrated with real wind data obtained from the pitot tube and processed through fusion using an extended Kalman filter. Finally, the effectiveness of our methods is confirmed through comprehensive real-world experiments conducted in outdoor settings. The results demonstrate superior robustness and accuracy in controlling challenging agile maneuvering trajectories compared to the existing method. Additionally, the test results highlight the effectiveness of our method in wind estimation. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition)
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13 pages, 5322 KiB  
Article
Assessment of LiDAR-Based Sensing Technologies in Bird–Drone Collision Scenarios
by Paula Seoane, Enrique Aldao, Fernando Veiga-López and Higinio González-Jorge
Drones 2025, 9(1), 13; https://doi.org/10.3390/drones9010013 - 27 Dec 2024
Cited by 1 | Viewed by 795
Abstract
The deployment of Advanced Air Mobility requires the continued development of technologies to ensure operational safety. One of the key aspects to consider here is the availability of robust solutions to avoid tactical conflicts between drones and other flying elements, such as other [...] Read more.
The deployment of Advanced Air Mobility requires the continued development of technologies to ensure operational safety. One of the key aspects to consider here is the availability of robust solutions to avoid tactical conflicts between drones and other flying elements, such as other drones or birds. Bird detection is a relatively underexplored area, but due to the large number of birds, their shared airspace with drones, and the fact that they are non-cooperative elements within an air traffic management system, it is of interest to study how their detection can be improved and how collisions with them can be avoided. This work demonstrates how a LiDAR sensor mounted on a drone can detect birds of various sizes. A LiDAR simulator, previously developed by the Aerolab research group, is employed in this study. Six different collision trajectories and three different bird sizes (pigeon, falcon, and seagull) are tested. The results show that the LiDAR can detect any of these birds at about 30 m; bird detection improves when the bird gets closer and has a larger size. The detection accuracy is higher than 1 m in most of the cases under study. The errors grow with increasing drone-bird relative speed. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition)
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23 pages, 6743 KiB  
Article
Online Autonomous Motion Control of Communication-Relay UAV with Channel Prediction in Dynamic Urban Environments
by Cancan Tao and Bowen Liu
Drones 2024, 8(12), 771; https://doi.org/10.3390/drones8120771 - 19 Dec 2024
Viewed by 846
Abstract
In order to improve the network performance of multi-unmanned ground vehicle (UGV) systems in urban environments, this article proposes a novel online autonomous motion-control method for the relay UAV. The problem is solved by jointly considering unknown RF channel parameters, unknown multi-agent mobility, [...] Read more.
In order to improve the network performance of multi-unmanned ground vehicle (UGV) systems in urban environments, this article proposes a novel online autonomous motion-control method for the relay UAV. The problem is solved by jointly considering unknown RF channel parameters, unknown multi-agent mobility, the impact of the environments on channel characteristics, and the unavailable angle-of-arrival (AoA) information of the received signal, making the solution of the problem more practical and comprehensive. The method mainly consists of two parts: wireless channel parameter estimation and optimal relay position search. Considering that in practical applications, the radio frequency (RF) channel parameters in complex urban environments are difficult to obtain in advance and are constantly changing, an estimation algorithm based on Gaussian process learning is proposed for online evaluation of the wireless channel parameters near the current position of the UAV; for the optimal relay position search problem, in order to improve the real-time performance of the method, a line search algorithm and a general gradient-based algorithm are proposed, which are used for point-to-point communication and multi-node communication scenarios, respectively, reducing the two-dimensional search to a one-dimensional search, and the stability proof and convergence conditions of the algorithm are given. Comparative experiments and simulation results under different scenarios show that the proposed motion-control method can drive the UAV to reach or track the optimal relay position and improve the network performance, while demonstrating that it is beneficial to consider the impact of the environments on the channel characteristics. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition)
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23 pages, 7403 KiB  
Article
Integrating Drone-Based LiDAR and Multispectral Data for Tree Monitoring
by Beatrice Savinelli, Giulia Tagliabue, Luigi Vignali, Roberto Garzonio, Rodolfo Gentili, Cinzia Panigada and Micol Rossini
Drones 2024, 8(12), 744; https://doi.org/10.3390/drones8120744 - 10 Dec 2024
Cited by 3 | Viewed by 1565
Abstract
Forests are critical for providing ecosystem services and contributing to human well-being, but their health and extent are threatened by climate change, requiring effective monitoring systems. Traditional field-based methods are often labour-intensive, costly, and logistically challenging, limiting their use for large-scale applications. Drones [...] Read more.
Forests are critical for providing ecosystem services and contributing to human well-being, but their health and extent are threatened by climate change, requiring effective monitoring systems. Traditional field-based methods are often labour-intensive, costly, and logistically challenging, limiting their use for large-scale applications. Drones offer advantages such as low operating costs, versatility, and rapid data collection. However, challenges remain in optimising data processing and methods to effectively integrate the acquired data for forest monitoring. This study addresses this challenge by integrating drone-based LiDAR and multispectral data for forest species classification and health monitoring. We developed the methodology in Ticino Park (Italy), where intensive field campaigns were conducted in 2022 to collect tree species compositions, the leaf area index (LAI), canopy chlorophyll content (CCC), and drone data. Individual trees were first extracted from LiDAR data and classified using spectral and textural features derived from the multispectral data, achieving an accuracy of 84%. Key forest traits were then retrieved from the multispectral data using machine learning regression algorithms, which showed satisfactory performance in estimating the LAI (R2 = 0.83, RMSE = 0.44 m2 m−2) and CCC (R2 = 0.80, RMSE = 0.33 g m−2). The retrieved traits were used to track species-specific changes related to drought. The results obtained highlight the potential of integrating drone-based LiDAR and multispectral data for cost-effective and accurate forest health monitoring and change detection. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition)
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21 pages, 9235 KiB  
Article
Feature-Enhanced Attention and Dual-GELAN Net (FEADG-Net) for UAV Infrared Small Object Detection in Traffic Surveillance
by Tuerniyazi Aibibu, Jinhui Lan, Yiliang Zeng, Weijian Lu and Naiwei Gu
Drones 2024, 8(7), 304; https://doi.org/10.3390/drones8070304 - 8 Jul 2024
Cited by 2 | Viewed by 1655
Abstract
With the rapid development of UAV and infrared imaging technology, the cost of UAV infrared imaging technology has decreased steadily. Small target detection technology in aerial infrared images has great potential for applications in many fields, especially in the field of traffic surveillance. [...] Read more.
With the rapid development of UAV and infrared imaging technology, the cost of UAV infrared imaging technology has decreased steadily. Small target detection technology in aerial infrared images has great potential for applications in many fields, especially in the field of traffic surveillance. Because of the low contrast and relatively limited feature information in infrared images compared to visible images, the difficulty involved in small road target detection in infrared aerial images has increased. To solve this problem, this study proposes a feature-enhanced attention and dual-GELAN net (FEADG-net) model. In this network model, the reliability and effectiveness of small target feature extraction is enhanced by a backbone network combined with low-frequency enhancement and a swin transformer. The multi-scale features of the target are fused using a dual-GELAN neck structure, and a detection head with the parameters of the auto-adjusted InnerIoU is constructed to improve the detection accuracy for small infrared targets. The viability of the method was proved using the HIT-UAV dataset and IRTS-AG dataset. According to a comparative experiment, the mAP50 of FEADG-net reached more than 90 percent, which was higher than that of any previous method and it met the real-time requirements. Finally, an ablation experiment was conducted to demonstrate that all three of the modules proposed in the method contributed to the improvement in the detection accuracy. This study not only designs a new algorithm for small road object detection in infrared remote sensing images from UAVs but also provides new ideas for small target detection in remote sensing images for other fields. Full article
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition)
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