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

We are pleased to introduce this Special Issue of Drones, entitled “Advances in Civil Applications of Unmanned Aircraft Systems: 2nd Edition.” In recent decades, Unmanned Aircraft Systems (UAS) have become indispensable tools across civil engineering, environmental monitoring, infrastructure inspection, urban planning, forestry, and geospatial data acquisition. Their technological evolution and the rising demand for efficient, data-driven methodologies have positioned UAS as key enablers of safer, more sustainable, and more resilient practices in both urban and natural environments. This rapid expansion also exposes significant challenges: dense urban environments amplify turbulence, GNSS degradation, microscale atmospheric variability, and collision risk, while the accuracy of photogrammetry, LiDAR, hyperspectral imaging, and other sensing modalities remains highly sensitive to flight-planning choices, environmental factors, and platform stability. These limitations underscore the need for ongoing research in autonomous navigation, real-time environmental modelling, deep-learning-based perception, and robust control systems capable of ensuring scalability, reproducibility, and long-term operational resilience.

At the same time, major advances in remote sensing, lightweight sensor integration, machine learning, 3D micro-weather modelling, and autonomous flight control continue to expand the capabilities of civil UAS applications. These developments enhance established techniques, such as terrain modelling, road extraction, structural analysis, and environmental impact assessment, while enabling new possibilities for risk prediction, ecological restoration monitoring, emergency response, and multi-sensor data fusion. In this context, this Special Issue presents a collection of cutting-edge research articles that collectively advance the state of the art. The contributions include optimized flight-parameter strategies for urban photogrammetry, CFD-based modelling approaches to strengthen urban air-mobility safety, advanced segmentation techniques for remote-sensing imagery, hyperspectral assessments of contaminated mining sites, LiDAR-based collision-risk evaluation, and resilient control strategies for UAVs operating under wind disturbances. Together, these studies illustrate the maturity, diversity, and societal relevance of modern UAS technologies and highlight future pathways driven by autonomy, precision sensing, resilience, and sustainability.

In “Comparative Assessment of Vegetation Removal for DTM Generation and Earthwork Volume Estimation Using RTK-UAV Photogrammetry and LiDAR Mapping (Contribution 1),” Hyeongseok Kang et al. compare UAV photogrammetry and LiDAR-based workflows for terrain modelling under varying vegetation conditions. Their study evaluates how vegetation-removal strategies affect the accuracy of digital terrain models and subsequent earthwork-volume estimations, providing insight into optimal data-processing pipelines for civil engineering applications.

“Optimization of UAV Flight Parameters for Urban Photogrammetric Surveys: Balancing Orthomosaic Visual Quality and Operational Efficiency (Contribution 2),” by José Lemus-Romani et al., examines how specific flight-planning parameters influence the quality of orthomosaics in dense urban environments. The authors analyze trade-offs between image resolution, mission duration and operational cost, offering evidence-based guidelines for improved photogrammetric performance.

In “Towards Safer UAV Operations in Urban Air Mobility: 3D Automated Modelling for CFD-Based Microweather Systems (Contribution 3),” Enrique Aldao et al. advance urban air-mobility safety by generating high-resolution 3D urban models for CFD simulations. Their research characterizes microscale wind phenomena such as turbulence and vortex shedding, contributing to more reliable risk-aware UAV routing in cities.

“UAV-YOLO12: A Multi-Scale Road Segmentation Model for UAV Remote Sensing Imagery (Contribution 4),” by Bingyan Cui et al., introduces a deep-learning architecture designed to mitigate scale variation and background complexity in aerial road-segmentation tasks. The proposed model enhances segmentation accuracy across heterogeneous landscapes and addresses dataset limitations common in UAV imaging.

In “Drone-Based VNIR–SWIR Hyperspectral Imaging for Environmental Monitoring of a Uranium Legacy Mine Site (Contribution 5),” Victor Tolentino et al. employ VNIR–SWIR hyperspectral techniques to assess contamination patterns in a legacy uranium mining area. Their findings illustrate the value of UAV-borne hyperspectral sensors for characterizing mineralogical variations and environmental impacts.

“PISCFF-LNet: A Method for Autonomous Flight of UAVs Based on Lightweight Road Extraction (Contribution 6),” by Yuanxu Zhu et al., proposes an efficient deep-learning model to support autonomous UAV navigation in environments with weak GNSS signals. The method extracts road networks in real time, enabling more stable and adaptable unmanned flight control.

In “Assessing Lightweight Folding UAV Reliability Through a Photogrammetric Case Study: Extracting Urban Village’s Buildings Using Object-Based Image Analysis (Contribution 7),” Junyu Kuang et al. evaluate the structural robustness and data-capture reliability of lightweight folding UAVs. Using an OBIA workflow, they assess the suitability of such platforms for mapping irregular urban settlements.

“Accurate Tracking of Agile Trajectories for a Tail-Sitter UAV Under Wind Disturbances Environments (Contribution 8),” by Xu Zou et al., develops a control strategy for tail-sitter UAVs performing highly dynamic maneuvers under outdoor wind disturbances. Their approach leverages differential flatness and robust control design to maintain stable trajectory tracking across challenging flight regimes.

In “Assessment of LiDAR-Based Sensing Technologies in Bird–Drone Collision Scenarios (Contribution 9),” Paula Seoane et al. examine LiDAR systems for detecting avian targets and reducing collision risks in emerging air-mobility operations. Their work provides performance benchmarks for sensing technologies aimed at ensuring safer multi-agent airspace coexistence.

“Online Autonomous Motion Control of Communication-Relay UAV with Channel Prediction in Dynamic Urban Environments (Contribution 10),” by Cancan Tao and Bowen Liu, presents an adaptive motion-control framework for UAV-based communication relays operating in mobile, signal-degraded urban settings. The method integrates channel prediction with real-time mobility modelling to ensure stable communication performance.

In “Integrating Drone-Based LiDAR and Multispectral Data for Tree Monitoring (Contribution 11),” Beatrice Savinelli et al. combine LiDAR structural metrics with multispectral vegetation information to assess forest health at high spatial resolution. Their integrated approach supports early detection of ecological stressors and improved ecosystem monitoring.

“Feature-Enhanced Attention and Dual-GELAN Net (FEADG-Net) for UAV Infrared Small Object Detection in Traffic Surveillance (Contribution 12),” by Tuerniyazi Aibibu et al., develops an infrared-optimized detection architecture targeting small, low-contrast objects in aerial traffic-monitoring scenarios. The model enhances feature representation and robustness, offering advances for safety-critical surveillance systems.

These articles, among others, offer a wide range of perspectives on the civil applications of Unmanned Aircraft Systems, providing valuable insights for researchers, engineers, policymakers, and practitioners engaged in the development and deployment of UAS technologies. Collectively, they enrich our scientific understanding of drone-based methods and inspire the adoption of innovative, data-driven approaches that enhance safety, efficiency, and sustainability across diverse civil domains.

We extend our sincere appreciation to all contributing authors for their expertise, methodological rigour, and commitment to advancing the state of the art in UAS research. We also express our gratitude to the dedicated reviewers, whose thoughtful evaluations and constructive guidance were essential in ensuring the scientific quality, clarity, and impact of this Special Issue.