Transportation infrastructure is central to economic development and the daily lives of citizens. However, rapid urbanization, increasing vehicle ownership, and growing concerns about sustainable development have significantly heightened the complexity of managing these systems. Although digital twin (DT) technology holds great promise, most current research focuses on specific areas, lacking a comprehensive framework that spans the entire lifecycle of transportation infrastructure, from planning and construction to operation and maintenance. The technical challenges of integrating different DT systems remain unclear, which to some extent limits the potential of DT technology in the management of transportation infrastructure. To address this gap, this review first summarizes the fundamental concepts and architectures involved in DT systems for transportation infrastructure, such as roads, bridges, tunnels, and hubs. From a lifecycle perspective, DT systems for transportation infrastructure are categorized based on functional scope, data integration methods, and application stages, and their key technologies and basic frameworks are outlined. Subsequently, the potential applications of DT in various lifecycle stages of transportation infrastructure—planning and construction, operation and maintenance, and decommissioning and renewal—are analyzed, and current research progress is reviewed and discussed. Finally, the challenges and future directions for achieving a full lifecycle DT system for transportation infrastructure, encompassing technical, operational, and ethical aspects, are discussed and summarized. The insights gained herein will be valuable for researchers, urban planners, engineers, and policymakers.

UAVs have become one of the widely used remote sensing platforms and played a critical role in the construction of smart cities. However, due to the complex environment in urban scenes, secure and accurate data acquisition brings great challenges to 3D modeling and scene updating. Optimal trajectory planning of UAVs and accurate data collection of onboard cameras are non-trivial issues in urban modeling. This study presents the principle of optimized views photogrammetry and verifies its precision and potential in large-scale 3D modeling. Different from oblique photogrammetry, optimized views photogrammetry uses rough models to generate and optimize UAV trajectories, which is achieved through the consideration of model point reconstructability and view point redundancy. Based on the principle of optimized views photogrammetry, this study first conducts a precision analysis of 3D models by using UAV images of optimized views photogrammetry and then executes a large-scale case study in the urban region of Qingdao city, China, to verify its engineering potential. By using GCPs for image orientation precision analysis and TLS (terrestrial laser scanning) point clouds for model quality analysis, experimental results show that optimized views photogrammetry could construct stable image connection networks and could achieve comparable image orientation accuracy. Benefiting from the accurate image acquisition strategy, the quality of mesh models significantly improves, especially for urban areas with serious occlusions, in which 3 to 5 times of higher accuracy has been achieved. Besides, the case study in Qingdao city verifies that optimized views photogrammetry can be a reliable and powerful solution for the large-scale 3D modeling in complex urban scenes.

Localization and perception play an important role as the basis of autonomous Unmanned Aerial Vehicle (UAV) applications, providing the internal state of movements and the external understanding of environments. Simultaneous Localization And Mapping (SLAM), one of the critical techniques for localization and perception, is facing technical upgrading, due to the development of embedded hardware, multi-sensor technology, and artificial intelligence. This survey aims at the development of visual SLAM and the basis of UAV applications. The solutions to critical problems for visual SLAM are shown by reviewing state-of-the-art and newly presented algorithms, providing the research progression and direction in three essential aspects: real-time performance, texture-less environments, and dynamic environments. Visual–inertial fusion and learning-based enhancement are discussed for UAV localization and perception to illustrate their role in UAV applications. Subsequently, the trend of UAV localization and perception is shown. The algorithm components, camera configuration, and data processing methods are also introduced to give comprehensive preliminaries. In this paper, we provide coverage of visual SLAM and its related technologies over the past decade, with a specific focus on their applications in autonomous UAV applications. We summarize the current research, reveal potential problems, and outline future trends from academic and engineering perspectives.

Stereo matching is a fundamental task in three-dimensional scene reconstruction. Recently, deep learning-based methods have proven effective on some benchmark datasets, such as KITTI and SceneFlow. Unmanned aerial vehicles (UAVs) are commonly used for surface observation, and the images captured are frequently used for detailed 3D reconstruction because of their high resolution and low-altitude acquisition. Currently, mainstream supervised learning networks require a significant amount of training data with ground-truth labels to learn model parameters. However, owing to the scarcity of UAV stereo-matching datasets, learning-based stereo matching methods in UAV scenarios is not fully investigated yet. To facilitate further research, this study proposes a pipeline for generating accurate and dense disparity maps using detailed meshes reconstructed based on UAV images and LiDAR point clouds. Through the proposed pipeline, we constructed a multi-resolution UAV scenario dataset called UAVStereo, with over 34,000 stereo image pairs covering three typical scenes. To the best of our knowledge, UAVStereo is the first stereo matching dataset for UAV low-altitude scenarios. The dataset includes synthetic and real stereo pairs to enable generalization from the synthetic domain to the real domain. Furthermore, our UAVStereo dataset provides multi-resolution and multi-scene image pairs to accommodate various sensors and environments. In this study, we evaluated traditional and state-of-the-art deep learning methods, highlighting their limitations in addressing challenges in UAV scenarios and offering suggestions for future research. Our dataset is available at https://github.com/rebecca0011/UAVStereo.git.

This paper investigates the formation control problem of multi-unmanned aerial vehicle (UAV) systems with multi-operating modes. While mode switching enhances the flexibility of multi-UAV systems, it also introduces dynamic model switching behaviors in UAVs. Moreover, obtaining an accurate dynamic model for a multi-UAV system is challenging in practice. In addition, communication link failures and time-varying unknown disturbances are inevitable in multi-UAV systems. Hence, to overcome the adverse effects of the above challenges, a hybrid iterative learning formation control strategy is proposed in this paper. The proposed controller does not rely on precise modeling and exhibits its learning ability by utilizing historical input–output data to update the current control input. Furthermore, two convergence theorems are proven to guarantee the convergence of state, disturbance estimation, and formation tracking errors. Finally, three simulation examples are conducted for a multi-UAV system consisting of four quadrotor UAVs under multi-operating modes, switching topologies, and external disturbances. The results of the simulations show the strategy’s effectiveness and superiority in achieving the desired formation control objectives.

Despite the ubiquity of unmanned aerial vehicles (UAVs) in urban monitoring, nighttime UAV imagery—plagued by high level of noise, uneven light, motion blur, and low dynamic range—poses unique challenges for 3D reconstruction. These degradations cripple traditional Structure from Motion (SfM) frameworks through unreliable feature extraction and inconsistent cross-view spatial association. We propose a 3D reconstruction framework to recover accurate camera poses and point clouds from unordered UAV images captured in nighttime urban scenes. Traditional SfM pipelines typically rely on well-lighted scene imagery, which becomes challenging in low-light conditions due to poor keypoint detection and matching, potentially leading to a breakdown of the entire SfM system. To address these challenges, we introduce a Low-Light Enhancement SfM (LLE-SfM) framework that leverages an enhancement network to suppress noise while restoring scene colors and texture details. Specifically, our framework first decomposes every image of a set of nighttime UAV images into high/low-frequency layers. The High-frequency Stream is employed to remove noise and enhance edge details, while transformer layers are deployed within the Low-frequency Stream to reconstruct scene colors and refine consistency. Due to the lack of low-light 3D reconstruction benchmark datasets for nighttime urban scenes, we introduce the LLUrban3d dataset, including the Rendered subset and Real subset, to provide sufficient ground truth for the training and evaluation of low-light 3D reconstruction pipeline. Experiments on the LLUrban3d dataset demonstrate that our framework significantly improves reconstruction accuracy and completeness over SfM baseline in nighttime urban scenes, with a maximum improvement of 24.17% in F-score.