To explore the airspace structure and safety performance of unmanned aerial vehicle (UAV) system based on spatial digital twins (DTs), the study introduces DTs technology, and combines convolutional neural network (CNN) algorithm with UAV autonomous network. The DTs system of UAV is constructed by using wireless communication technology, and its security performance is simulated. The results show that in the analysis of the system packet loss rate, it is found that with the increase of the acquisition points, the amount of transmitted data only increases slightly, but the packet loss rate does not change significantly. In the analysis of the network performance of the unmanned aircraft system, it is found that the node energy-based weighted clustering algorithm (EWCA) can be used to increase the life of the overall network and enhance its availability by rationally controlling the number of nodes and the number of switching between clusters. As the number of nodes increases, the minimum survival time of each clustering algorithm decreases linearly. When the number of nodes is less than 600, the growth rate of cluster head is higher; When the number of nodes is more than 600, the curve growth is relatively smooth. In the analysis of the probability of network safety interruption, it is found that using the model constructed, when the energy acquisition coefficient is close to 0.5, the energy conversion efficiency is higher, the signal-to-noise ratio is larger. Also, when the number of intermediate nodes is increased to 10, the UAV has the best network safety performance. Therefore, through the research, it is found that the UAV DTs system constructed can significantly improve the safety performance of the UAV during its airspace flight. It can provide experimental references for the widespread application of the UAV in the later period.

Three-dimensional (3D) building models play an important role in digital cities and have numerous potential applications in environmental studies. In recent years, the photogrammetric point clouds obtained by aerial oblique images have become a major source of data for 3D building reconstruction. Aiming at reconstructing a 3D building model at Level of Detail (LoD) 2 and even LoD3 with preferred geometry accuracy and affordable computation expense, in this paper, we propose a novel method for the efficient reconstruction of building models from the photogrammetric point clouds which combines the rule-based and the hypothesis-based method using a two-stage topological recovery process. Given the point clouds of a single building, planar primitives and their corresponding boundaries are extracted and regularized to obtain abstracted building counters. In the first stage, we take advantage of the regularity and adjacency of the building counters to recover parts of the topological relationships between different primitives. Three constraints, namely pairwise constraint, triplet constraint, and nearby constraint, are utilized to form an initial reconstruction with candidate faces in ambiguous areas. In the second stage, the topologies in ambiguous areas are removed and reconstructed by solving an integer linear optimization problem based on the initial constraints while considering data fitting degree. Experiments using real datasets reveal that compared with state-of-the-art methods, the proposed method can efficiently reconstruct 3D building models in seconds with the geometry accuracy in decimeter level.

As an essential part of point cloud processing, autonomous classification is conventionally used in various multifaceted scenes and non-regular point distributions. State-of-the-art point cloud classification methods mostly process raw point clouds, using a single point as the basic unit and calculating point cloud features by searching local neighbors via the k-neighborhood method. Such methods tend to be computationally inefficient and have difficulty obtaining accurate feature descriptions due to inappropriate neighborhood selection. In this paper, we propose a robust and effective point cloud classification approach that integrates point cloud supervoxels and their locally convex connected patches into a random forest classifier, which effectively improves the point cloud feature calculation accuracy and reduces the computational cost. Considering the different types of point cloud feature descriptions, we divide features into three categories (point-based, eigen-based, and grid-based) and accordingly design three distinct feature calculation strategies to improve feature reliability. Two International Society of Photogrammetry and Remote Sensing benchmark tests show that the proposed method achieves state-of-the-art performance, with average F1-scores of 89.16 and 83.58, respectively. The successful classification of point clouds with great variation in elevation also demonstrates the reliability of the proposed method in challenging scenes.