The fifth-generation (5G) wireless communication has an urgent need for target tracking. Digital programmable metasurface (DPM) may offer an intelligent and efficient solution owing to its powerful and flexible controls of electromagnetic waves and advantages of lower cost, less complexity and smaller size than the traditional antenna array. Here, we report an intelligent metasurface system to perform target tracking and wireless communications, in which computer vision integrated with a convolutional neural network (CNN) is used to automatically detect the locations of moving targets, and the dual-polarized DPM integrated with a pre-trained artificial neural network (ANN) serves to realize the smart beam tracking and wireless communications. Three groups of experiments are conducted for demonstrating the intelligent system: detection and identification of moving targets, detection of radio-frequency signals, and real-time wireless communications. The proposed method sets the stage for an integrated implementation of target identification, radio environment tracking, and wireless communications. This strategy opens up an avenue for intelligent wireless networks and self-adaptive systems.

No single sensor can acquire complete information by applying one or several multi-surveys to cultural object reconstruction. For instance, a terrestrial laser scanner (TLS) usually obtains information on building facades, whereas aerial photogrammetry is capable of providing the perspective for building roofs. In this study, a camera-equipped unmanned aerial vehicle system (UAV) and a TLS were used in an integrated design to capture 3D point clouds and thus facilitate the acquisition of whole information on an object of interest for cultural heritage. A camera network is proposed to modify the image-based 3D reconstruction or structure from motion (SfM) method by taking full advantage of the flight control data acquired by the UAV platform. The camera network improves SfM performances in terms of image matching efficiency and the reduction of mismatches. Thus, this camera network modified SfM is employed to process the overlapping UAV image sets and to recover the scene geometry. The SfM output covers most information on building roofs, but has sparse resolution. The dense multi-view 3D reconstruction algorithm is then applied to improve in-depth detail. The two groups of point clouds from image reconstruction and TLS scanning are registered from coarse to fine with the use of an iterative method. This methodology has been tested on one historical monument in Fujian Province, China. Results show a final point cloud with complete coverage and in-depth details. Moreover, findings demonstrate that these two platforms, which integrate the scanning principle and image reconstruction methods, can supplement each other in terms of coverage, sensing resolution, and model accuracy to create high-quality 3D recordings and presentations.

Road furniture plays an important role in road safety. To enhance road safety, policies that encourage the road furniture inventory are prevalent in many countries. Such an inventory can be remarkably facilitated by the automatic recognition of road furniture. Current studies typically detect and classify road furniture as one single above-ground component only, which is inadequate for road furniture with multiple functions such as a streetlight with a traffic sign attached. Due to the recent developments in mobile laser scanners, more accurate data is available that allows for the segmentation of road furniture at a detailed level. In this paper, we propose an automatic framework to decompose road furniture into different components based on their spatial relations in a three-step procedure: first, pole-like road furniture are initially detected by removing ground points and an initial classification. Then, the road furniture is decomposed into poles and attachments. The result of the decomposition is taken as a feedback to remove spurious pole-like road furniture as a third step. If there are no poles extracted in the decomposition stage, these incorrectly detected pole-like road furniture—such as the pillars of buildings—will be removed from the detection list. We further propose a method to evaluate the results of the decomposition. Compared with our previous work, the performance of decomposition has been much improved. In our test sites, the correctness of detection is higher than 90% and the completeness is approximately 95%, showing that our procedure is competitive to state of the art methods in the field of pole-like road furniture detection. Compared to our previous work, the optimized decomposition improves the correctness by 7.3% and 18.4% in the respective test areas. In conclusion, we demonstrate that our method decomposes pole-like road furniture into poles and attachments with respect to their spatial relations, which is crucial for road furniture interpretation.

Detecting land use changes in urban areas from very-high-resolution (VHR) satellite images presents two primary challenges: 1) Traditional methods focus mainly on comparing changes in land cover-related features, which are insufficient for detecting changes in land use and are prone to pseudo changes caused by illumination differences, seasonal variations and subtle structural changes; 2) Spatial structural information, which is characterised by topological relationships among land cover objects, is crucial for urban land use classification but remains underexplored in change detection. To address these challenges, this study developed a local–global structural interaction network (LGSI-Net) based on Siamese graph neural network (SGNN) that integrates high-level structural and semantic information to detect urban land use changes from bi-temporal VHR images. We developed both local and global structural feature interaction modules to enhance the representation of bi-temporal structural features at the global scene graph and local object node levels. Experiments on the publicly available MtS-WH dataset and two generated datasets, LUCD-FZ and LUCD-HF, show that the proposed method outperforms existing BoVW-based method and CorrFusionNet. Furthermore, we evaluated the detection performance for different semantic feature extraction strategies and structural feature extraction backbones. The results demonstrate that the proposed method, which integrates high-level semantic and GIN-derived structural features achieves the best performance. The method trained on the LUCD-FZ dataset was successfully transferred to the LUCD-HF dataset with different urban landscapes, indicating its effectiveness in detecting land use changes from VHR satellite images, even in areas with relatively large imbalances between changed and unchanged samples.

This study investigates the usability of low-attitude unmanned aerial vehicle (UAV) acquiring high resolution images for the geometry reconstruction of opencast mine. Image modelling techniques like Structure from Motion (SfM) and Patch-based Multiview Stereo (PMVS) algorithms are used to generate dense 3D point cloud from UAV collections. Then, precision of 3D point cloud will be first evaluated based on Real-time Kinematic (RTK) ground control points (GCPs) at point level. The experimental result shows that the mean square error of the UAV point cloud is 0.11 m. Digital surface model (DSM) of the study area is generated from UAV point cloud, and compared with that from the Terrestrial Laser Scanner (TLS) data for further comparison at the surface level. Discrepancy map of 3D distances based on DSMs shows that most deviation is less than ±0.4 m and the relative error of the volume is 1.55 %.