Insulators are key components to ensure the normal operation of power facilities in transmission corridors. Existing insulator identification methods mainly use image data and lack the acquisition of three-dimensional information. This paper proposes an efficient insulator extraction method based on UAV (unmanned aerial vehicle) LiDAR (light detection and ranging) point cloud, using five histogram features: horizontal density (HD), horizontal void (HV), horizontal width (HW), vertical width (VW) and vertical void (VV). Firstly, a voxel-based method is employed to roughly extract power lines and pylons from the original point cloud. Secondly, the VV histogram is used to categorize the pylons into suspension and tension types, and the HD histogram is used to locate the tower crossarm and further refine the roughly extracted powerlines. Then, for the suspension tower, insulators are segmented based on the HV histogram and HD difference histogram. For the tension tower, the HW histogram is used to recognize the jumper conductor (JC) and transmission conductor (TC) from the power line. The HW histogram and VW histogram are used to extract the tension insulator in the TC and suspension insulator in the JC, respectively. Finally, considering the problem of setting a suitable grid width when constructing the feature histogram, an adaptive method of multi-scale histograms is proposed to refine the extraction result. Two 220 kV long transmission lines are used for the validation, and the overall object-based accuracy for suspension and tension towers are 100% and 97.3%, respectively. Compared with the point feature-based method, the mean F1 score of the proposed method improved by 0.3, and the runtime for each tower is within 2 s.

Automatic ground filtering is an essential step for Digital Elevation Model (DEM) generation, which has significant application value. However, extraction and classification of ground points from the Light Detection and Ranging (LiDAR) data, especially in multitudinous terrain situations, is a challenging task because it is difficult to determine the set of optimal parameters for removing various non-ground features. In this paper, a new ground filtering technique based on an improved Ball Pivot Algorithm (BPA) is proposed. At the beginning, the LiDAR point cloud dataset was divided into different subsets based on the 2D regular grid. The lowest point in each grid was selected as the seed point to build a single-layer surface. After that, the improved BPA was executed to remove points on the higher location. Then, the rest of the points were calculated and selected as a new seed point according to the spatial relationship with the initial surface. Finally, non-ground points were filtered by means of improved BPA traversing all the grids. Our experimental results on the Benchmark dataset provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) Working Group III/3 showed high accuracy (with a mean kappa coefficient over 80%) in terms of completeness, correctness, and quality for DEM generation. The experimental results demonstrated the proposed method is robust to various terrain situations, as it is more effective and feasible for ground filtering.

Mobile Augmented Reality (MAR) systems are becoming ideal platforms for visualization, permitting users to better comprehend and interact with spatial information. Subsequently, this technological development, in turn, has prompted efforts to enhance mechanisms for registering virtual objects in real world contexts. Most existing AR 3D Registration techniques lack the scene recognition capabilities needed to describe accurately the positioning of virtual objects in scenes representing reality. Moreover, the application of such registration methods in indoor AR-GIS systems is further impeded by the limited capacity of these systems to detect the geometry and semantic information in indoor environments. In this paper, we propose a novel method for fusing virtual objects and indoor scenes, based on indoor scene recognition technology. To accomplish scene fusion in AR-GIS, we first detect key points in reference images. Then, we perform interior layout extraction using a Fully Connected Networks (FCN) algorithm to acquire layout coordinate points for the tracking targets. We detect and recognize the target scene in a video frame image to track targets and estimate the camera pose. In this method, virtual 3D objects are fused precisely to a real scene, according to the camera pose and the previously extracted layout coordinate points. Our results demonstrate that this approach enables accurate fusion of virtual objects with representations of real world indoor environments. Based on this fusion technique, users can better grasp virtual three-dimensional representations on an AR-GIS platform.

3D visualization of Location Based Services (LBS) in Geographic Information System (GIS) and Internet of Things (IoT) applications could enhance the user experience. Texture is generally the most noticeable visual element in a 3D indoor model. However, it usually tends to be out-dated and becomes inaccurate in complex and dynamic environments. The applications of such 3D visualizations, especially in indoor environments, are further impeded by a limited capacity to represent frequently changing scenes due to object displacements. To overcome this problem, a novel location and 3D visual awareness-based method is proposed to automatically register and update a live image onto a 3D indoor model. In this method, a multi-sensor fusion-based indoor localization algorithm employs an indoor map to integrate multi source data, including activity detection information, Pedestrian Dead Reckoning (PDR), and 3D vision-based localization data. Based on the positioning results, we locate the region of the photo capture within a scene and identify the triangular meshes of the facing wall. The texture is extracted from real-time images through a 3D indoor understanding technique and automatically fused with the indoor 3D model. The geometric relationship between texture and the 3D indoor model is established by calculating the UV coordinates. Experimental results demonstrate that our approach can accurately update dynamic indoor model textures regardless of the scene. Based on this texture updating technique, users experience an enhanced presentation of LBS information on our 3D visualization platform.

Geographic Information System (GIS)-based noise management is crucial in urban environments as it provides precise spatial analysis, helping to identify noise hotspots and optimize noise control measures. By integrating noise propagation models with GIS technology, dynamic simulation and visualization of noise distribution can be achieved, offering scientific support for urban planning and noise management. Most existing noise prediction models fail to fully account for three-dimensional (3D) spatial information and a wide range of environmental factors. As a result, there are often discrepancies between the actual noise measurements at monitoring points and the predicted values generated by these models. Furthermore, there is a lack of a system that can effectively integrate noise data with three-dimensional scenes for simulation. This paper proposes a new method to simulate urban noise propagation, aiming to achieve more accurate noise prediction and visualization in a three-dimensional environment. First, we computed the preliminary noise propagation based on a traffic noise model. Next, machine learning techniques were applied to analyze the relationship between noise discrepancies and multi-modal factors, thereby improving the accuracy of environmental noise level estimation. Based on this, we developed an urban noise simulation system. The system integrates functions such as noise simulation, traffic simulation, and weather changes, enabling accurate noise visualization within a three-dimensional virtual environment. Experimental results demonstrate that this method enhances the accuracy of urban noise prediction and visualization, providing users with a more comprehensive understanding of the spatial distribution of urban noise.