Research on Target Recognition Method Based on Laser Point Cloud Data

This paper proposed an organization method of laser point cloud data for the efficient management and rapid visualization of the massive point cloud data of vehicle-mounted laser scanner. In this paper, both the original point cloud data and its trajectory are sectioned for the fast indexing firstly, then the LOD(levels of details) index of each section is build based on octree structure. With a tile type and multiresolution storage mode based on folder system, the depth of octree structure is represented by the level of folder directory, and in every node folder, the corresponding point cloud data file and its node properties file are both include. The storage method of this paper decreases the preprocessing time greatly and can support concurrent access owing to mutually independence of each node. Moreover, with the application of view frustum culling technology and multi-thread dynamic dispatch technology, the rendering and roaming of massive point cloud can realize real time updating according to viewpoint change, which significantly improves the dispatch efficiency of vehicle-mounted laser point cloud data. In the experiment, our method was compared with several popular software of point cloud data processing (e.g. XGRT, Quick Terrain Reader) on the data dispatch. The results show our method has obvious advantage on storage capacity, data access time, memory footprint, rendering frame rate and so on, it indicates our method is effective for the management of vehicle-mounted laser point cloud data and can improve the production efficiency for indoor-work of vehicle-mounted laser point cloud data processing.

Research and Application of Dynamic Tree Growth Cycle Prediction Technology Based on Transmission Channel Laser Point Cloud

Three-dimensional laser scanning technology primarily relies on dense scanning points to collect vectorized information of the target object and perform point cloud data processing without physical contact. There are numerous case studies in various fields regarding the use of three-dimensional laser scanning technology for point cloud data acquisition of different objects. By utilizing a three-dimensional laser scanner, precise three-dimensional data of the object's surface can be obtained, effectively meeting the high-precision measurement requirements of three-dimensional points on the object. The main objective of this study is to determine the most suitable point cloud processing method for preprocessing the solid obtained through laser-based 3D scanning. To achieve this goal, we systematically reviewed and reported the technical papers discussing/proposing point cloud data acquisition and processing techniques based on 3D laser scanning technology. As a result of our search, we collected 31 papers on this topic. In this report, we present the process of data collection, result analysis, and classification of related studies. The classification is based on the applicability of methods in processing point cloud data using 3D laser scanning technology. The proposed research clearly demonstrates the latest approaches in data processing in this field and highlights areas that remain unexplored, providing potential avenues for further research.

Water leakages can affect the safety and durability of shield tunnels, so rapid and accurate identification and diagnosis are urgently needed. However, current leakage detection methods are mostly based on mobile LiDAR data, making it challenging to detect leakage damage in both mobile and terrestrial LiDAR data simultaneously, and the detection results are not intuitive. Therefore, an integrated cylindrical voxel and Mask R-CNN method for water leakage inspection is presented in this paper. This method includes the following three steps: (1) a 3D cylindrical-voxel data organization structure is constructed to transform the tunnel point cloud from disordered to ordered and achieve the projection of a 3D point cloud to a 2D image; (2) automated leakage segmentation and localization is carried out via Mask R-CNN; (3) the segmentation results of water leakage are mapped back to the 3D point cloud based on a cylindrical-voxel structure of shield tunnel point cloud, achieving the expression of water leakage disease in 3D space. The proposed approach can efficiently detect water leakage and leakage not only in mobile laser point cloud data but also in ground laser point cloud data, especially in processing its curved parts. Additionally, it achieves the visualization of water leakage in shield tunnels in 3D space, making the water leakage results more intuitive. Experimental validation is conducted based on the MLS and TLS point cloud data collected in Nanjing and Suzhou, respectively. Compared with the current commonly used detection method, which combines cylindrical projection and Mask R-CNN, the proposed method can achieve water leakage detection and 3D visualization in different tunnel scenarios, and the accuracy of water leakage detection of the method in this paper has improved by nearly 10%.

As a fundamental process of three-dimensional lidar point cloud data (3D LPCD) processing, numerous registration methods are time consuming and easily fall into local optimum. A 3D LPCD registration method based on the iterative closest point (ICP) algorithm, which is improved by the Gaussian mixture model (GMM) considering corner features, is proposed in this article to address these limitations. The GMM method is used for coarse registration, and the input original 3D LPCD is replaced by corner features extracted by the improved 3D Harris algorithm to improve the efficiency of coarse registration. In addition, a satisfactory initial position between the reference and the moving 3D LPCD is prepared for ICP fine registration by coarse registration; thus, the accuracy of fine registration can be improved. The registration accuracy and efficiency of the new method is proved to be higher than those of four common ICP-based registration methods (3DSC-RANSACICP, 3DSC-SAC-IAICP, FPFH-RANSACICP, and FPFH-SAC-IAICP), and GMM registration methods, and the local optimum problem is effectively addressed.

3D city modelling plays a major role in today's smart city development. LiDAR facilitates acquisition and processing of point cloud data. The proposed plan in this paper will estimate the height of a building. LiDAR data processing is done by the software, LAS tools. In this paper the study focuses on LiDAR scan of urban scenes (buildings). Canopy Height Model (CHM) of a building is obtained which is useful in finding the height of a building, shape of the building and peak location. This can be a contribution to smart city modeling and urban development. Canopy height model for LiDAR data is obtained through classifiers and filters available in LAStools. Also this paper presents about Digital Terrain Model (DTM) and Digital Surface Model (DSM) for LiDAR point cloud data. Further the obtained CHM can be used for the application of finding the number of buildings in an area which are above a threshold height.

UAV LiDAR and digital aerial photogrammetry (DAP) have shown great performance in forest inventory due to their advantage in three-dimensional information extraction. Many studies have compared their performance in individual tree segmentation and structural parameters extraction (e.g. tree height). However, few studies have compared their performance in tree species classification. Therefore, we have compared the performance of UAV LiDAR and DAP-based point clouds in individual tree species classification with the following steps: (1) Point cloud data processing: Denoising, smoothing, and normalization were conducted on LiDAR and DAP-based point cloud data separately. (2) Feature extraction: Spectral, structural, and texture features were extracted from the pre-processed LiDAR and DAP-based point cloud data. (3) Individual tree segmentation: The marked watershed algorithm was used to segment individual trees on canopy height models (CHM) derived from LiDAR and DAP data, respectively. (4) Pixel-based tree species classification: The random forest classifier (RF) was used to classify urban tree species with features derived from LiDAR and DAP data separately. (5) Individual tree species classification: Based on the segmented individual tree boundaries and pixel-based classification results, the majority filtering method was implemented to obtain the final individual tree species classification results. (6) Fused with hyperspectral data: LiDAR-hyperspectral and DAP-hyperspectral fused data were used to conduct individual tree species classification. (7) Accuracy assessment and comparison: The accuracy of the above results were assessed and compared. The results indicate that LiDAR outperformed DAP in individual tree segmentation (F-score 0.83 vs. 0.79), while DAP achieved higher pixel-level classification accuracy (73.83% vs. 57.32%) due to spectral-textural features. Fusion with hyperspectral data narrowed the gap, with LiDAR reaching 95.98% accuracy in individual tree classification. Our findings suggest that DAP offers a cost-effective alternative for urban forest management, balancing accuracy and operational costs.

Abstract. The newly development of technology clearly shows an improvement of three-dimension (3D) data acquisition techniques. The requirements of 3D information and features have been obviously increased during past few years in many related fields. Generally, 3D visualization can provide more understanding and better analysis for making decision. The need of 3D GIS also pushed by the highly demand of 3D in geospatial related applications as well as the existing fast and accurate 3D data collection techniques. This paper focuses on the 3D data acquisition by using terrestrial laser scanning. In this study, Leica C10 terrestrial laser scanner was used to collect 3D data of the assets inside a computer laboratory. The laser scanner device is able to capture 3D point cloud data with high speed and high accuracy. A series of point clouds was produced from the laser scanner. However, more attention must be paid during the point clouds data processing, 3D modelling, and analysis of the laser scanned data. Hence, this paper will discuss about the data processing from 3D point clouds to 3D models. The processing of point cloud data divided into pre-processing (data registration and noise filter) and post-processing (3D modelling). During the process, Leica Cyclone 7.3 was used to process the point clouds and SketchUp was used to construct the 3D asset models. Afterward, the 3D asset models were exported to multipatch geometry format, which is a 3D GIS-ready format for displaying and storing 3D model in GIS environment. The final result of this study is a set of 3D asset models display in GIS-ready format since GIS can provides the best visual interpretation, planning and decision making process. This paper shows the 3D GIS data could be produced by laser scanning technology after further processing of point cloud data.

Integrating vision and laser point cloud data for shield tunnel digital twin modeling

The data acquired by airborne lidar are mainly spatial points, commonly known as point clouds. Density, as an important attribute of point clouds, is a measure of terrain fineness. The higher the density of the point cloud, the more accurate the description of the terrain and its characteristics and laws will be. In this paper, a point cloud density enhancement method based on super-resolution convolution network is proposed. Firstly, three-dimensional laser point cloud data are transformed into depth maps, then depth maps are sent to super-resolution convolution neural network for super-clarity. Finally, the super-clarity depth maps obtained by us are transformed into three-dimensional point cloud data. And we verify it through experiments. Through our method, the density of point cloud has been obviously enhanced.

Due to the characteristics of large data volume, uneven distribution, and excessive background noise of substation point cloud data, substation modeling processing with traditional methods have problems such as noise residue, difficulty in equipment segmentation, and low model matching efficiency during the process of point cloud modeling in substations. Therefore, based on the modeling principle of point cloud data, an adaptive voxel filtering algorithm is proposed. This algorithm calculates the neighborhood density of each point as the judgment criterion, and processes point clouds in different density areas with different compression parameters to ensure high compression ratio while preserving low-density details inside the substation equipment. In addition, a fast-matching method based on image recognition was proposed to solve the problem of low model matching efficiency, with a recognition accuracy of 99%. Finally, the correctness and effectiveness of the proposed algorithm were verified through examples, confirming that it improves modeling speed while solving the processing of substation point cloud data.

The fusion of dissimilar data modalities in neural networks presents a significant challenge, particularly in the case of multimodal hyperspectral and lidar data. Hyperspectral data, typically represented as images with potentially hundreds of bands, provide a wealth of spectral information, while lidar data, commonly represented as point clouds with millions of unordered points in 3D space, offer structural information. The complementary nature of these data types presents a unique challenge due to their fundamentally different representations requiring distinct processing methods. In this work, we introduce an alternative hyperspectral data representation in the form of a hyperspectral point cloud (HSPC), which enables ingestion and exploitation with point cloud processing neural network methods. Additionally, we present a composite fusion-style, point convolution-based neural network architecture for the semantic segmentation of HSPC and lidar point cloud data. We investigate the effects of the proposed HSPC representation for both unimodal and multimodal networks ingesting a variety of hyperspectral and lidar data representations. Finally, we compare the performance of these networks against each other and previous approaches. This study paves the way for innovative approaches to multimodal remote sensing data fusion, unlocking new possibilities for enhanced data analysis and interpretation.

The accurate extraction and location of pole tower point cloud in massive laser point cloud data of transmission line is the basis of airborne laser LiDAR line inspection application. In view of the current complex mountain terrain pole tower point cloud extraction depends on manual intervention, low accuracy, low efficiency and so on. An automatic extraction method of line tower point cloud based on three-dimensional grid spatial distribution feature is proposed in this paper. Firstly, the three-dimensional grid of the original point cloud of the preprocessed transmission line is divided, and the spatial distribution characteristics of the point cloud in the statistical line corridor are analyzed (point cloud density, elevation histogram, terrain elevation distribution, ground object elevation distribution). In this way, the tower point cloud is extracted, and the least square space line fitting method is used to accurately locate the tower position, so as to realize the automatic extraction and accurate location of the tower point cloud. The extraction algorithm is verified by the transmission line laser point cloud data obtained from the actual inspection of a power grid company, and the integrity of the extracted tower point cloud data is 95%, and the average positioning error of the tower position is 9cm. The experimental results show that the method has good rapidity and accuracy.

Abstract. This work uses the canopy height model (CHM) based workflow for individual tree crown delineation and 3D feature extraction approach (Overwatch Geospatial's proprietary algorithm) for building feature delineation from high-density light detection and ranging (LiDAR) point cloud data in an urban environment and evaluates its accuracy by using very high-resolution panchromatic (PAN) (spatial) and 8-band (multispectral) WorldView-2 (WV-2) imagery. LiDAR point cloud data over San Francisco, California, USA, recorded in June 2010, was used to detect tree and building features by classifying point elevation values. The workflow employed includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model (DTM), generation of a hill-shade image and an intensity image, extraction of digital surface model, generation of bare earth digital elevation model (DEM) and extraction of tree and building features. First, the optical WV-2 data and the LiDAR intensity image were co-registered using ground control points (GCPs). The WV-2 rational polynomial coefficients model (RPC) was executed in ERDAS Leica Photogrammetry Suite (LPS) using supplementary *.RPB file. In the second stage, ortho-rectification was carried out using ERDAS LPS by incorporating well-distributed GCPs. The root mean square error (RMSE) for the WV-2 was estimated to be 0.25 m by using more than 10 well-distributed GCPs. In the second stage, we generated the bare earth DEM from LiDAR point cloud data. In most of the cases, bare earth DEM does not represent true ground elevation. Hence, the model was edited to get the most accurate DEM/ DTM possible and normalized the LiDAR point cloud data based on DTM in order to reduce the effect of undulating terrain. We normalized the vegetation point cloud values by subtracting the ground points (DEM) from the LiDAR point cloud. A normalized digital surface model (nDSM) or CHM was calculated from the LiDAR data by subtracting the DEM from the DSM. The CHM or the normalized DSM represents the absolute height of all aboveground urban features relative to the ground. After normalization, the elevation value of a point indicates the height from the ground to the point. The above-ground points were used for tree feature and building footprint extraction. In individual tree extraction, first and last return point clouds were used along with the bare earth and building footprint models discussed above. In this study, scene dependent extraction criteria were employed to improve the 3D feature extraction process. LiDAR-based refining/ filtering techniques used for bare earth layer extraction were crucial for improving the subsequent 3D features (tree and building) feature extraction. The PAN-sharpened WV-2 image (with 0.5 m spatial resolution) was used to assess the accuracy of LiDAR-based 3D feature extraction. Our analysis provided an accuracy of 98 % for tree feature extraction and 96 % for building feature extraction from LiDAR data. This study could extract total of 15143 tree features using CHM method, out of which total of 14841 were visually interpreted on PAN-sharpened WV-2 image data. The extracted tree features included both shadowed (total 13830) and non-shadowed (total 1011). We note that CHM method could overestimate total of 302 tree features, which were not observed on the WV-2 image. One of the potential sources for tree feature overestimation was observed in case of those tree features which were adjacent to buildings. In case of building feature extraction, the algorithm could extract total of 6117 building features which were interpreted on WV-2 image, even capturing buildings under the trees (total 605) and buildings under shadow (total 112). Overestimation of tree and building features was observed to be limiting factor in 3D feature extraction process. This is due to the incorrect filtering of point cloud in these areas. One of the potential sources of overestimation was the man-made structures, including skyscrapers and bridges, which were confounded and extracted as buildings. This can be attributed to low point density at building edges and on flat roofs or occlusions due to which LiDAR cannot give as much precise planimetric accuracy as photogrammetric techniques (in segmentation) and lack of optimum use of textural information as well as contextual information (especially at walls which are away from roof) in automatic extraction algorithm. In addition, there were no separate classes for bridges or the features lying inside the water and multiple water height levels were also not considered. Based on these inferences, we conclude that the LiDAR-based 3D feature extraction supplemented by high resolution satellite data is a potential application which can be used for understanding and characterization of urban setup.

Airborne light detection and ranging (lidar) data are widely used for high-resolution land cover mapping. The lidar elevation data are typically used as complementary information to passive multispectral or hyperspectral imagery to enable higher land cover classification accuracy. In this paper, we examine the capabilities of a recently developed multispectral airborne laser scanner, manufactured by Teledyne Optech, for direct classification of multispectral point clouds into ten land cover classes including grass, trees, two classes of soil, four classes of pavement, and two classes of buildings. The scanner, Titan MW, collects point clouds at three different laser wavelengths simultaneously, opening the door to new possibilities in land cover classification using only lidar data. We show that the recorded intensities of laser returns together with spatial metrics calculated from the three-dimensional (3D) locations of laser returns are sufficient for classifying the point cloud into ten distinct land cover classes. Our classification methods achieved an overall accuracy of 94.7% with a kappa coefficient of 0.94 using the support vector machine (SVM) method to classify single-return points and an overall accuracy of 79.7% and kappa coefficient of 0.77 using a rule-based classifier on multireturn points. A land cover map is then generated from the classified point cloud. We show that our results outperform the common approach of rasterizing the point cloud prior to classification by ∼4% in overall accuracy, 0.04 in kappa coefficient, and by up to 16% in commission and omission errors. This improvement however comes at the price of increased complexity and computational burden.