Water leakages are very important signals that characterize the serious potential structural damages or flaws in shield tunnels. Automatic, timely, and accurate detection of water leakages is of great significance to the safe operation and maintenance for shield tunnels. However, existing methods (e.g., passive optical images) are highly limited by the confined spaces and dim light conditions in shield tunnels. This paper proposes a new method that uses the intensity images of terrestrial mobile LiDAR (Light Detection and Ranging) for automatic leakage detection in shield tunnels based on deep learning. A self-developed terrestrial mobile LiDAR system (Faro Focus X330) are used to simultaneously obtain the point clouds and intensity information of the shield tunnels. The original intensity data are corrected for the distance effect to generate intensity images. A improved Fully Convolutional Network (FCN) based VGG-19 that is a network structure in deep learning is constructed to achieve accurate tunnel leakage detection based on intensity images. The results show that the proposed method can rapidly and accurately detect leakages in shield tunnels and the leakage detection results are not affected by the tunnel attachments (e.g., bolt holes, cables, and metal facilities). The error rate and segmentation speed are 4.8% and 0.6 s, respectively.

Terrestrial laser scanning (TLS) is a highly effective and noninvasive technology for retrieving the structural and biophysical attributes of trees using 3-D high-accuracy and high-density point clouds. The separation of leaf and wood points in TLS data is a prerequisite for the accurate and reliable derivation of these attributes. In this study, a new method is proposed to separate the leaf and wood points of individual trees by combining the TLS radiometric (intensity) and geometric (density) data. The leaf points are separated from the wood ones through three steps. First, the corrected intensity data are used to separate a part of the leaf points preliminarily given the differences in reflectance characteristics. Second, the density data are adopted for the further separation of another part of the leaf points because the density of the remaining leaf points is smaller than that of the wood points. Finally, a connectivity clustering algorithm is conducted to form several clusters with different sizes (points) and the remaining leaf points are separated in accordance with the cluster sizes. Eight different trees are selected to evaluate the performance of the proposed method. The averaged overall accuracy and kappa coefficient of the eight trees are approximately 95% and 0.81, respectively. The results suggest that the combination of TLS intensity and density data can perform a superior separation of leaf and wood points in terms of efficiency and accuracy, and the proposed separation method can be accurately and robustly used for various trees with different species, sizes, and structures.

Terrestrial laser scanner (TLS) and airborne laser scanner (ALS) can effectively capture point clouds from side or top view, respectively. Registering point clouds captured by ALS and TLS provides an integrated data source for three-dimensional (3-D) reconstruction. However, registration is difficult between TLS and ALS data because of the differences in scanning perspectives, scanning area, and spatial resolutions. A new method that can achieve automatic horizontal registration with ALS and TLS data based on building contour features is proposed in this study. The key steps include horizontal and vertical registrations based on 2-D building outlines and ground planes in ALS and TLS data, respectively. First, the 2-D building outlines are extracted from both ALS and TLS data. Second, the horizontal registration is accomplished by using the four-point congruent sets method for initial registration and the global optimization method for refined registration. Finally, the ground surface in the same region of ALS and TLS data are fitted for vertical registration, and the average elevation difference between the corresponding ground planes is calculated as the translation parameter value in the vertical direction. The results indicate that the proposed method can successfully match ALS and TLS data with an accuracy of 0.2-m both in the horizontal and vertical directions.

Airborne LiDAR (ALS) and terrestrial LiDAR (TLS) data integration provides complementary perspectives for acquiring detailed 3D forest information. However, challenges in registration arise due to feature instability, low overlap, and differences in cross-platform point cloud density. To address these issues, this study proposes an automatic point cloud registration method based on the consistency of the single-tree position distribution in multi-species and complex forest scenes. In this method, single-tree positions are extracted as feature points using the Stepwise Multi-Form Fitting (SMF) technique. A novel feature point matching method is proposed by constructing a polar coordinate system, which achieves fast horizontal registration. Then, the Z-axis translation is determined through the integration of Cloth Simulation Filtering (CSF) and grid-based methods. Finally, the Iterative Closest Point (ICP) algorithm is employed to perform fine registration. The experimental results demonstrate that the method achieves high registration accuracy across four forest plots of varying complexity, with root-mean-square errors of 0.0423 m, 0.0348 m, 0.0313 m, and 0.0531 m. The registration accuracy is significantly improved compared to existing methods, and the time efficiency is enhanced by an average of 90%. This method offers robust and accurate registration performance in complex and diverse forest environments.

Quantitatively characterizing coastal salt-marsh terrains and the corresponding spatiotemporal changes are crucial for formulating comprehensive management plans and clarifying the dynamic carbon evolution. Multiline light detection and ranging (LiDAR) exhibits great capability for terrain measuring for salt marshes with strong penetration performance and a new scanning mode. The prerequisite to obtaining the high-precision terrain requires accurate filtering of the salt-marsh vegetation points from the ground/mudflat ones in the multiline LiDAR data. In this study, a new alternative salt-marsh vegetation point-cloud filtering method is proposed for drone multiline LiDAR based on the extreme gradient boosting (i.e., XGBoost) model. According to the basic principle that vegetation and the ground exhibit different geometric and radiometric characteristics, the XGBoost is constructed to model the relationships of point categories with a series of selected basic geometric and radiometric metrics (i.e., distance, scan angle, elevation, normal vectors, and intensity), where absent instantaneous scan geometry (i.e., distance and scan angle) for each point is accurately estimated according to the scanning principles and point-cloud spatial distribution characteristics of drone multiline LiDAR. Based on the constructed model, the combination of the selected features can accurately and intelligently predict the category of each point. The proposed method is tested in a coastal salt marsh in Shanghai, China by a drone 16-line LiDAR system. The results demonstrate that the averaged AUC and G-mean values of the proposed method are 0.9111 and 0.9063, respectively. The proposed method exhibits enhanced applicability and versatility and outperforms the traditional and other machine-learning methods in different areas with varying topography and vegetation-growth status, which shows promising potential for point-cloud filtering and classification, particularly in extreme environments where the terrains, land covers, and point-cloud distributions are highly complicated.

The individual extraction of street trees from mobile laser point clouds is the prerequisite for their digital expression and application. However, due to the complexity of urban road environment and the diversity of street trees, especially the scenes where adjacent trees are different in type, size, and crown overlap, accurate extraction of individual street trees is still difficult to achieve. Therefore, in this paper, a new method to extract street trees individually from MLS point clouds is proposed. First, the ground and buildings are removed through data preprocessing. Then, the artificial poles that may overlap with street tree crowns are further removed by supervoxels region growing, and the regions of interest (ROI) including street trees and understory vegetation are selected. After that, the main branch part (including trunk) of each tree is separated from the ROI by non-photosynthetic components clustering. Finally, based on the individual clustering results of non-photosynthetic components, the remaining photosynthetic components in the ROI are segmented individually, and the vegetation under the tree is removed through gradual refinement to achieve the complete segmentation of individual trees. An urban area with a total road length of more than 2.1 km, including 6 roads with different complexity, was used to verify the effectiveness of the proposed method for the individual extraction of street trees. The results show that the proposed method can be effectively used for individual extraction of street trees in different complexity scenes. Overall, the precision, recall and F-score of street tree individual extraction are 94.5%, 97.4% and 95.9%, respectively.