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Roadway deformation is a comprehensive response to changes in surrounding rock masses as well as the geological environment. Deformation patterns survey can help us to understand rock masses, especially soft swelling rock mass and guide roadway renovation. In this paper, an overall deformation analysis method for a largely deformed roadway in soft swelling rock mass has been proposed based on 3D mobile laser scanning. The application procedures of the proposed method mainly include point cloud data acquisition, data pre-processing, coordinate transformation, surface change detection and visualization. In the proposed method, the reference point cloud is created according to the roadway design to overcome the issue of the missing original roadway profile point cloud. By incorporating the K-nearest neighbor algorithm, a point-to-plane distance calculation algorithm is developed for the source and the reference point cloud data to detect roadway deformation. Taking the main haulage roadway in skarn rock mass at − 480 m level of Taiping Mine as an example, the deformation patterns of this roadway were analyzed using the proposed method. The results reveal that the roadway deformed seriously up to 1187 mm and the roadway deformation characterizes asymmetry and non-uniformity. The accuracy of the proposed method can meet engineering requirements by comparing the calculated and measured results. Aiming at the asymmetrical deformation patterns of this roadway, an asymmetrical support methodology has been suggested for the roadway renovation. The proposed deformation analysis method provides a reliable tool for largely deformed underground workings in soft swelling rock mass. Highlights: An overall deformation analysis method based on 3D mobile laser scanning has been proposed for largely deformed roadway in soft swelling rock mass. In the proposed method, a surface change detection algorithm has been developed by incorporating the K-nearest neighbor algorithm for the source and the reference point clouds. Taking Anhui Taiping Mine as an engineering case, the deformation patterns of the roadway in skarn rock masses have been revealed to be distinct asymmetry and non-uniformity using the proposed method. [ABSTRACT FROM AUTHOR]

This study explores how data from a handheld mobile laser scanning (MLS) system and quantitative structural models (QSM) can be used to estimate tree structural attributes. Four MLS acquisition scenarios were investigated in a 1 ha temperate hardwood stand, including 15 and 35 m parallel lines, nine circular plots, and a 20 m × 20 m grid. Results were compared against terrestrial laser scanning and destructive field measurements. All acquisition scenarios yielded comparable results, except for the 35 m scenario, which showed greater variability. The 20 m × 20 m grid scenario showed the highest accuracy, with an RMSE of 0.41 m (2.07%) for tree height, 3.98 cm (14.93%) for diameter at breast height, 0.21 m³ (19.28%) for merchantable wood volume, and 0.07 m³ (10.11%) for merchantable stem volume. A bias < 5% was observed for these key attributes, except for an 11.68% bias in merchantable wood volume. Overestimation of branch volume was identified as the primary source of bias related to merchantable wood volume. This study highlights MLS's potential for accurate, non-destructive estimation of tree structural attributes, while pointing out the need to refine noise removal and to assess the most suitable acquisition scenarios for various forest types. [ABSTRACT FROM AUTHOR]

The use of mobile laser scanning for mapping forests has scarcely been studied in difficult forest conditions. In this paper, we compare the accuracy of retrieving tree attributes, particularly diameter at breast height (DBH), stem curve, stem volume, and tree height, using six different laser scanning systems in a managed natural boreal forest. These compared systems operated both under the forest canopy on handheld and unmanned aerial vehicle (UAV) platforms and above the canopy from a helicopter. The complexity of the studied forest sites ranged from easy to difficult, and thus, this is the first study to compare the performance of several laser scanning systems for the direct measurement of stem curve in difficult forest conditions. To automatically detect tree stems and to calculate their attributes, we utilized our previously developed algorithm integrated with a novel bias compensation method to reduce the overestimation of stem diameter arising from finite laser beam divergence. The bias compensation method reduced the absolute value of the diameter bias by 55–99%. The most accurate laser scanning systems were equipped with a Velodyne VLP-16 sensor, which has a relatively low beam divergence, on a handheld or UAV platform. In easy plots, these systems found a root-mean-square error (RMSE) of below 10% for DBH and stem curve estimates and approximately 10% for stem volume. With the handheld system in difficult plots, the DBH and stem curve estimates had an RMSE under 10%, and the stem volume RMSE was below 20%. Even though bias compensation reduced the difference in bias and RMSE between laser scanners with high and low beam divergence, the RMSE remained higher for systems with a high beam divergence. The airborne laser scanner operating above the forest canopy provided tree attribute estimates close to the accuracy of the under-canopy laser scanners, but with a significantly lower completeness rate for stem detection, especially in difficult forest conditions. [ABSTRACT FROM AUTHOR]

Forest understory complexity is important for many species, from large herbivores such as deer to small mammals such as mice and voles. For species that utilize the forest understory on a very small scale, it is often impractical to conduct correspondingly fine‐grained manual surveys of the understory, and thus few studies consider this small‐scale variation in understory complexity and instead work with average values on a larger scale. We explored the use of a mobile laser scanning derived understory complexity measure—understory roughness—to predict the capture probability of two representative small mammal species, the yellow‐necked mouse (Apodemus flavicollis) and the bank vole (Clethrionomys glareolus). We found a positive relationship between capture probability and understory roughness for both bank voles and yellow‐necked mice. Our results suggest that mobile laser scanning is a promising technology for measuring understory complexity in an ecologically meaningful way. [ABSTRACT FROM AUTHOR]

Gamification techniques have been proven effective in various fields such as education and industry. In this paper, we introduce a novel approach that applies gamification techniques to the identification of road assets in Mobile Laser Scanning (MLS) data. Our method utilises three gamification techniques: avatar (vehicle), point cloud segmentation into levels, and scoring. We implemented these techniques in Unreal Engine and evaluated their performance using three real-world case studies. We also compared two ways of point cloud visualisation: mesh-based and point-based. Our results demonstrate that our gamification approach improves the handling and visualisation of point clouds when compared to other free software such as Cloud Compare. Specifically, the point-based visualisation method provides a more accurate representation of the road environment and the input point cloud and is easier to import into Unreal Engine. However, this method requires more computational resources for visualisation. On the other hand, level segmentation ensures a constant frame rate of 60 frames per second. Furthermore, our gamification approach enhances the experience of road asset identification, making it more enjoyable for the user. However, we acknowledge that the quality of the point cloud remains the primary factor affecting the accuracy of asset identification, regardless of the software used. Overall, our proposed gamification approach offers a promising solution for improving the identification of road assets in MLS data and has the potential to be applied to other fields beyond road asset identification. [ABSTRACT FROM AUTHOR]

This study introduces an enhanced octree-based framework for integrated geotechnical analysis, combining geological, microseismic, and Mobile Laser Scanning (MLS)-based change detection data. Our approach leverages efficient statistical inference-based change detection techniques and additional octree data structures, enabling voxel-level data integration and association, semantic clustering, and comprehensive geotechnical analysis. We detail the implementation of our method, demonstrating capabilities such as integrating geological fault data, seismic energy exposure modeling, and combination of Random Sample Consensus (RANSAC) classification with Density-Based Spatial Clustering of Applications with Noise (DBSCAN). We conduct a suite of statistical analyses to investigate multi-dimensional trends and correlations among MLS-measured changes, distances to geological faults, and seismic energy exposure. We found that binary change classification and change magnitude negatively correlate with three investigated fault-distance metrics. Multivariant analysis reveals that increased seismic energy exposure and decreased fault distance positively correlates with MLS-based change classification and magnitudes. Our findings imply that proximity to geological faults and seismic energy exposure can be statistically linked to increases in geotechnically relevant deformations. This study is the first to demonstrate that octree-based MLS data processing can reveal multi-dimensional trends in underground mine datasets, thereby enhancing understanding of complex geomechanical behaviors crucial for mining productivity and safety. Highlights: Introduced octree-based framework for integrated geotechnical analysis. Combined geological, microseismic, and MLS data at voxel level. Utilized statistical inference for change detection in framework. Found correlations between MLS changes, fault proximity, and seismic energy. [ABSTRACT FROM AUTHOR]

The precise extraction of road boundaries is an essential task to obtain road infrastructure data that can support various applications, such as maintenance, autonomous driving, vehicle navigation, and the generation of high-definition maps (HD map). Despite promising outcomes in prior studies, challenges persist in road extraction, particularly in discerning diverse road types. The proposed methodology integrates state-of-the-art techniques like DBSCAN and RANSAC, aiming to establish a universally applicable approach for diverse mobile mapping systems. This effort represents a pioneering step in extracting road information from image-based point cloud data. To assess the efficacy of the proposed method, we conducted experiments using a large-scale dataset acquired by two mobile mapping systems on the Yıldız Technical University campus; one system was configured as a mobile LiDAR system (MLS), while the other was equipped with cameras to operate as a photogrammetry-based mobile mapping system (MMS). Using manually measured reference road boundary data, we evaluated the completeness, correctness, and quality parameters of the road extraction performance of our proposed method based on two datasets. The completeness rates were 93.2% and 84.5%, while the correctness rates were 98.6% and 93.6%, respectively. The overall quality of the road curb extraction was 93.9% and 84.5% for the two datasets. Our proposed algorithm is capable of accurately extracting straight or curved road boundaries and curbs from complex point cloud data that includes vehicles, pedestrians, and other obstacles in urban environment. Furthermore, our experiments demonstrate that the algorithm can be applied to point cloud data acquired from different systems, such as MLS and MMS, with varying spatial resolutions and accuracy levels. [ABSTRACT FROM AUTHOR]

Efficient geometric evaluation of roads and tunnels is crucial to traffic management, especially in post-disaster situations. This paper reports on a study of the geometric feature detection method based on multi-sensor mobile laser scanning (MLS) system data. A portable, low-cost system that can be mounted on vehicles and utilizes integrated laser scanning devices was developed. Coordinate systems and timestamps from numerous devices were merged to create 3D point clouds of objects being measured. Feature points reflecting the geometric information of measuring objects were retrieved based on changes in the point cloud's shape, which contributed to measuring the road width, vertical clearance, and tunnel cross section. Self-developed software was used to conduct the measuring procedure, and a real-time online visualized platform was designed to reconstruct 3D models of the measured objects, forming a 3D digital map carrying the obtained geometric information. Finally, a case study was carried out. The measurement results of several representative nodes are discussed here, verifying the robustness of the proposed system. In addition, the main sources of interference are also discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] Deep learning (DL) semantic segmentation of tunnel point cloud shows an efficient path for applications related to subway tunnel scenes, such as health inspection and building information modelling (BIM). Current methods for tunnel point cloud segmentation often suffer from a shortage of benchmarks. This paper proposed a large-scale, multi-modal dataset for semantic segmentation of subway tunnel point cloud called subway tunnel segmentation dataset (STSD). The STSD comprises point clouds and projected images annotated into 12 categories, encompassing three types of subway tunnels with a combined length exceeding 2700 m, totaling over 2.26 billion points. A novel approach for DL semantic segmentation of subway tunnel point clouds is proposed herein. This approach enables the direct utilization of image-based DL segmentation networks on subway tunnel point clouds. Furthermore, it incorporates a lossless coordinate transformation method capable of converting tunnel point clouds of any cross-section shape into images with minimal information loss. Further evaluation of several classic or state-of-the-art 2D and 3D DL semantic segmentation models shows the feasibility of the approach and dataset. The best 2D model achieves a mIoU of 86.26% and outperforms the best 3D model by almost 10%. This research provides a novel approach for DL semantic segmentation in subway tunnel point clouds, contributes a large-scale, multi-modal dataset for the tunnel semantic segmentation, and creates a benchmark for further evaluation of the corresponding algorithms. [ABSTRACT FROM AUTHOR]

Accidents and fatalities during scaffolding work are significant concerns in the construction industry. Scaffold monitoring technology based on 3D reconstruction can offer a potential solution to prevent such fatalities. However, the practical application of this technology at actual construction sites faces a notable challenge due to the presence of safety nets. Safety nets hinder the acquisition of complete scaffold point clouds necessary for reconstruction. To address this issue, we propose a novel registration method for scaffold point clouds that considers safety nets, employing sensor fusion and deep learning techniques. The method consists of three steps: 1) LiDAR-camera calibration and SLAM-based point cloud data acquisition, 2) object detection and coordinate transformation, and 3) identification of scaffold installation stages and point cloud registration. The proposed method was verified to be effective in registering scaffold point clouds without safety nets. • The proposed method registers scaffold point clouds considering the installation of safety nets for 3D reconstruction. • Sensor fusion and deep learning are used to estimate the global coordinates of objects during scaffolding work. • By identifying the installation stages of scaffolds, a point cloud map without safety nets is obtained. • The effectiveness of the method is validated at the real scaffold installation site. [ABSTRACT FROM AUTHOR]