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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]

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]

Mobile mapping systems are part of modern data collection in geodesy. It is one of many surveying methods where field collection is performed in a short time. Among their advantages are cost savings and better visualisation than classic surveying methods. This article is focused on accuracy determinations in urban built-up areas of mobile laser scanning using the Riegl VMX-2HA system. These areas, where there is a combination of dense housing and trees, are an integral part of cities. Their diversity and complexity make surveying by other surveying methods time-consuming and complicated. In particular, the GNSS RTK method encounters problematic locations where sky obscuration by surrounding elements reduces measurement accuracy. Data collection was performed on a test base in the city of Pilsen, Czech Republic. The base includes 27 control points and more than 100 checkpoints. Two sets of coordinates were created for the points; the first set is calculated using tied net adjustment and the second one is determined by RTK GNSS measurements. Point cloud calculations were processed in RiPROCESS software from Riegl, using different configurations and qualities of the control points. Each point cloud was analysed including the determination of point cloud deviations. This article is also dedicated to the identification of problematic spots, where measurement can be degraded. The results presented in this paper show the influence of the quality and different spacing of the control points on the point cloud, its accuracy compared to the precise points, and the global and local deformation of the point cloud. This work can be used as a basis for replacing classical surveying methods with a more efficient mobile laser scanning method. [ABSTRACT FROM AUTHOR]

The traditional measurement method (e.g., field survey) of tree diameter circumference often has high labor costs and is time-consuming. Mobile laser scanning (MLS) is a powerful tool for measuring forest diameter at breast height (DBH). However, the accuracy of point cloud registration seriously affects the results of DBH measurements. To address this issue, this paper proposes a new method for extracting tree DBH parameters; it achieves the purpose of efficient and accurate extraction of tree DBH by point cloud filtering, single-tree instance segmentation, and least squares circle fitting. Firstly, the point cloud data of the plantation forest samples were obtained by a self-constructed unmanned vehicle-mounted mobile laser scanning system, and the ground point cloud was removed using cloth simulation filtering (CSF). Secondly, fast Euclidean clustering (FEC) was employed to segment the single-tree instances, and the point cloud slices at breast height were extracted based on the point sets of single-tree instances, which were then fitted in two dimensions using the horizontally projected point cloud slices. Finally, a circle fitting algorithm based on intensity weighted least squares (IWLS) was proposed to solve the optimal circle model based on 2D point cloud slices, to minimize the impact of misaligned point clouds on DBH measures. The results showed that the mean absolute error (MAE) of the IWLS method was 2.41 cm, the root mean square error (RMSE) was 2.81 cm, and the relative accuracy was 89.77%. Compared with the random sample consensus (RANSAC) algorithm and ordinary least squares (OLS), the MAE was reduced by 36.45% and 9.14%, the RMSE was reduced by 40.90% and 12.26%, and the relative accuracy was improved by 8.99% and 1.63%, respectively. The R2 value of the fitted curve of the IWLS method was the closest to 1, with the highest goodness of fit and a significant linear correlation with the true value. The proposed intensity weighted least squares circle-fitting DBH extraction method can effectively improve the DBH extraction accuracy of mobile laser scanning point cloud data and reduce the influence of poorly aligned point clouds on DBH fitting. [ABSTRACT FROM AUTHOR]

Precise individual tree or sample-based inventories derived from 3D point cloud data of mobile laser scanning can improve our comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests. This study assessed the performance of a handheld laser scanning system (HLS) for the extraction of tree position, diameter at breast height (DBH), and tree height (H) in an urban area. A total of 2083 trees of 13 species from 34 plots were analyzed. The results showed that the registration of tree positions using ground control points (GCPs) demonstrated high accuracy, with errors consistently below 0.4 m, except for a few instances. The extraction accuracy of DBH for all trees and individual species remained consistently high, with a total root mean square error (RMSE) of 2.06 cm (6.89%) and a bias of 0.62 cm (2.07%). Notably, broad-leaved trees outperformed coniferous trees, with RMSE and bias values of 1.86 cm (6%) and 0.76 cm (2.46%), respectively, compared to 2.54 cm (9.46%) and 0.23 cm (0.84%), respectively. The accuracy of H extraction varied significantly among different species, with R2 values ranging from 0.65 to 0.92. Generally, both DBH and H were underestimated compared to ground measurements. Linear mixed-effects models (LMEs) were applied to evaluate factors affecting the performance of HLS with the plot as a random factor. LME analysis revealed that plant type and terrain significantly influenced the accuracy of DBH and H derived from HLS data, while other fixed factors such as plot area, tree density, and trajectory length showed no significance. With a large sample size, we concluded that the HLS demonstrated sufficient accuracy in extracting individual tree parameters in urban forests. [ABSTRACT FROM AUTHOR]

Mobile laser scanning (MLS) systems have become an important technology for collecting and measuring road information for highway maintenance and reconstruction services. However, the efficient and accurate extraction of unstructured road surfaces from MLS point cloud data collected on highways is challenging. Specifically, the complex and unstructured characteristics of road surveying point cloud data lead to traditional 3D point cloud segmentation. When traditional 3D point cloud algorithms extract unstructured road surfaces, over-segmentation and under-segmentation often occur, which affects efficiency and accuracy. To solve these problems, this study introduces an enhanced road extraction method that integrates supervoxel and trajectory information into a traditional region growing algorithm. The method involves two main steps: first, a supervoxel data structure is applied to reconstruct the original MLS point cloud data, which diminishes the calculation time of the point cloud feature vector and accelerates the merging speed of a similar region; second, the trajectory information of the vehicle is used to optimize the seed selection strategy of the regio growing algorithm, which improves the accuracy of road surface extraction. Finally, two typical highway section tests (flat road and slope road) were conducted to validate the positioning performance of the proposed algorithm in an MLS point cloud. The results show that, compared with three kinds of traditional road surface segmentation algorithms, our method achieves an average extraction recall and precision of 99.1% and 96.0%, and by calculating the recall and precision, an F1 score of 97.5% can be obtained to evaluate the performance of the proposed method, for both datasets. Additionally, our method exhibits an average road surface extraction time that is 45.0%, 50.3%, and 55.8% faster than those of the other three automated segmentation algorithms. [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]

This paper presents a practical and efficient workflow for deformation monitoring of transport infrastructure. We propose using commercially available mobile laser scanning (MLS) systems to scan civil infrastructure while driving by in a car or rail vehicle. Our processing pipeline corrects for MLS-specific systematic deviations and models deformations from point clouds of two epochs. Following the concept of rigorous deformation analysis, we statistically test the deformations for significance. The required point cloud uncertainty may be obtained in two ways. First option is empirically by multiple passes and, secondly, by prediction with a learned stochastic model. We apply the method to three retaining structures and evaluate results based on ground truth geodetic surveys. The deviations did not exceed 10 mm, even for complex object surfaces or when traveling at 80 km/h. We demonstrate that the method is capable of revealing displacements in the centimeter range without relying on any installations on the structure. The approach shows great potential as a novel, efficient tool for detecting and quantifying defective structures in a road and railway network. [ABSTRACT FROM AUTHOR]

The Mobile Laser Scanning (MLS) data inevitably includes dynamic objects because there are always other vehicles (e.g., other cars, motorbikes, bikes, etc.) moving in the area near the MLS data collection vehicle on the road. These dynamic objects need to be removed in advance for many point cloud applications. This paper designs an efficient and memory-friendly data frame aware optimized Octomap-based dynamic object detection and removal method for MLS data. Firstly, the input MLS data is split into multiple data frames based on the timestamp. Each data frame is inserted into a separate Octomap with part of its neighbouring data frames. A statistics-based method is applied to each data frame to find the passable voxel cell space (free space) in Octomap and all points in the free space are extracted as free points. Second, the region of interest (ROI) related to the dynamic object is delineated to retain free points related to dynamic objects. Then the free-point rate and the multi-return rate are calculated to further remove noise and vegetation points from free points. Finally, the fixed radius search is used to extract dynamic objects from the filtered free points. The proposed method is tested in four case sites in Delft, the Netherlands. Results show that 84.98% of dynamic objects are detected and extracted correctly. The proposed method is 18.27% more efficient on average than the original Octomap method, can be further accelerated by parallel computing, and only needs 39.40% of the maximum memory consumption.

Mobile mapping systems are part of modern data collection in geodesy. It is one of many surveying methods where field collection is performed in a short time. Among their advantages are cost savings and better visualisation than classic surveying methods. This article is focused on accuracy determinations in urban built-up areas of mobile laser scanning using the Riegl VMX-2HA system. These areas, where there is a combination of dense housing and trees, are an integral part of cities. Their diversity and complexity make surveying by other surveying methods time-consuming and complicated. In particular, the GNSS RTK method encounters problematic locations where sky obscuration by surrounding elements reduces measurement accuracy. Data collection was performed on a test base in the city of Pilsen, Czech Republic. The base includes 27 control points and more than 100 checkpoints. Two sets of coordinates were created for the points; the first set is calculated using tied net adjustment and the second one is determined by RTK GNSS measurements. Point cloud calculations were processed in RiPROCESS software from Riegl, using different configurations and qualities of the control points. Each point cloud was analysed including the determination of point cloud deviations. This article is also dedicated to the identification of problematic spots, where measurement can be degraded. The results presented in this paper show the influence of the quality and different spacing of the control points on the point cloud, its accuracy compared to the precise points, and the global and local deformation of the point cloud. This work can be used as a basis for replacing classical surveying methods with a more efficient mobile laser scanning method.

The Mobile Laser Scanning (MLS) data inevitably includes dynamic objects because there are always other vehicles (e.g., other cars, motorbikes, bikes, etc.) moving in the area near the MLS data collection vehicle on the road. These dynamic objects need to be removed in advance for many point cloud applications. This paper designs an efficient and memory-friendly data frame aware optimized Octomap-based dynamic object detection and removal method for MLS data. Firstly, the input MLS data is split into multiple data frames based on the timestamp. Each data frame is inserted into a separate Octomap with part of its neighbouring data frames. A statistics-based method is applied to each data frame to find the passable voxel cell space (free space) in Octomap and all points in the free space are extracted as free points. Second, the region of interest (ROI) related to the dynamic object is delineated to retain free points related to dynamic objects. Then the free-point rate and the multi-return rate are calculated to further remove noise and vegetation points from free points. Finally, the fixed radius search is used to extract dynamic objects from the filtered free points. The proposed method is tested in four case sites in Delft, the Netherlands. Results show that 84.98% of dynamic objects are detected and extracted correctly. The proposed method is 18.27% more efficient on average than the original Octomap method, can be further accelerated by parallel computing, and only needs 39.40% of the maximum memory consumption. [ABSTRACT FROM AUTHOR]

Precise individual tree or sample-based inventories derived from 3D point cloud data of mobile laser scanning can improve our comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests. This study assessed the performance of a handheld laser scanning system (HLS) for the extraction of tree position, diameter at breast height (DBH), and tree height (H) in an urban area. A total of 2083 trees of 13 species from 34 plots were analyzed. The results showed that the registration of tree positions using ground control points (GCPs) demonstrated high accuracy, with errors consistently below 0.4 m, except for a few instances. The extraction accuracy of DBH for all trees and individual species remained consistently high, with a total root mean square error (RMSE) of 2.06 cm (6.89%) and a bias of 0.62 cm (2.07%). Notably, broad-leaved trees outperformed coniferous trees, with RMSE and bias values of 1.86 cm (6%) and 0.76 cm (2.46%), respectively, compared to 2.54 cm (9.46%) and 0.23 cm (0.84%), respectively. The accuracy of H extraction varied significantly among different species, with R2 values ranging from 0.65 to 0.92. Generally, both DBH and H were underestimated compared to ground measurements. Linear mixed-effects models (LMEs) were applied to evaluate factors affecting the performance of HLS with the plot as a random factor. LME analysis revealed that plant type and terrain significantly influenced the accuracy of DBH and H derived from HLS data, while other fixed factors such as plot area, tree density, and trajectory length showed no significance. With a large sample size, we concluded that the HLS demonstrated sufficient accuracy in extracting individual tree parameters in urban forests.

Mobile laser scanning (MLS) systems have become an important technology for collecting and measuring road information for highway maintenance and reconstruction services. However, the efficient and accurate extraction of unstructured road surfaces from MLS point cloud data collected on highways is challenging. Specifically, the complex and unstructured characteristics of road surveying point cloud data lead to traditional 3D point cloud segmentation. When traditional 3D point cloud algorithms extract unstructured road surfaces, over-segmentation and under-segmentation often occur, which affects efficiency and accuracy. To solve these problems, this study introduces an enhanced road extraction method that integrates supervoxel and trajectory information into a traditional region growing algorithm. The method involves two main steps: first, a supervoxel data structure is applied to reconstruct the original MLS point cloud data, which diminishes the calculation time of the point cloud feature vector and accelerates the merging speed of a similar region; second, the trajectory information of the vehicle is used to optimize the seed selection strategy of the regio growing algorithm, which improves the accuracy of road surface extraction. Finally, two typical highway section tests (flat road and slope road) were conducted to validate the positioning performance of the proposed algorithm in an MLS point cloud. The results show that, compared with three kinds of traditional road surface segmentation algorithms, our method achieves an average extraction recall and precision of 99.1% and 96.0%, and by calculating the recall and precision, an F1 score of 97.5% can be obtained to evaluate the performance of the proposed method, for both datasets. Additionally, our method exhibits an average road surface extraction time that is 45.0%, 50.3%, and 55.8% faster than those of the other three automated segmentation algorithms.

Tree species characterise biodiversity, health, economic potential, and resilience of an ecosystem, for example. Tree species classification based on remote sensing data, however, is known to be a challenging task. In this paper, we study for the first time the feasibility of tree species classification using high-density point clouds collected with an airborne close-range multispectral laser scanning system – a technique that has previously proved to be capable of providing stem curve and volume accurately and rapidly for standing trees. To this end, we carried out laser scanning measurements from a helicopter on 53 forest sample plots, each with a size of 32 m × 32 m. The plots covered approximately 5500 trees in total, including Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H.Karst.), and deciduous trees such as Downy birch (Betula pubescens Ehrh.) and Silverbirch (Betula pendula Roth). The multispectral laser scanning system consisted of integrated Riegl VUX-1HA, miniVUX-3UAV, and VQ-840-G scanners (Riegl GmbH, Austria) operating at wavelengths of 1550 nm, 905 nm, and 532 nm, respectively. A new approach, layer-by-layer segmentation, was developed for individual tree detection and segmentation from the dense point cloud data. After individual tree segmentation, 249 features were computed for tree species classification, which was tested with approximately 3000 trees. The features described the point cloud geometry as well as single-channel and multi-channel reflectance metrics. Both feature selection and the tree species classification were conducted using the random forest method. Using the layer-by-layer segmentation algorithm, trees in the dominant and co-dominant categories were found with detection rates of 89.5% and 77.9%, respectively, whereas suppressed trees were detected with a success rate of 15.2%–42.3%, clearly improving upon the standard watershed segmentation. The overall accuracy of the tree species classification was 73.1% when using geometric features from the 1550 nm scanner data and 86.6% when combining the geometric features with reflectance information of the 1550 nm data. The use of multispectral reflectance and geometric features improved the overall classification accuracy up to 90.8%. Classification accuracies were as high as 92.7% and 93.7% for dominant and co-dominant trees, respectively.

At the present time, many publicly available point cloud datasets exist, which are mainly focused on autonomous driving. The objective of this study is to develop a new large-scale mobile 3D LiDAR point cloud dataset for outdoor scene semantic segmentation tasks, which has a classification scheme suitable for geospatial applications. Our dataset (Saint Petersburg 3D) contains both real-world (34 million points) and synthetic (34 million points) subsets that were acquired using real and virtual sensors with the same characteristics. An original classification scheme is proposed that contains a set of 10 universal object categories into which any scene represented by dense outdoor mobile LiDAR point clouds can be divided. The evaluation procedure for semantic segmentation of point clouds for geospatial applications is described. An experiment with the Kernel Point Fully Convolution Neural Network model trained on the proposed dataset was carried out. We obtained an overall 92.56% mIoU, which demonstrates the high efficiency of using deep learning models for point cloud semantic segmentation for geospatial applications in accordance with the proposed classification scheme. [ABSTRACT FROM AUTHOR]

The 3D semantic segmentation of a LiDAR point cloud is essential for various complex infrastructure analyses such as roadway monitoring, digital twin, or even smart city development. Different geometric and radiometric descriptors or diverse combinations of point descriptors can extract objects from LiDAR data through classification. However, the irregular structure of the point cloud is a typical descriptor learning problem—how to consider each point and its surroundings in an appropriate structure for descriptor extraction? In recent years, convolutional neural networks (CNNs) have received much attention for automatic segmentation and classification. Previous studies demonstrated deep learning models' high potential and robust performance for classifying complicated point clouds and permutation invariance. Nevertheless, such algorithms still extract descriptors from independent points without investigating the deep descriptor relationship between the center point and its neighbors. This paper proposes a robust and efficient CNN-based framework named D-Net for automatically classifying a mobile laser scanning (MLS) point cloud in urban areas. Initially, the point cloud is converted into a regular voxelized structure during a preprocessing step. This helps to overcome the challenge of irregularity and inhomogeneity. A density value is assigned to each voxel that describes the point distribution within the voxel's location. Then, by training the designed CNN classifier, each point will receive the label of its corresponding voxel. The performance of the proposed D-Net method was tested using a point cloud dataset in an urban area. Our results demonstrated a relatively high level of performance with an overall accuracy (OA) of about 98% and precision, recall, and F1 scores of over 92%. [ABSTRACT FROM AUTHOR]

The continuous flow of autonomous vehicle-based data could revolutionize current map updating procedures and allow completely new types of mapping applications. Therefore, in this article, we demonstrate the feasibility of using perception data of autonomous vehicles to replace traditionally conducted mobile mapping surveys with a case study focusing on updating a register of roadside city trees. In our experiment, we drove along a 1.3-km-long road in Helsinki to collect laser scanner data using our autonomous car platform ARVO, which is based on a Ford Mondeo hybrid passenger vehicle equipped with a Velodyne VLS-128 Alpha Prime scanner and other high-grade sensors for autonomous perception. For comparison, laser scanner data from the same region were also collected with a specially-planned high-grade mobile mapping laser scanning system. Based on our results, the diameter at breast height, one of the key parameters of city tree registers, could be estimated with a lower root-mean-square error from the perception data of the autonomous car than from the specially-planned mobile laser scanning survey, provided that time-based filtering was included in the post-processing of the autonomous perception data to mitigate distortions in the obtained point cloud. Therefore, appropriately performed post-processing of the autonomous perception data can be regarded as a viable option for keeping maps updated in road environments. However, point cloud-processing algorithms may need to be adapted for the post-processing of autonomous perception data due to the differences in the sensors and their arrangements compared to designated mobile mapping systems. We also emphasize that time-based filtering may be required in the post-processing of autonomous perception data due to point cloud distortions around objects seen at multiple times. This highlights the importance of saving the time stamp for each data point in the autonomous perception data or saving the temporal order of the data points. [ABSTRACT FROM AUTHOR]

The automatic extraction of individual tree from mobile laser scanning (MLS) scenes has important applications in tree growth monitoring, tree parameter calculation and tree modeling. However, trees often grow in rows and tree crowns overlap with varying shapes, and there is also incompleteness caused by occlusion, which makes individual tree extraction a challenging problem. In this paper, we propose a trunk-constrained and tree structure analysis method to extract trees from scanned urban scenes. Firstly, multi-feature enhancement is performed via PointNet to segment the tree points from raw urban scene point clouds. Next, the candidate local tree trunk clusters are obtained by clustering based on the intercepted local tree trunk layer, and the real local tree trunk is obtained by removing noise data. Then, the trunk is located and extracted by combining circle fitting and region growing, so as to obtain the center of the tree crown. Further, the points near the tree's crown (core points) are segmented through distance difference, and the tree crown boundary (boundary points) is distinguished by analyzing the density and centroid deflection angle. Therefore, the core and boundary points are deleted to obtain the remaining points (intermediate points). Finally, the core, intermediate and boundary points, as well as the tree trunks, are combined to extract individual tree. The performance of the proposed method was evaluated on the Pairs-Lille-3D dataset, which is a benchmark for point cloud classification, and data were produced using a mobile laser system (MLS) applied to two different cities in France (Paris and Lille). Overall, the precision, recall, and F1-score of instance segmentation were 90.00%, 98.22%, and 99.08%, respectively. The experimental results demonstrate that our method can effectively extract trees with multiple rows of occlusion and improve the accuracy of tree extraction. [ABSTRACT FROM AUTHOR]

Gardens play a key role in the definition of the cultural landscape since they reflect the culture, identity, and history of a people. They also contribute to the ecological balance of the city. Despite the fact that gardens have an historic and social value, they are not protected as much as the rest of the existing heritage, such as architecture and archaeological sites. While methods of built-heritage mapping and monitoring are increasing and constantly improving to reduce built-heritage loss and the severe impact of natural disasters, the documentation and survey techniques for gardens are often antiquated. In addition, inventories are typically made by non-updated/updateable reports, and they are rarely in digital format or in 3D. This paper presents the results of a comprehensive study on the latest technology for laser scanning in gardens. We compared static terrestrial laser scanning and mobile laser scanning point clouds generated by the Focus 3D S120 and the Leica BLK2GO, respectively, to evaluate their quality for documentation, estimate tree attributes, and terrain morphology. The evaluation is based on visual observation, C2C comparisons, and terrain information extraction capabilities, i.e., M3C2 comparisons for topography, DTM generation, and contour lines. Both methods produced useful outcomes for the scope of the research within their limitations. Terrestrial laser scanning is still the method that offers accurate point clouds with a higher point density and less noise. However, the more recent mobile laser scanning is able to survey in less time, significantly reducing the costs for site activities, data post-production, and registration. Both methods have their own restrictions that are amplified by site features, mainly the lack of plans for the geometric alignment of scans and the simultaneous location and mapping (SLAM) process. We offer a critical description of the issues related to the functionality of the two sensors, such as the operative range limit, light dependency, scanning time, point cloud completeness and size, and noise level. [ABSTRACT FROM AUTHOR]

Although point features have shown their usefulness in classification with Machine Learning, point cloud visualization enhancement methods focus mainly on lighting. The visualization of point features helps to improve the perception of the 3D environment. This paper proposes Multi Feature-Rich Synthetic Colour (MFRSC) as an alternative non-photorealistic colour approach of natural-coloured point clouds. The method is based on the selection of nine features (reflectance, return number, inclination, depth, height, point density, linearity, planarity, and scattering) associated with five human perception descriptors (edges, texture, shape, size, depth, orientation). The features are reduced to fit the RGB display channels. All feature permutations are analysed according to colour distance with the natural-coloured point cloud and Image Quality Assessment. As a result, the selected feature permutations allow a clear visualization of the scene's rendering objects, highlighting edges, planes, and volumetric objects. MFRSC effectively replaces natural colour, even with less distorted visualization according to BRISQUE, NIQUE and PIQE. In addition, the assignment of features in RGB channels enables the use of MFRSC in software that does not support colorization based on point attributes (most commercially available software). MFRSC can be combined with other non-photorealistic techniques such as Eye-Dome Lighting or Ambient Occlusion. [ABSTRACT FROM AUTHOR]

Currently, the street tree resource survey using Mobile laser scanning (MLS) represents a hot spot around the world. Refined trunk extraction is an essential step for 3D reconstruction of street trees. However, due to scanning errors and the effects of occlusion by various types of features in the urban environment, street tree point cloud data processing has the problem of excessive noise. For the noise points that are difficult to remove using statistical methods in close proximity to the tree trunk, we propose an adaptive trunk extraction and denoising method for street trees based on an improved iForest (Isolation Forest) algorithm. Firstly, to extract the individual tree trunk points, the trunk and the crown are distinguished from the individual tree point cloud through point cloud slicing. Next, the iForest algorithm is improved by conducting automatic calculation of the contamination and further used to denoise the tree trunk point cloud. Finally, the method is validated with five datasets of different scenes. The results indicate that our method is robust and effective in extracting and denoising tree trunks. Compared with the traditional Statistical Outlier Removal (SOR) filter and Radius filter denoising methods, the denoising accuracy of the proposed method can be improved by approximately 30% for noise points close to tree trunks. Compared to iForest, the proposed method automatically calculates the contamination, improving the automation of the algorithm. Our method can provide more precise trunk point clouds for 3D reconstruction of street trees. [ABSTRACT FROM AUTHOR]

To reduce the cost of manually annotating training data for supervised classifiers, we propose an automated approach to extract training data of urban objects in six classes: buildings, fences, man-made poles, vegetation, vehicles, and low objects. In this study, two segmentation algorithms are firstly implemented to generate meaningful objects from the non-ground point cloud. Then, we generated valid strict rules to label partial RANSAC (Random Sample Consensus) planes and meaningful objects as training data. The strict rules are built upon the semantic knowledge formed by the features of geometric, eigenvalue, RANSAC plane, multidimensional slice, and relative location. The accuracy of strict rule-based (SRB) training data is higher than 98.5 % for buildings, man-made poles, vegetation, and vehicles. The accuracy of low objects and fences reaches 97.10 % and 94.99 %, respectively. Finally, we compared the performance of the KPConv and PointNET++ networks trained by SRB and manually labeled training data to evaluate the effectiveness of our training data. The KPConv overall accuracy using manually labeled and (SRB) training data are 91.5 % and 86.8 % in the Paris dataset, 95.6 % and 92.0 % in the Freiburg dataset, respectively. The experiments demonstrate that automatically labeled training data can achieve similar accuracy compared to manual labels when coupled with two deep learning networks. Therefore, SRB training data extraction can effectively deal with the problem of training data scarcity and provide significant advancements in urban point cloud classification, where manual labeling of training data remains a crucial challenge. [ABSTRACT FROM AUTHOR]

Mobile laser scanning (MLS) systems with light detection and ranging (LiDAR) sensors, global navigation satellite system (GNSS) receivers, and inertial measurement unit (IMU) sensors have been widely used in applications such as smart city facility censuses and highdefinition mapping. However, because of the complexity of urban environments, there are often decimeter-to-meter-level position deviations between multiple scanning data of the same area. To solve this problem, we propose a trajectory correction model for registering city-scale MLS point cloud data. First, the proposed model segments the trajectory in terms of the data accuracy and then segments the data with segmented subtrajectories while maintaining the relationship of matching pairs in overlapping areas. Second, the proposed model transforms the matching pairs based on multiple metrics registration, which uses the poles and planar feature points extracted from the point cloud data using local geometric features. Finally, the global pose optimization method is used to improve the consistency of the MLS point clouds. In data registration experiments on different urban scenes, the proposed method performed well with high robustness and decreased the position deviations by 70%. [ABSTRACT FROM AUTHOR]

A mobile laser scanning (MLS) system can acquire railway scene information quickly and provide a data foundation for regular railway inspections. The location of the catenary support device in an electrified railway system has a direct impact on the regular operation of the power supply system. However, multi-type support device data accounts for a tiny proportion of the whole railway scene, resulting in its poor characteristic expression in the scene. Therefore, using traditional point cloud filtering or point cloud segmentation methods alone makes it difficult to achieve an effective segmentation and extraction of the support device. As a result, this paper proposes an automatic extraction algorithm for complex railway support devices based on MLS point clouds. First, the algorithm obtains hierarchies of the pillar point clouds and the support device point clouds in the railway scene through high stratification and then realizes the noise that was point-cloud-filtered in the scene. Then, the center point of the pillar device is retrieved from the pillar corridor by a neighborhood search, and then the locating and initial extracting of the support device are realized based on the relatively stable spatial topological relationship between the pillar and the support device. Finally, a post-processing optimization method integrating the pillar filter and the voxelized projection filter is designed to achieve the accurate and efficient extraction of the support device based on the feature differences between the support device and other devices in the initial extraction results. Furthermore, in the experimental part, we evaluate the treatment effect of the algorithm in six types of support devices, three types of support device distribution scenes, and two types of railway units. The experimental results show that the average extraction IoU of the multi-type support device, support device distribution scenes, and railway unit were 97.20%, 94.29%, and 96.11%, respectively. In general, the proposed algorithm can achieve the accurate and efficient extraction of various support devices in different scenes, and the influence of the algorithm parameters on the extraction accuracy and efficiency is elaborated in the discussion section. [ABSTRACT FROM AUTHOR]

The traditional railway survey adopts a manual observation method, such as a total station measuring system. This method has high precision, but the amount of data is small, and the measurement efficiency is low. Manual measurement cannot meet the requirements of dynamic continuous high-precision holographic measurement during railway outages. Mobile laser scanning is a mobile mapping system based mainly on a laser scanner, inertial measurement unit (IMU) and panoramic camera. Mobile laser scanning has the advantages of high efficiency, high precision and automation. However, integrating inertial navigation data and mobile laser scanning data to obtain real 3D information about railways has always been an urgent problem to be solved. Therefore, a point cloud reconstruction method is proposed based on trajectory filtering for a mobile laser scanning system. This paper corrects the odometer data by identifying railway feature points through deep learning and uses Rauch–Tung–Striebel (RTS) filtering to optimize the trajectory results. Combined with the railway experimental track data, the maximum difference in the east and north coordinate direction can be controlled within 7 cm, and the average elevation error is 2.39 cm. This paper applies a multi-sensor integrated mobile detection system to railway detection. It is of great significance to the healthy development of the intelligent railway system. [ABSTRACT FROM AUTHOR]

Urban trees are vital elements of outdoor scenes via mobile laser scanning (MLS), accurate individual trees detection from disordered, discrete, and high-density MLS is an important basis for the subsequent analysis of city management and planning. However, trees cannot be easily extracted because of the occlusion with other objects in urban scenes. In this work, we propose a coarse-to-fine individual trees detection method from MLS point cloud data (PCD) based on treetop points extraction and radius expansion. Firstly, an improved semantic segmentation deep network based on PointNet is applied to segment tree points from the scanned urban scene, which combining spatial features and dimensional features. Next, through calculating the local maximum, the candidate treetop points are located. In addition, the optimized treetop points are extracted after the tree point projection plane was filtered to locate the candidate treetop points, and a distance rule is used to eliminate the pseudo treetop points then the optimized treetop points are obtained. Finally, after the initial clustering of treetop points and vertical layering of tree points, a top-down layer-by-layer segmentation based on radius expansion to realize the complete individual extraction of trees. The effectiveness of the proposed method is tested and evaluated on five street scenes in point clouds from Oakland outdoor MLS dataset. Furthermore, the proposed method is compared with two existing individual trees segmentation methods. Overall, the precision, recall, and F-score of instance segmentation are 98.33%, 98.33%, and 98.33%, respectively. The results indicate that our method can extract individual trees effectively and robustly in different complex environments. [ABSTRACT FROM AUTHOR]

Traditional precise engineering surveys adopt manual static, discrete observation, which cannot meet the dynamic, continuous, high-precision and holographic fine measurements required for large-scale infrastructure construction, operation and maintenance, where mobile laser scanning technology is becoming popular. However, in environments without GNSS signals, it is difficult to use mobile laser scanning technology to obtain 3D data. We fused a scanner with an inertial navigation system, odometer and inclinometer to establish and track mobile laser measurement systems. The control point constraints and Rauch-Tung-Striebel filter smoothing were fused, and a 3D point cloud generation method based on multi-sensor fusion was proposed. We verified the method based on the experimental data; the average deviation of positioning errors in the horizontal and elevation directions were 0.04 m and 0.037 m, respectively. Compared with the stop-and-go mode of the Amberg GRP series trolley, this method greatly improved scanning efficiency; compared with the method of generating a point cloud in an absolute coordinate system based on tunnel design data conversion, this method improved data accuracy. It effectively avoided the deformation of the tunnel, the sharp increase of errors and more accurately and quickly processed the tunnel point cloud data. This method provided better data support for subsequent tunnel analysis such as 3D display, as-built surveying and disease system management of rail transit tunnels. [ABSTRACT FROM AUTHOR]

Mobile Laser Scanning (MLS) is rapidly increasing in popularity for the capture and quantification of vegetation structure in natural landscapes. However, understanding the optimal implementation for forest capture in the field is still limited, particularly regarding the choice in acquisition path and length followed during data capture. How variable is the characterisation of vegetation structure when using different acquisition paths, and is this difference likely to be significant for most users? In this study we compared four acquisition path designs commonly cited in the literature to determine the importance of this choice for users when capturing vegetation structure. MLS point clouds were systematically captured to repeatedly survey study plots in a closed-canopy forest ecosystem in south-eastern Australia. Digital elevation models, canopy height models, and vertical voxel occupancy profiles were derived to illustrate the sensitivity of the path's length and configuration to variability in the quantification of vegetation structure. We found strong agreement between the acquisition path designs that increased as path length increased. No significant differences were found in digital elevation models derived at 75% and 100% path lengths. However, differences were observed in two and four of the 25% and 50% path length plots respectively. Significant differences in canopy height model data were only found in one plot at the 50% path length. Mean differences to the reference digital elevation models was 0.09 m across all plots, and 0.4 m for the canopy height models. Voxel occupancy profiles showed the greatest agreement between the understory and canopy where the mean difference between acquisition paths across all plots was 5.72%. Our findings have significant implications for the use of MLS from an operational perspective, as they illustrate the variability and reliability of their use in natural forested landscapes. Users can choose to balance their choice in acquisition path design with path length to meet requirements such as efficiency or ease of data capture in the field. • Terrestrial mobile laser scanning acquisition path designs and lengths were compared. • Acquisition path design did not significantly affect vegetation structure characterisation. • Shorter path lengths significantly influenced the capture of vegetation structure. [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]

Visibility analysis plays a vital role in the design and placing of traffic signs in the urban street environment. This work investigates the occlusion detection of traffic lights and traffic signs caused by vegetation. The presented analysis method is built upon the inputs from the expected situation reflected by a highly detailed 3D city model and the as-is situation captured by 3D Mobile Laser Scanning (MLS). The model contains the location and orientation of streets, traffic lights, and traffic signs; the measurements add detail on irregular-shaped and morphing objects such as vegetation, respectively. The analysis covers the visibility of traffic lights and traffic signs by ray-tracing in an occupancy grid that is generated by the voxelization of the space. The voxels facilitate the distinction between occupied and empty space. The identification of unknown volumes is added and considered in the decision process, to cope with the regions invisible to the sensor. As output, we provide a visibility metric and detailed 3D space descriptions on different levels of granularity, including the knowledge of the semantic classes of traversed voxels. During the whole process, the awareness of unknown volumes is added to an otherwise binary decision between visible and invisible targets. Experiments are conducted on the TUM-MLS-2016 dataset. Results demonstrate that the proposed method is feasible for the detection of occlusions by vegetation in the street scenario, and reveal that the identification of unknown volumes proves necessary for a reliable interpretation of the measurements.

Outdated or sketchy inventory of street furniture may misguide the planners on the renovation and upgrade of transportation infrastructures, thus posing potential threats to traffic safety. Previous studies have taken their steps using point clouds or street-view imagery (SVI) for street furniture inventory, but there remains a gap to balance semantic richness, localization accuracy and working efficiency. Therefore, this paper proposes an effective pipeline that combines SVI and point clouds for the inventory of street furniture. The proposed pipeline encompasses three steps: (1) Off-the-shelf street furniture detection models are applied on SVI for generating two-dimensional (2D) proposals and then three-dimensional (3D) point cloud frustums are accordingly cropped; (2) The instance mask and the instance 3D bounding box are predicted for each frustum using a multi-task neural network; (3) Frustums from adjacent perspectives are associated and fused via multi-object tracking, after which the object-centric instance segmentation outputs the final street furniture with 3D locations and semantic labels. This pipeline was validated on datasets collected in Shanghai and Wuhan, producing component-level street furniture inventory of nine classes. The instance-level mean recall and precision reach 86.4%, 80.9% and 83.2%, 87.8% respectively in Shanghai and Wuhan, and the point-level mean recall, precision, weighted coverage all exceed 73.7%. [ABSTRACT FROM AUTHOR]

At the present time, many publicly available point cloud datasets exist, which are mainly focused on autonomous driving. The objective of this study is to develop a new large-scale mobile 3D LiDAR point cloud dataset for outdoor scene semantic segmentation tasks, which has a classification scheme suitable for geospatial applications. Our dataset (Saint Petersburg 3D) contains both real-world (34 million points) and synthetic (34 million points) subsets that were acquired using real and virtual sensors with the same characteristics. An original classification scheme is proposed that contains a set of 10 universal object categories into which any scene represented by dense outdoor mobile LiDAR point clouds can be divided. The evaluation procedure for semantic segmentation of point clouds for geospatial applications is described. An experiment with the Kernel Point Fully Convolution Neural Network model trained on the proposed dataset was carried out. We obtained an overall 92.56% mIoU, which demonstrates the high efficiency of using deep learning models for point cloud semantic segmentation for geospatial applications in accordance with the proposed classification scheme.

In most Mobile Laser Scanning (MLS) applications, filtering is a necessary step. In this paper, a segmentation-based filtering method is proposed for MLS point cloud, where a segment rather than an individual point is the basic processing unit. In particular, the MLS point clouds in some blocks are clustered into segments by a surface growing algorithm, and then the object segments are detected and removed. A segment-based filtering method is employed to detect the ground segments. The experiment in this paper uses two MLS point cloud datasets to evaluate the proposed method. Experiments indicate that, compared with the classic progressive TIN (Triangulated Irregular Network) densification algorithm, the proposed method is capable of reducing the omission error, the commission error and total error by 3.62%, 7.87% and 5.54% on average, respectively. [ABSTRACT FROM AUTHOR]

Mobile laser scanning (MLS) is a favored information source for urban areas because of its capacity to gather quick and dense three-dimensional information, and classification studies of these large point clouds are carried out by enriching the data set collected. This study discusses a method for classifying an MLS point cloud by constructing a local neighborhood relationship with cylindrical neighborhood information and augmenting the dataset with geometric features in the absence of RGB images. Two benchmark datasets, TUM-MLS1 and Toronto-3D, were employed. The Random Forest (RF) classifier, which has been preferred in many researches for MLS classification, was built and assessed on eight classes of benchmark datasets for point-based supervised classification. As a result, we achieved 94.5% overall accuracy with only four geometric features for both datasets. When comparing our findings for dataset of TUM-MLS1 to those of a previous study, we found a 2.4% increase in overall accuracy. [ABSTRACT FROM AUTHOR]

The tree diameter at breast height (DBH) is one of the most important variables for monitoring the forest ecology. Mobile laser scanning (MLS), which has been widely applied in the forestry field, makes DBH measurement fast and convenient. However, there are many shrubs and deadwood in the neutral forest environment and the point clouds quality from MLS are easily affected by the environment which results in low single tree segmentation and DBH estimation accuracy. To improve the accuracy in a complex forest environment and low point cloud quality, we propose a relative density segmentation method for the single tree segmentation and DBH estimation method based on multi-height diameters for the DBH estimation. The relative density segmentation method calculates the relative density according to the ratio of density in two different scales, and segments the tree trunks by the higher relative density of trunk point clouds compared with their surroundings points. In the natural forest plot, the precision and recall of the proposed segmentation method reached 0.966 and 0.946, respectively; In the urban forest plot, the precision and recall reached 1 and 0.966, respectively. The proposed DBH estimation method was used to estimate the DBH of trees using multi-height diameters. The multi-height diameters combined with the outlier detection algorithm were able to improve the accuracy and robustness when the trunk point clouds have large noise. For the DBH estimation results, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were 2.5 cm, 11.54%, and 3.17 cm, respectively, in the natural forest plot and 1.65 cm, 6.31%, and 1.97 cm, respectively, in the urban forest plot. The good experiment results indicate that the proposed method can achieve accurate and robust DBH extraction and provide fundamental data for supervision and sustainable development of forest resources.

Currently, mobile laser scanning (MLS) systems can conveniently and rapidly measure the backscattered laser beam properties of the object surfaces in large-scale roadway scenes. Such properties is digitalized as the intensity value stored in the acquired point cloud data, and the intensity as an important information source has been widely used in a variety of applications, including road marking inventory, manhole cover detection, and pavement inspection. However, the collected intensity is often deviated from the object reflectance due to two main factors, i.e. different scanning distances and worn-out surfaces. Therefore, in this paper, we present a new intensity-enhanced method to gradually and efficiently achieve the intensity enhancement in the MLS point clouds. Concretely, to eliminate the intensity inconsistency caused by different scanning distances, the direct relationship between scanning distance and intensity value is modeled to correct the inconsistent intensity. To handle the low contrast between 3D points with different intensities, we proposed to introduce and adapt the dark channel prior for adaptively transforming the intensity information in point cloud scenes. To remove the isolated intensity noises, multiple filters are integrated to achieve the denoising in the regions with different point densities. The evaluations of our proposed method are conducted on four MLS datasets, which are acquired at different road scenarios with different MLS systems. Extensive experiments and discussions demonstrate that the proposed method can exhibit the remarkable performance on enhancing the intensities in MLS point clouds.

Background and Aims In addition to terrestrial laser scanning (TLS), mobile laser scanning (MLS) is increasingly arousing interest as a technique which provides valuable 3-D data for various applications in forest research. Using mobile platforms, the 3-D recording of large forest areas is carried out within a short space of time. Vegetation structure is described by millions of 3-D points which show an accuracy in the millimetre range and offer a powerful basis for automated vegetation modelling. The successful extraction of single trees from the point cloud is essential for further evaluations and modelling at the individual-tree level, such as volume determination, quantitative structure modelling or local neighbourhood analyses. However, high-precision automated tree segmentation is challenging, and has so far mostly been performed using elaborate interactive segmentation methods. Methods Here, we present a novel segmentation algorithm to automatically segment trees in MLS point clouds, applying distance adaptivity as a function of trajectory. In addition, tree parameters are determined simultaneously. In our validation study, we used a total of 825 trees from ten sample plots to compare the data of trees segmented from MLS data with manual inventory parameters and parameters derived from semi-automatic TLS segmentation. Key Results The tree detection rate reached 96 % on average for trees with distances up to 45 m from the trajectory. Trees were almost completely segmented up to a distance of about 30 m from the MLS trajectory. The accuracy of tree parameters was similar for MLS-segmented and TLS-segmented trees. Conclusions Besides plot characteristics, the detection rate of trees in MLS data strongly depends on the distance to the travelled track. The algorithm presented here facilitates the acquisition of important tree parameters from MLS data, as an area-wide automated derivation can be accomplished in a very short time. [ABSTRACT FROM AUTHOR]

The traditional railway survey adopts a manual observation method, such as a total station measuring system. This method has high precision, but the amount of data is small, and the measurement efficiency is low. Manual measurement cannot meet the requirements of dynamic continuous high-precision holographic measurement during railway outages. Mobile laser scanning is a mobile mapping system based mainly on a laser scanner, inertial measurement unit (IMU) and panoramic camera. Mobile laser scanning has the advantages of high efficiency, high precision and automation. However, integrating inertial navigation data and mobile laser scanning data to obtain real 3D information about railways has always been an urgent problem to be solved. Therefore, a point cloud reconstruction method is proposed based on trajectory filtering for a mobile laser scanning system. This paper corrects the odometer data by identifying railway feature points through deep learning and uses Rauch–Tung–Striebel (RTS) filtering to optimize the trajectory results. Combined with the railway experimental track data, the maximum difference in the east and north coordinate direction can be controlled within 7 cm, and the average elevation error is 2.39 cm. This paper applies a multi-sensor integrated mobile detection system to railway detection. It is of great significance to the healthy development of the intelligent railway system.

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]

Street tree inventory is an important part of urban forest inventory. Mobile laser scanning (MLS) technology has a strong data acquisition ability inside the canopy and the trunk and is suitable for parameter estimation at the tree level. The first and key step is to segment individual trees from the street MLS data, which is a challenging problem due to the semantic gap. This paper proposes an efficient and effective method for street tree segmentation from MLS data using deep learning-based image instance segmentation. First, the three-dimensional (3D) street point cloud captured by the MLS system is mapped to a two-dimensional (2D) RGB image. Next, pixelwise tree proposals are segmented from the street image by a trained deep learning-based image instance segmentation model. Then, the 2D segmentation mask is mapped back to the 3D street point cloud to generate pointwise tree proposals. Finally, the proposals are optimized to obtain the final results. To evaluate and verify algorithm performance, an MLS point cloud is collected from a 1481.8 m-long one-side urban street containing various objects. Three deep learning-based image instance segmentation algorithms, YOLACT, BlendMask, and YOLOv8, are carried out, and YOLOv8 achieves the best results in terms of both accuracy and speed. YOLOv8 has the highest segmentation accuracy, with IoU= 0.85:0.05:0.95, with an average segmentation time of 26 ms per image. In the experiments comparing the algorithms to the existing hierarchical segmentation and classification segmentation methods, the proposed method outperforms the other two methods in accuracy and is faster. The precision is 0.9988, the recall is 0.9986, the F 1 score is 0.9987, and the time per scanline is 4.05 ms. Moreover, the proposed method can be applied to MLS data with a broad range of resolutions by introducing image resizing. [ABSTRACT FROM AUTHOR]

• An effective urban scene laser point cloud precise registration method. • Explores the technical details of pole-like objects as registration primitives. • MLS&BLS registration provide detailed representation of urban environments. • Fusion of multi-source laser data contributes to the construction of smart cities. In consideration of the significant changes in urban environmental scenes and the impact of factors such as signal obstruction on positioning, the spatial positions of multi-temporal Mobile Laser Scanning (MLS) point clouds collected in the same area are inconsistent, which makes it challenging to provide a complete expression of the urban road environment. To address this issue, a precise registration algorithm based on pole-like targets is proposed. The core steps include registration primitive extraction: a cylindrical spatial neighborhood is established based on the distinctive key points, and precise extraction of pole-like geospatial point cloud data is performed using the Euclidean clustering method with the normal curvature constraint. Achieving optimal spatial transformation: a circle center fitting method with additional Mean Absolute Error (MAE) loss function constraints was developed based on the stable elemental unit which was utilized to accurately construct homonymous feature point pairs. Validation results from multiple real urban point cloud datasets collected from different experimental areas and platforms demonstrate that our method achieves superior registration performance, with mean residuals below 5 cm in both the planar and elevation directions. The proposed method exhibits strong universality and registration robustness for multi-temporal point cloud data of the homologous or cross-sources. [ABSTRACT FROM AUTHOR]

Optimization of road networks is a common concern worldwide, primarily for safety purposes. Because the extent of these networks is substantial, automation of their inventory is highly desirable. This paper concentrates on the road inventory process that is necessary for regular maintenance. The key part of our road marking detection and reconstruction is based on spanning tree usage. The spanning trees are obtained from alpha shapes of the detected road markings. The spanning trees application enables the reliable identification of the road markings and precise reconstruction of their contours even with noisy data. Our method processes the point cloud data obtained from LiDAR measurements, and provides a common vector layer with road lane polygons. Such a vector layer is stored in a common file format supported by the majority of geographical information systems, thus producing an output that can be conveniently used for decision-making based on the road inventory process.

Mobile laser scanning is being used more often in Estonia and abroad to obtain geospatial information. As the system is still new and being upgraded, different methods are being used to find out how accurately the mobile laser scanner can measure in different conditions. In this article the mobile laser scanner accuracy is being examined depending on surrounding environment and the importance of postprocessing. Mobile laser scanning elevation data accuracy obtained in difficult conditions is being assessed. Difficult conditions are considered locations where there are tall objects that interrupt the satellite signals’ trajectory to the receiver. To determine the elevation accuracy of mobile laser scanner data, coordinates with the raw broadcast and final ephemerides with the combination of the GNSS receivers’ data that were installed in ideal and not ideal environmental conditions were computed. As a result of the study, the error of the elevation data in the first polygon, situated in difficult conditions was 7 mm when the Kunda reference station data was used with raw broadcast and final ephemerides. Error in the second polygon was accordingly 17 mm and 19 mm. When calculations were conducted using raw broadcast ephemerides in a base station in an imperfect environment, the error in the first polygon was 103 mm and in the second 75 mm. When the precise ephemerides were added to base station data, the error in the first polygon was 6 mm and in the second 21 mm. From the study results, it could be concluded that the mobile laser scanning system measures within 2 cm accuracy even in a complicated environment.

Objectives: In recent years, the convergence of shield tunnel using the terrestrial laser scanning is more and more widely, but stations changed frequently have an extreme influence on monitoring efficien-cy. The position of endpoints will change with the tunnel deformation when the laser point clouds is applied to fit curve in order to calculate the coordinates of endpoints. Therefore, it is difficult to ensure that the same convergent endpoints are obtained at different times using fitting curve based on point clouds which have an influence on the precision of convergence.Methods: Aiming at the problem of low efficiency of sta-tion-based 3D laser scanning, this paper introduces an economical, efficient and reliable mobile laser scan-ning monitoring method. For the calculation of convergence value, this paper introduces a new method based on tunnel expanded image in detail. Firstly, the expanded images of tunnel were obtained by the cy-lindrical projection of point clouds, the expansion of standard cylinder and gray rasterization, interpolation and gray enhancement of image. Secondly, it can accurately locate the section of point clouds of each ring in shield tunnel through the binaryzation of image, connected analysis, automatic identification and manual correction of ring cracks. Then, the positions between segments of each ring were manually drawn in the expanded image and the orientations of the fixed endpoints were deduced. Finally, the coordinates of the convergent endpoints were calculated and the convergences were calculated by fitting the segments. Re- sults: The results show that the difference between the repetitive results of the convergence value using mo-bile laser scanning is less than 1.5 mm. Conclusions: The proposed calculation method can efficiently moni-tor the convergence of the single-circle shield tunnel with reliable accuracy. [ABSTRACT FROM AUTHOR]

Multipath effects and signal obstruction by buildings in urban canyons can lead to inaccurate GNSS measurements and therefore errors in the estimated trajectory of Mobile Laser Scanning (MLS) systems; consequently, derived point clouds are distorted and lose spatial consistency. We obtain decimetre-level trajectory accuracy making use of corresponding points between the MLS data and aerial images with accurate exterior orientations instead of using ground control points. The MLS trajectory is estimated based on observation equations resulting from these corresponding points, the original IMU observations, and soft constraints on the pitch and yaw rotations of the vehicle. We analyse the quality of the trajectory enhancement under several conditions where the experiments were designed to test the influence of the number and quality of corresponding points and to test different settings for a B-spline representation of the vehicle trajectory. The method was tested on two independently acquired MLS datasets in Rotterdam by enhancing the trajectories and evaluating them using checkpoints. The RMSE values of the original GNSS/IMU based Kalman filter results at the checkpoints were 0.26 m, 0.30 m, and 0.47 m for the X-, Y- and Z-coordinates in the first dataset and 1.10 m, 1.51 m, and 1.81 m in the second dataset. The latter dataset was recorded with a lower quality IMU in an area with taller buildings. After trajectory adjustment these RMSE values were reduced to 0.09 m, 0.11 m, and 0.16 m for the first dataset and 0.12 m, 0.14 m, and 0.18 m for the second dataset. The results confirmed that, if sufficient tie points between the point cloud and aerial imagery are available, the method supports geo-referencing of MLS point clouds in urban canyons with a near-decimetre accuracy. [ABSTRACT FROM AUTHOR]

Mobile laser scanning is being used more often in Estonia and abroad to obtain geospatial information. As the system is still new and being upgraded, different methods are being used to find out how accurately the mobile laser scanner can measure in different conditions. In this article the mobile laser scanner accuracy is being examined depending on surrounding environment and the importance of postprocessing. Mobile laser scanning elevation data accuracy obtained in difficult conditions is being assessed. Difficult conditions are considered locations where there are tall objects that interrupt the satellite signals' trajectory to the receiver. To determine the elevation accuracy of mobile laser scanner data, coordinates with the raw broadcast and final ephemerides with the combination of the GNSS receivers' data that were installed in ideal and not ideal environmental conditions were computed. As a result of the study, the error of the elevation data in the first polygon, situated in difficult conditions was 7 mm when the Kunda reference station data was used with raw broadcast and final ephemerides. Error in the second polygon was accordingly 17 mm and 19 mm. When calculations were conducted using raw broadcast ephemerides in a base station in an imperfect environment, the error in the first polygon was 103 mm and in the second 75 mm. When the precise ephemerides were added to base station data, the error in the first polygon was 6 mm and in the second 21 mm. From the study results, it could be concluded that the mobile laser scanning system measures within 2 cm accuracy even in a complicated environment. [ABSTRACT FROM AUTHOR]

The applicability of a handheld mobile laser scanner (MLS) in oil shale mine surveys and subsequent three-dimensional modelling of postextracted surfaces is assessed. Recommendations for optimizing the acquisition and processing of MLS data and visualization of the results are given. The resulting surface geometry accuracy is validated via terrestrial laser scanner (TLS) reference data. Typical discrepancies between TLS and MLS data points remain within 2 and 5 cm in horizontal and vertical directions, respectively. The area of pillars and volumes of the extracted material are estimated by data analysis. The results are compared with those of the conventional mining survey. The detected discrepancies evidence that the laser scanning results provide a realistic outcome due to the evenly and densely spaced points within the point cloud. The result discrepancies between the tested surveying technologies are small and fully satisfy contemporary accuracy requirements. However, the handheld mobile laser scanning appears to be the most suitable method for underground mining surveys. The survey results enable reduction of mining losses and improvementt of the design of mining geometry. [ABSTRACT FROM AUTHOR]

Detecting metro gauge is very important for the safe operation of the subway. In this study, we design a low-cost metro tunnel mobile scanning system (MDS-TJ-1), which integrates a laser profile scanner with an inertial measure unit and an odometer to provide positioning and attitude parameters of the trajectory. The Lagrange interpolation is used to accomplish the time unification of different sensors. A dynamic alignment scheme for profile scanner is proposed based on the designed plane reflector target with high reflectivity. The error accumulation of the odometer is corrected by recognising the tunnel longitudinal joints, and finish the multi-source data fusion. The horizontal ray method is developed to process the metro gauge inspection. The experiment results show that the alignment accuracy of scanner is within 8 mm, the inner coincidence of the point cloud is within 3 cm, and the average error of the gauge inspection is 7.8 mm. [ABSTRACT FROM AUTHOR]