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The spread of new survey strategies for the documentation and 3D reconstruction of complex cultural heritage sites enables the implementation of virtual web navigation systems that are useful for their virtual fruition. In particular, remote indoor/outdoor exploration enhances our knowledge of cultural heritage sites, even in inaccessible or difficult-to-visit states. However, the 3D data acquisition of complex sites for documentation remains a challenge, and the 3D virtual exploration of these datasets is often limited to property software implementations. This work describes the 3D documentation and construction of an indoor/outdoor web visualization system based on the WebGL open-source technology of a complex cultural heritage site. The case study regards the complex of "Santa Maria della Grotta" in Marsala (Italy), which is composed of a church that is located mostly underground and is connected to a human-dug hypogea on the site of a Punic necropolis. The aim of the work was to obtain detailed 3D documentation of the indoor and outdoor spaces through the integration of mobile laser scanning and aerial photogrammetry survey, and to develop a virtual web navigation system for the remote exploration of the site. The indoor/outdoor web navigation system provides users with a simple, web-browser-based 3D visualization, enabling the dissemination of the monuments' knowledge on the web through an economically sustainable solution based on open-source technologies. [ABSTRACT FROM AUTHOR]

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]

Automatic detection of railroad infrastructure using Mobile Laser Scanning systems is a key technology for both advanced rail driver assistance and intelligent track maintenance. The recent research into railway facility extraction or condition monitoring usually relies on high-density point cloud dataset with known sensor parameters but ignores the processing performance in actual deployment and has high requirements for the acquisition device. To address these limitations, a novel rail extraction algorithm is proposed for processing Mobile Laser Scanning data in real time during acquisition, which could extract rail features by a hierarchical coarse-to-fine method with basic structural parameters. Using the geometric and global statistical characteristics of rail in raw data, a new rail descriptor is defined based on the quantitative statistics of points satisfying height difference in generalized local neighborhood. The approach is evaluated experimentally by a simulated real-time acquisition data and compared with a reference method. The experimental results show that the proposed algorithm has a finer extraction effect and good real-time performance. [ABSTRACT FROM AUTHOR]

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe forest 3D structures at high resolution. To confirm the consistency in the retrieval of forest structural parameters using hand-held laser scanning (HLS), before operationalizing the method, confirming the data is crucial. We analyzed the performance of tree-level mapping based on HLS under different phenology conditions on a mixed forest in western Spain comprising Pinus pinaster and two deciduous species, Alnus glutinosa and Quercus pyrenaica. The area was surveyed twice during the growing season (July 2022) and once in the deciduous season (February 2022) using several scanning paths. Ground reference data (418 trees, 15 snags) was used to calibrate the HLS data and to assess the influence of phenology when converting 3D data into tree-level attributes (DBH, height and volume). The HLS-based workflow was robust at isolating tree positions and recognizing stems despite changes in phenology. Ninety-six percent of all pairs matched below 65 cm. For DBH, phenology barely altered estimates. We observed a strong agreement when comparing HLS-based tree height distributions. The values exceeded 2 m when comparing height measurements, confirming height data should be carefully used as reference in remote sensing-based inventories, especially for deciduous species. Tree volume was more precise for pines (r = 0.95, and relative RMSE = 21.3 –23.8%) compared to deciduous species (r = 0.91 –0.96, and relative RMSE = 27.3–30.5%). HLS data and the forest structural complexity tool performed remarkably, especially in tree positioning considering mixed forests and mixed phenology conditions. [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]

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]

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 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 aim of this study was to determine whether the iPad Pro 12th generation LiDAR sensor is useful to measure tree diameter at breast height (DBH) in natural boreal forests. This is a follow-up to a previous study that was conducted in a research forest and identified the optimal method for (DBH) estimation as a circular scanning and fitting ellipses to 4 cm stem cross-sections at breast height. The iPad Pro LiDAR scanner was used to acquire point clouds for 15 sites representing a range of natural boreal forest conditions in Ontario, Canada, and estimate DBH. The secondary objective was to determine if tested stand (species composition, age, density, understory) or tree (species, DBH) factors affected the accuracy of estimated DBH. Overall, estimated DBH values were within 1 cm of actual DBH values for 78 of 133 measured trees (59%). An RMSE of 1.5 cm (8.6%) was achieved. Stand age had a large effect (>0.15) on the accuracy of estimated DBH values, while density, understory, and DBH had moderate effects (0.05–0.14). No trend was identified between accuracy and stand age. Accuracy improved as understory density decreased and as tree DBH increased. Inertial measurement unit (IMU) and positional accuracy errors with the iPad Pro scanner limit the feasibility of using this device for forest inventories. [ABSTRACT FROM AUTHOR]

Autonomous drive systems are a dynamically developed sector of the automotive industry. The key problem in such technological solutions is to provide a reliable navigation system, which is typically based on high-definition (HD) maps supporting the identification of the position of a maneuvering vehicle. HD maps should include possibly up-to-date and detailed information on traffic lanes and on the traffic rules and regulations on such lanes. An effective development of an HD map should be based on the geodetic measurement methods, which ensure efficient and accurate acquisition of spatial data. This article presents the results of an experiment consisting in the manipulation of data obtained with the use of the mobile laser scanning method and further in employing this data in the development of an HD map in an open-source environment. The applied measurement technology and the processing method allowed data of high resolution (frequently above 1000 points per m2) and of high accuracy (3D accuracy down to less than 5 cm). The obtained data were processed in the Vector Map Builder environment (which is accessible from the level of an internet browser) and the final product - HD map was created in the Lanelet2 open-source environment. The above-described experiments allowed two main conclusions. Most importantly, they demonstrate the importance of planning and performing in-field mobile laser scanning measurements. They also point to the important role of the human analyst who needs to manually vectorize the key elements of road infrastructure and to define traffic rules. [ABSTRACT FROM AUTHOR]

In this paper, we compare the positioning accuracy of commercial, mobile laser scanning systems operating under a forest canopy. The accuracy was evaluated on a 800-m-long positioning track, using tree locations from both a traditional field reference, collected with total station, and a high-density airborne laser scanning (ALS) system as a reference. Tree locations were used since mobile lasers are studied for automation of field reference for forest inventory and location of individual trees with high accuracy is required. We also developed a novel method for evaluating the ground level around the trees, as it not only affects the z-coordinate, but the horizontal position as well if the tree is tilted.In addition to the accuracy that could only be evaluated for systems equipped with a GNSS receiver, we evaluate the consistency of laser scanning systems by registering the tree locations extracted from the mobile systems to both the field reference and ALS. We demonstrated that the high-density ALS has similar accuracy (RMSE of approximately 6 cm) and precision as the total station field reference, while being much faster to collect. Furthermore, the completeness of the high-density ALS was over 80 %, which is more than enough to register the other methods to it in a robust manner, providing a global position for laser scanners without an inherit way of georeferencing themselves, such as a GNSS receiver.The positioning of all the mobile systems were based on the Simultaneous Localization and Mapping (SLAM) algorithm integrated with an inertial measurement unit (IMU), and they showed a similar precision; planar positioning error of less than 15 cm and vertical error of 10–30 cm. However, the accuracy of the only commercial system in this test whose positioning methods included a GNSS receiver, was order of several meters, indicating a demand for better methods for GNSS-based global positioning inside a dense forest canopy.

Real-time semantic segmentation of point clouds has increasing importance in applications related to 3D city modelling and mapping, automated inventory of forests, autonomous driving and mobile robotics. Current state-of-the-art point cloud semantic segmentation methods rely heavily on the availability of 3D laser scanning data. This is problematic in regards of low-latency, real-time applications that use data from high-precision mobile laser scanners, as those are typically 2D line scanning devices. In this study, we experiment with real-time semantic segmentation of high-density multispectral point clouds collected from 2D line scanners in urban environments using encoder - decoder convolutional neural network architectures. We introduce a rasterized multi-scan input format that can be constructed exclusively from the raw (non-georeferenced profiles) 2D laser scanner measurement stream without odometry information. In addition, we investigate the impact of multispectral data on the segmentation accuracy. The dataset used for training, validation and testing was collected with multispectral FGI AkhkaR4-DW backpack laser scanning system operating at the wavelengths of 905 nm and 1550 nm, and consists in total of 228 million points (39 583 scans). The data was divided into 13 classes that represent various targets in urban environments. The results show that the increased spatial context of the multi-scan format improves the segmentation performance on the single-wavelength lidar dataset from 45.4 mIoU (a single scan) to 62.1 mIoU (24 consecutive scans). In the multispectral point cloud experiments we achieved a 71 % and 28 % relative increase in the segmentation mIoU (43.5 mIoU) as compared to the purely single-wavelength reference experiments, in which we achieved 25.4 mIoU (905 nm) and 34.1 mIoU (1550 nm). Our findings show that it is possible to semantically segment 2D line scanner data with good results by combining consecutive scans without the need for odometry information. The results also serve as motivation for developing multispectral mobile laser scanning systems that can be used in challenging urban surveys.

The provision of ecosystem functions and services in forests is closely linked to the presence of complex structures. One such service is the ability to store carbon. It has recently become possible to quantify both structural complexity and biomass of forests (as proxy of carbon storage) using light detection and ranging (LiDAR). The objective of this study was to analyze how the community-level complexity of a forest stand relates to structural characteristics, and biomass in particular, of the trees comprising the stand. To do so, we virtually assembled 30 forests (3D models), all representing different versions of a beech-pine forest in Germany, based on real world 3D LiDAR scan data of all trees in the forest. At the individual tree level, various structural characteristics, including wood volume and biomass were derived using both voxel models and quantitative structure models (QSM). Basal area and biomass, as well as to a lower degree also the mean height of maximum crown projection area, significantly affected the structural complexity at stand level. Among the different forest models, the variation in complexity could best be described using a combination of basal area, mean height of the maximum crown projection area, and the coefficient of variation of total tree height. Biomass alone explained 54% of the variation in stand-level complexity, while the multivariate model based on measures addressing the amount and vertical distribution of plant material explained 86% of the variability in complexity. Using a laser-based and holistic approach of assessing the structural complexity, namely the box-dimension, allowed identifying key structural attributes that promote aboveground structural complexity of the forest studied here.

The health and stability of trees are essential information for the safety of people and property in urban greenery, parks or along roads. The stability of the trees is linked to root stability but essentially also to trunk decay. Currently used internal tree stem decay assessment methods, such as tomography and penetrometry, are reliable but usually time-consuming and unsuitable for large-scale surveys. Therefore, a new method based on close-range remotely sensed data, specifically close-range photogrammetry and iPhone LiDAR, was tested to detect decayed standing tree trunks automatically. The proposed study used the PointNet deep learning algorithm for 3D data classification. It was verified in three different datasets consisting of pure coniferous trees, pure deciduous trees, and mixed data to eliminate the influence of the detectable symptoms for each group and species itself. The mean achieved validation accuracies of the models were 65.5% for Coniferous trees, 58.4% for Deciduous trees and 57.7% for Mixed data classification. The accuracies indicate promising data, which can be either used by practitioners for preliminary surveys or for other researchers to acquire more input data and create more robust classification models. [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.

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.

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.

This contribution focuses on a comparison of modern geomatics technologies for the derivation of growth parameters in forest management. The present text summarizes the results of our measurements over the last five years. As a case project, a mountain spruce forest with planned forest logging was selected. In this locality, terrestrial laser scanning (TLS) and terrestrial and drone close-range photogrammetry were experimentally used, as was the use of PLS mobile technology (personal laser scanning) and ALS (aerial laser scanning). Results from the data joining, usability, and economics of all technologies for forest management and ecology were discussed. ALS is expensive for small areas and the results were not suitable for a detailed parameter derivation. The RPAS (remotely piloted aircraft systems, known as "drones") method of data acquisition combines the benefits of close-range and aerial photogrammetry. If the approximate height and number of the trees are known, one can approximately calculate the extracted cubage of wood mass before forest logging. The use of conventional terrestrial close-range photogrammetry and TLS proved to be inappropriate and practically unusable in our case, and also in standard forestry practice after consultation with forestry workers. On the other hand, the use of PLS is very simple and allows you to quickly define ordered parameters and further calculate, for example, the cubic volume of wood stockpiles. The results from our research into forestry show that drones can be used to estimate quantities (wood cubature) and inspect the health status of spruce forests, However, PLS seems, nowadays, to be the best solution in forest management for deriving forest parameters. Our results are mainly oriented to practice and in no way diminish the general research in this area. [ABSTRACT FROM AUTHOR]

Background: Accurate, timely and easily obtainable information on stand fuel is of great importance in the prediction of fire behaviour. Aims: The objective of this study is to measure several stand fuel characteristics with handheld mobile laser scanning (HMLS) in six fuel types for Mediterranean region, and compare the results with traditional field fuel measurements (FFM) in 35 different sampling plots. Methods: The measurements involved overstorey (the number of trees, diameter at breast height, crown base height, tree height, maximum tree height, stand crown closure) and understorey (understorey closure, understorey height) fuel characteristics, and ground slope. Correlation analysis and t -test were performed to examine the relationship between FFM and HMLS datasets. In addition, cross-validation statistics (RMSE, r RMSE and R 2) were employed to evaluate the accuracy of the HMLS method. Key results: The results indicated strong correlations among all fuel characteristics. However, overstorey fuel characteristics were more favourable (r -values between 0.804 and 0.996, P < 0.01) than understorey (r -values between 0.483 and 0.612, P < 0.01). There was no significant difference between FFM and HMLS datasets in all fuel characteristics (P > 0.05). Conclusions: The results indicated that the HMLS was practical, cost-effective, time-efficient and required less labour as compared to traditional FFM in plot-level (i.e. 0.1 ha) inventories. Accurate, timely and easily obtainable information on stand fuel characteristics plays an important role in fire behaviour prediction. In this study, stand fuel characteristics were measured using handheld mobile laser scanning in six fuel types. The results were compared to those obtained through field fuel measurements from the same plots. [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. [ABSTRACT FROM AUTHOR]

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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.

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.

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.

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.

The aim of this study was to determine whether the iPad Pro 12th generation LiDAR sensor is useful to measure tree diameter at breast height (DBH) in natural boreal forests. This is a follow-up to a previous study that was conducted in a research forest and identified the optimal method for (DBH) estimation as a circular scanning and fitting ellipses to 4 cm stem cross-sections at breast height. The iPad Pro LiDAR scanner was used to acquire point clouds for 15 sites representing a range of natural boreal forest conditions in Ontario, Canada, and estimate DBH. The secondary objective was to determine if tested stand (species composition, age, density, understory) or tree (species, DBH) factors affected the accuracy of estimated DBH. Overall, estimated DBH values were within 1 cm of actual DBH values for 78 of 133 measured trees (59%). An RMSE of 1.5 cm (8.6%) was achieved. Stand age had a large effect (>0.15) on the accuracy of estimated DBH values, while density, understory, and DBH had moderate effects (0.05–0.14). No trend was identified between accuracy and stand age. Accuracy improved as understory density decreased and as tree DBH increased. Inertial measurement unit (IMU) and positional accuracy errors with the iPad Pro scanner limit the feasibility of using this device for forest inventories.

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.

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.

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]

Convergence and rockmass failure are significant hazards to personnel and physical assets in underground tunnels, caverns, and mines. Mobile Laser Scanning Systems (MLS) can deliver large volumes of point cloud data at a high frequency and on a large scale. However, current change detection approaches do not deliver sufficient sensitivity and precision for real-time performance on large-scale datasets. We present a novel, octree-based computational framework for intra-voxel statistical inference change detection and deformation analysis. Our approach exploits high-density MLS data to test for statistical significance for appearing objects caused by rockfall and for low-magnitude deformations, such as convergence. In field tests, our method detects rock falls with side lengths as small as 0.03 m and convergence as low as 0.01 m, or 0.5% wall-to-wall strain. When compared against a state-of-the-art multi-scale model-to-model cloud comparison (M3C2)-based method, ours is less sensitive to noisy data and parameter selection while also requiring fewer parameters. Most notably, our method is the only one tested that can perform real-time change detection on large-scale datasets on a single processor thread. Our method achieves a computational improvement of 50 times over single-threaded M3C2 while maintaining a performance scalability that is four times greater with dataset size. Our framework shows significant potential to enable accurate real-time geotechnical monitoring of large-scale underground spaces. [ABSTRACT FROM AUTHOR]

Measuring tree structure using three-dimensional (3D) mapping tools such as light detection and ranging (LiDAR) remote sensing is needed to provide well-managed and designed green spaces. The metrics used to estimate tree structure could be different depending on which LiDAR systems are used. This may lead to confusion and reduce confidence when evaluating tree structures and their derived products, such as plant area index (PAI). Therefore, studies that can determine similarities among measurements derived from different LiDAR systems are needed. In this study, structural canopy metrics in airborne laser scanning (ALS), terrestrial laser scanning (TLS), and mobile laser scanning (MLS) were compared to seek consistencies among the three LiDAR systems. The specific objectives were to test whether the estimates made by the metrics differed depending on single or clustered trees and to test whether LiDAR-derived errors in the metrics are related to tree structures. Tree point clouds were manually classified into single and clustered trees. Heights-related metrics, Rumple Index, area, and PAI were calculated for comparison analysis. Root-mean-square error (RMSE), bias, and Pearson’s correlation coefficient (r) were calculated to evaluate the consistencies in each metric among the LiDAR systems. The results showed that the maximum height of the point clouds (ZMAX) and max and mean heights derived from the canopy height models (minCHM and maxCHM) demonstrated good consistency (RMSE% < 10%, Bias% < 10%, and r > 0.900). Moreover, the biases from the ZMAX- and CHM-derived metrics comparisons among the LiDAR systems did not show strong linear relationships with the tree heights and canopy complexities (i.e. Pearson’s correlation coefficient r < |0.29|). On the contrary, the 95th percentile (Zq95) height and mean z height (ZMEAN) differed depending on the tree classes and showed significant linear relations with canopy heights and complexity. The configurations of LiDAR systems, such as point density and sensing locations, seem to affect the Zq95, ZMEAN metrics, and PAI. Our results suggest that assessing for consistencies among the different LiDAR systems is required before using multiple LiDAR systems interchangeably to estimate the structure of urban park areas.

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]