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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]

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]

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]

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]

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.

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]

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.

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]

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]

Piñon-juniper (PJ) woodlands are a widespread dryland ecosystem in the US containing an immense but poorly-constrained amount of aboveground biomass (AGB). Found at the dry end of the climatic range within which trees can persist, PJ faces an uncertain future in a changing climate, giving unique importance to mapping its AGB, past, present, and future. Lidar remote sensing offers great potential towards that end with research in a range of tree-dominant ecosystems demonstrating a strong capacity for mapping AGB. However, studies applying lidar to the task of mapping AGB in PJ are few. Given the unique structural characteristics of PJ trees, which tend to be short in stature (

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]

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]

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]

The emergence of mobile laser scanning (MLS) systems that use simultaneous localization and mapping (SLAM) technology to map their environment opens up new opportunities for characterizing forest structure. The speed and accuracy of data acquisition makes them particularly adapted to operational inventories. MLS also shows great potential for estimating inventory attributes that are difficult to measure in the field, such as wood volume or crown dimensions. Hardwood species represent a significant challenge for wood volume estimation compared to softwoods because a substantial portion of the volume is included in the crown, making them more prone to allometric bias and more complex to model. This study assessed the potential of MLS data to estimate tree structural attributes in a temperate hardwood stand: height, crown dimensions, diameter at breast height (DBH), and merchantable wood volume. Merchantable wood volume estimates were evaluated to the third branching order using the quantitative structural modeling (QSM) approach. Destructive field measurements and terrestrial laser scanning (TLS) data of 26 hardwood trees were used as reference to quantify errors on wood volume and inventory attribute estimations from MLS data. Results reveal that SLAM-based MLS systems provided accurate estimates of tree height (RMSE = 0.42 m (1.78%), R2 = 0.93), crown projected area (RMSE = 3.23 m2 (5.75%), R2 = 0.99), crown volume (RMSE = 71.4 m3 (23.38%), R2 = 0.99), DBH (RMSE = 1.21 cm (3.07%), R2 = 0.99), and merchantable wood volume (RMSE = 0.39 m3 (18.57%), R2 = 0.95), when compared to TLS. They also estimated operational merchantable volume with good accuracy (RMSE = 0.42 m3 (21.82%), R2 = 0.94) compared to destructive measurements. Finally, the merchantable stem volume derived from MLS data was estimated with high accuracy compared to TLS (RMSE = 0.11 m3 (8.32%), R2 = 0.96) and regional stem taper models (RMSE = 0.16 m3 (14.7%), R2 = 0.93). We expect our results would provide a better understanding of the potential of SLAM-based MLS systems to support in-situ forest inventory. [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.

As innovative technologies emerge, extensive research has been undertaken to develop new structural health monitoring procedures. The current methods, involving on-site visual inspections, have proven to be costly, time-consuming, labor-intensive, and highly subjective for assessing the safety and integrity of civil infrastructures. Mobile and stationary LiDAR (Light Detection and Ranging) devices have significant potential for damage detection, as the scans provide detailed geometric information about the structures being evaluated. This paper reviews the recent developments for LiDAR-based structural health monitoring, in particular, for detecting cracks, deformation, defects, or changes to structures over time. In this regard, mobile laser scanning (MLS) and terrestrial laser scanning (TLS), specific to structural health monitoring, were reviewed for a wide range of civil infrastructure systems, including bridges, roads and pavements, tunnels and arch structures, post-disaster reconnaissance, historical and heritage structures, roofs, and retaining walls. Finally, the existing limitations and future research directions of LiDAR technology for structural health monitoring are discussed in detail. [ABSTRACT FROM AUTHOR]

This paper, by critically reviewing different years (from 2010 to 2020) of research activities performed with Mobile Laser Scanning system, aims to review existing systems and how they are exploited in multifaceted domains. To such extent, the work defines five field domains where Mobile Laser Scanning have been used: Built and urban environment, Cultural heritage and Archaeology, Underground environment, Environmental monitoring, Forestry and Agriculture. Besides, this paper sheds the light on the pros and cons for each domain field, providing useful guidelines for those researchers involved in three-dimensional data collection with innovative systems. To achieve these purposes, research papers, were analysed, mainly considering geosciences related journals. The comparison among them revealed that, despite the incredible potential of Mobile Mapping System, the human intervention is still mandatory, and post-processing actions are needed to achieve the desired results, regardless the domain field. Moreover, our study provides insight into the technical and methodological limitations that raise a general scepticism on Mobile Mapping System for three-dimensional surveying, highlighting that in most of cases supplementary data are required to make the final result trustworthy. Such obstacles, hampering Mobile Laser Scanning diffusion, point towards unexplored areas for further investigations, serving as useful guidelines for future research directions.

The development of devices capable of generating three-dimensional (3D) point clouds of the forest is flourishing in recent years. It is possible to generate relatively dense and accurate 3D data not only by terrestrial laser scanning but also mobile laser scanning, personal laser scanning (hand-held or in a backpack), photogrammetry, or even using smart devices with Time-of-Flight sensors. Each of the mentioned devices has their limits of usability, and different method to capture and generate 3D point clouds needs to be applied. Therefore, the objective of our experiment was to compare the performance of low-cost technologies capable of generating point clouds and their accuracy of tree detection and diameter at breast height estimation. We tested a multi-camera prototype (MultiCam) for terrestrial mobile photogrammetry constructed by authors. This device is capable of capturing images from four cameras simultaneously and with exact synchronization during mobile data acquisition. Secondly, we have designed and conducted a data collection with iPad Pro 2020 using the new built-in LiDAR sensor. Then we have used mobile scanning approach applied a hand-held personal laser scanning (PLShh) using GeoSlam Horizon scanner. Moreover, we have used terrestrial laser scanning (TLS) using FARO Focus s70. With all mentioned devices, we have focused on individual tree detection and diameter at breast height measurements by cylinder-based algorithm across eight test sites with dimensions 25x25 m. Altogether, 301 trees were located on test sites, and 268 were considered for the analysis and comparisons (DBH > 7 cm). TLS provided the most accurate and reliable data. Across all test sites, we achieved the highest accuracy (rRMSE ranged from 3.7% to 6.4%) and tree detection rate (90.6–100%). When we have considered only trees with DBH higher than 20 cm, the tree detection rate was 100% across all test sites (altogether 159 trees). When the threshold of trees considered in the evaluation was changed to 10 cm and then to 20 cm (from 7 cm), the accuracy (rRMSE) and tree detection rate increased for all devices significantly. Results achieved (DBH > 7 cm) by iPad Pro were closest to TLS results. The rRMSE ranged across test sites from 8.6% to 12.9% and tree detection 64.5% to 87.5%. PLShh and MultiCam, the rRMSE ranged from 13.1% to 24.9% and 14% to 38.2%, respectively. The tree detection rate ranged from 55.6% to 75% and 57.1% to 71.9%, respectively. The time needed to conduct data collection on a test site was fastest using MultiCam (approx. 8 min) and slowest using TLS (approx. 40 min).

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.

Forest inventory (FI) is the most challenging stage of forest management and planning process. Therefore, in situ surveys are often reinforced by modern remote sensing (RS) methods for collecting forestry-related data more efficiently. This study tests a stateof- the-art data collection method for practical use in the Turkish FI system for the first time. To this end, forest sampling plots were conventionally measured to collect dendrometric data from 437 trees in Artvin and Saçınka Forest Enterprises. Then, each plot was scanned using a handheld mobile laser scanning (HMLS) instrument. Finally, HMLS data were compared against ground measurements via basic FI measures. Based on statistical tests, no apparent differences were found between the two datasets at the plot level (P < 0.05). There were also robust correlations for diameter breast height at individual tree level (r > 0.97; P < 0.01). Residual analysis showed that both positive and negative errors had a homogeneous distribution, except for plot 8 where tree stems were in irregular shapes due to anthropogenic pressures. When all plots' data were aggregated, average values for the number of trees, basal area, and timber volume were estimated as 535 trees/ha-1, 49.6 m²/ha-1, and 499.7 m³/ha-1, respectively. Furthermore, secondary measures such as the number of saplings and slope were successfully retrieved using HMLS method. The highest overestimation was in timber volume with less than 10% difference at the landscape level. The differences were attributed to poor data quality of conventional measurements, as well as marginal site conditions in some plots. We concluded that the HMLS method met the accuracy standards for most FI measures, except for stand height. Thus, the Turkish FI system could benefit from this novel technology, which in turn supports the implementation of sound forest management and planning. [ABSTRACT FROM AUTHOR]

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

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. [ABSTRACT FROM AUTHOR]

Traffic signs are a key element in driver safety. Governments invest a great amount of resources in maintaining the traffic signs in good condition, for which a correct inventory is necessary. This work presents a novel method for mapping traffic signs based on data acquired with MMS (Mobile Mapping System): images and point clouds. On the one hand, images are faster to process and artificial intelligence techniques, specifically Convolutional Neural Networks, are more optimized than in point clouds. On the other hand, point clouds allow a more exact positioning than the exclusive use of images. The false positive rate per image is only 0.004. First, traffic signs are detected in the images obtained by the 360° camera of the MMS through RetinaNet and they are classified by their corresponding InceptionV3 network. The signs are then positioned in the georeferenced point cloud by means of a projection according to the pinhole model from the images. Finally, duplicate geolocalized signs detected in multiple images are filtered. The method has been tested in two real case studies with 214 images, where 89.7% of the signals have been correctly detected, of which 92.5% have been correctly classified and 97.5% have been located with an error of less than 0.5 m. This sequence, which combines images to detection−classification, and point clouds to geo-referencing, in this order, optimizes processing time and allows this method to be included in a company’s production process. The method is conducted automatically and takes advantage of the strengths of each data type.

Urban trees are an important component of our environment and ecosystem. Trees are able to combat climate change, clean the air and cool the streets and city. Tree inventory and monitoring are of great interest for biomass estimation and change monitoring. Conventionally, parameters of trees are manually measured and documented in situ, which is not efficient regarding labour and costs. Light Detection And Ranging (LiDAR) has become a well-established surveying technique for the acquisition of geo-spatial information. Combined with automatic point cloud processing techniques, this in principle enables the efficient extraction of geometric tree parameters. In recent years, studies have investigated to what extend it is possible to perform tree inventories using laser scanning point clouds. Give the availability of a city of Delft Open data tree repository, we are now able to present, validate and extend a workflow to automatically obtain tree data from tree location until tree species. The results of a test over 47 trees show that the proposed methods in the workflow are able to individual urban trees. The tree species classification results based on the extracted tree parameters show that only one tree was wrongly classified using k-means clustering. [ABSTRACT FROM AUTHOR]

Pole-like road furniture detection received much attention due to its traffic functionality in recent years. In this paper, we develop a framework to detect pole-like road furniture from sparse mobile laser scanning data. The framework is carried out in four steps. The unorganised point cloud is first partitioned. Then above ground points are clustered and roughly classified after removing ground points. A slicing check in combination with cylinder masking is proposed to extract pole-like road furniture candidates. Pole-like road furniture are obtained after occlusion analysis in the last stage. The average completeness and correctness of pole-like road furniture in sparse and unevenly distributed mobile laser scanning data was above 0.83. It is comparable to the state of art in the field of pole-like road furniture detection in mobile laser scanning data of good quality and is potentially of practical use in the processing of point clouds collected by autonomous driving platforms. [ABSTRACT FROM AUTHOR]

We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our main goal is to produce a reconstruction of a scene that is adapted to the local geometry of objects . Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each possible connexion between adjacent points and filter them by searching collinear structures in the scene, or structures perpendicular to the laser beams. Next, we create triangles for each triplet of self-connected edges. Last, we improve this method with a regularization based on the co-planarity of triangles and collinearity of remaining edges. We compare our results to a naive simplicial complexes reconstruction based on edge length. [ABSTRACT FROM AUTHOR]

The article describes the method of calibration of the Riegl VMZ-400 mobile scanning system on a floating platform and an experiment aimed at conducting measurements from a previously calibrated instrument. The issue of factors affecting the quality of measurements was discussed. Mobile laser scanning is an increasingly popular measurement solution, but it is still innovative on a European scale. The use of a floating platform is unique on a national scale. The presented solution is the result of the work of the Gdańsk University of Technology team on the development of the measurement procedure for scanning land from the sea and the river. [ABSTRACT FROM AUTHOR]

Classifying the original point clouds into ground and non-ground points is a key step in LiDAR (light detection and ranging) data post-processing. Cloth simulation filtering (CSF) algorithm, which based on a physical process, has been validated to be an accurate, automatic and easy-to-use algorithm for airborne LiDAR point cloud. As a new technique of three-dimensional data collection, the mobile laser scanning (MLS) has been gradually applied in various fields, such as reconstruction of digital terrain models (DTM), 3D building modeling and forest inventory and management. Compared with airborne LiDAR point cloud, there are some different features (such as point density feature, distribution feature and complexity feature) for mobile LiDAR point cloud. Some filtering algorithms for airborne LiDAR data were directly used in mobile LiDAR point cloud, but it did not give satisfactory results. In this paper, we explore the ability of the CSF algorithm for mobile LiDAR point cloud. Three samples with different shape of the terrain are selected to test the performance of this algorithm, which respectively yields total errors of 0.44%, 0.77% and1.20%. Additionally, large area dataset is also tested to further validate the effectiveness of this algorithm, and results show that it can quickly and accurately separate point clouds into ground and non-ground points. In summary, this algorithm is efficient and reliable for mobile LiDAR point cloud. [ABSTRACT FROM AUTHOR]

During the past decade in forest mapping and monitoring applications, the ability to acquire spatially accurate, 3D remote-sensing information by means of laser scanning, digital stereo imagery and radar imagery has been a major turning point. These 3D data sets that use single- or multi-temporal point clouds enable a wide range of applications when combined with other geoinformation and logging machine-measured data. New technologies enable precision forestry, which can be defined as a method to accurately determine characteristics of forests and treatments at stand, sub-stand or individual tree level. In precision forestry, even individual tree-level assessments can be used for simulation and optimization models of the forest management decision support system. At the moment, the forest industry in Finland is looking forward to next generation’s forest inventory techniques to improve the current wood procurement practices. Our vision is that in the future, the data solution for detailed forest management and wood procurement will be to use multi-source and -sensor information. In this communication, we review our recent findings and describe our future vision in precision forestry research in Finland.

The stem diameter distribution, stem form and quality information must be measured as accurately as possible to optimize cutting. For a detailed measurement of the stands, we developed and demonstrated the use of a multisource single-tree inventory (MS-STI). The two major bottlenecks in the current airborne laser scanning (ALS)-based single-tree-level inventory, tree detection and tree species recognition, are avoided in MS-STI. In addition to airborne 3D data, such as ALS, MS-STI requires an existing tree map with tree species information as the input information. In operational forest management, tree mapping would be carried out after or during the first thinning. It should be highlighted that the tree map is a challenging prerequisite, but that the recent development in mobile 2D and 3D laser scanning indicates that the solution is within reach. In our study, the tested input tree map was produced by terrestrial laser scanning (TLS) and by using a Global Navigation Satellite System. Predictors for tree quality attributes were extracted from ALS data or digital stereo imagery (DSI) and used in the nearest-neighbor estimation approach. Stem distribution was compiled by summing the predicted single-tree measures. The accuracy of the MS-STI was validated using harvester data (timber assortments) and field measures (stem diameter, tree height). RMSEs for tree height, diameter, saw log volume and pulpwood volume varied from 4.2% to 5.3%, from 10.9% to 19.9%, from 28.7% to 43.5% and from 125.1% to 134.3%, respectively. Stand-level saw log recoveries differed from −2.2% to 1.3% from the harvester measurements, as the respective differences in pulpwood recovery were between −3.0% and 10.6%. We conclude that MS-STI improves the predictions of stem-diameter distributions and provides accurate estimates for tree quality variables if an accurate tree map is available.

In the near future, the communication between autonomous cars will produce a network of sensors that will allow us to know the state of the roads in real time. Lidar technology, upon which most autonomous cars are based, allows the acquisition of 3D geometric information of the environment. The objective of this work is to use point clouds acquired by Mobile Laser Scanning (MLS) to segment the main elements of road environment (road surface, ditches, guardrails, fences, embankments, and borders) through the use of PointNet. Previously, the point cloud was automatically divided into sections in order for semantic segmentation to be scalable to different case studies, regardless of their shape or length. An overall accuracy of 92.5% has been obtained, but with large variations between classes. Elements with a greater number of points have been segmented more effectively than the other elements. In comparison with other point-by-point extraction and ANN-based classification techniques, the same success rates have been obtained for road surfaces and fences, and better results have been obtained for guardrails. Semantic segmentation with PointNet is suitable when segmenting the scene as a whole, however, if certain classes have more interest, there are other alternatives that do not need a high training cost.

Indoor mobile mapping systems are important for a wide range of applications starting from disaster management to straightforward indoor navigation. This paper presents the design and performance of a low-cost backpack indoor mobile mapping system (ITC-IMMS) that utilizes a combination of laser range-finders (LRFs) to fully recover the 3D building model based on a feature-based simultaneous localization and mapping (SLAM) algorithm. Specifically, we use robust planar features. These are advantageous, because oftentimes the final representation of the indoor environment is wanted in a planar form, and oftentimes the walls in an indoor environment physically have planar shapes. In order to understand the potential accuracy of our indoor models and to assess the system’s ability to capture the geometry of indoor environments, we develop novel evaluation techniques. In contrast to the state-of-the-art evaluation methods that rely on ground truth data, our evaluation methods can check the internal consistency of the reconstructed map in the absence of any ground truth data. Additionally, the external consistency can be verified with the often available as-planned state map of the building. The results demonstrate that our backpack system can capture the geometry of the test areas with angle errors typically below 1.5° and errors in wall thickness around 1 cm. An optimal configuration for the sensors is determined through a set of experiments that makes use of the developed evaluation techniques.

Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however, reviews of the many innovative data processing strategies described in the literature have not been conducted in sufficient depth. To this end, we review and summarize the state of the art for MLS data processing approaches, including feature extraction, segmentation, object recognition, and classification. In this review, we first discuss the impact of the scene type to the development of an MLS data processing method. Then, where appropriate, we describe relevant generalized algorithms for feature extraction and segmentation that are applicable to and implemented in many processing approaches. The methods for object recognition and point cloud classification are further reviewed including both the general concepts as well as technical details. In addition, available benchmark datasets for object recognition and classification are summarized. Further, the current limitations and challenges that a significant portion of point cloud processing techniques face are discussed. This review concludes with our future outlook of the trends and opportunities of MLS data processing algorithms and applications.

Accurate terrain models are a crucial component of studies of river channel evolution. In this paper we describe a new methodology for creating high-resolution seamless digital terrain models (DTM) of river channels and their floodplains. We combine mobile laser scanning and low-altitude unmanned aerial vehicle (UAV) photography-based methods for creating both a digital bathymetric model of the inundated river channel and a DTM of a point bar of a meandering sub-arctic river. We evaluate mobile laser scanning and UAV-based photogrammetry point clouds against terrestrial laser scanning and combine these data with an optical bathymetric model to create a seamless DTM of two different measurement periods. Using this multi-temporal seamless data, we calculate a DTM of difference that allows a change detection of the meander bend over a one-year period.

Rough roads influence the safety of the road users as accident rate increases with increasing unevenness of the road surface. Road roughness regions are required to be efficiently detected and located in order to ensure their maintenance. Mobile Laser Scanning (MLS) systems provide a rapid and cost-effective alternative by providing accurate and dense point cloud data along route corridor. In this paper, an automated algorithm is presented for detecting road roughness from MLS data. The presented algorithm is based on interpolating smooth intensity raster surface from LiDAR point cloud data using point thinning process. The interpolated surface is further processed using morphological and multi-level Otsu thresholding operations to identify candidate road roughness regions. The candidate regions are finally filtered based on spatial density and standard deviation of elevation criteria to detect the roughness along the road surface. The test results of road roughness detection algorithm on two road sections are presented. The developed approach can be used to provide comprehensive information to road authorities in order to schedule maintenance and ensure maximum safety conditions for road users. [ABSTRACT FROM AUTHOR]

The growing number of LiDAR scanning solutions offer an alternative to classical methods of measurement that are often used underground, the results obtained being comparable from an accuracy point of view but when work duration and degree of detail are taken into account, laser scanning is increasingly superior. Compared with other methods of remote sensing, the methods of laser scanning can be easier to use in order to perform subterranean measurements due to the fact that the fulfilment of lighting and visibility conditions is no longer necessary, only site access for crew and equipment being a prerequisite for mission execution. [ABSTRACT FROM AUTHOR]

• A piecewise distance effect function and the Blinn-Phong model-based angle effect correction is proposed to correct the raw intensity for road marking detection. • The background data-driven angle effect function (BDAF) is beneficial for enhancing marking and road classification. • The incidence angle span (IAS) has a major influence on intensity correction of road marking, the road marking separability was improved by 35%@535 pt/m2 when IAS was increased from 3.17° to 13.5°. The intensity data recorded by light detection and ranging (LiDAR) contains the spectral characteristics of a scanned target, making it an essential source of information in mobile laser scanning (MLS) related applications. However, the raw intensity can be distorted by distance and incidence angle, obstructing its use in autonomous driving. To correct the intensity distortion, an automatic demarcation selection, based on distance effect function, and a Blinn-Phong model-based angle effect correction, have been proposed to correct the raw intensity, establishing the relationship between intensity correction performance and the influencing factors, including modeling data selection, incidence angle span (IAS) and point cloud density. Separability (SEP) and balanced F score (BFS) were used to evaluate the separability, accuracy and totality of road marking detection. Experimental results showed that the background data-driven angle effect function (BDAF) used was beneficial for enhancing marking and road classification. IAS has a major influence on correcting intensity performance. SEP was improved by 35% when IAS was increased from 3.17° to 13.5°, with point density of 535 pt/m2. The IAS threshold could be adjusted according to the instrument performance used. It offers a meaningful investigation of detection and classification of planar target using corrected MLS intensity. [ABSTRACT FROM AUTHOR]

• The total leaf area (TLA) of the canopy was evaluated for variable-rate spraying. • The total grid area (TGA) of the canopy was measured by the first and second echoes of a 2D LiDAR. • The TGA-based TLA evaluation method can adapt to varying point density. In recent years, variable-rate spraying technology has been proposed to solve the pesticide waste and environmental pollution problems caused by traditional spraying methods. Variable-rate spraying methods use sensors to measure the leaf amounts in the target canopies, which are used to adjust the amount of liquid and ensure even spraying for canopies of different sizes and densities. One of these methods uses mobile laser scanning (MLS) technologies with a two-dimensional (2D) light detection and ranging (LiDAR) sensor to capture the point cloud of the canopy, and then the total point number (TPN) is used to evaluate the total leaf area (TLA) of the canopy as a basis for variable-rate spraying. However, the moving speed and ranging distance of LiDAR often vary in the process of spraying, resulting in an inconsistent TPN and a large influence on the accuracy of the TLA measurement. In this paper, a new method for estimating the TLA of an individual tree canopy is proposed based on the total grid area (TGA) measured from the first and second echoes of a LiDAR sensor. The grid area at a single point is calculated by multiplying the LiDAR moving resolution and scanning resolution. To evaluate the proposed method, we use an artificial tree, the branches of which are pruned to generate nine canopies with different densities. Both sides of the canopies are scanned at three distances, namely, 1.0 m, 1.5 m, and 2.0 m, and at a constant moving speed of 0.104 m/s. Then, the point cloud of each canopy is sampled multiple times to simulate a variety of moving speeds ranging from 1.040 m/s to 2.080 m/s. Finally, a total of 594 test canopies are obtained. Univariate polynomial regression is adopted to evaluate the TLA by using the TPN and TGA. When the polynomial order is set to 2, the coefficient of determination (R-squared) of the TPN evaluation is 0.5759 with a 4662.4542 cm2 root mean squared error (RMSE), and the R-squared of the TGA evaluation is 0.9617 with a 1401.1652 cm2 RMSE. The experimental results show that the proposed method can effectively eliminate the effects of different LiDAR moving speeds and ranging distances during TLA measurements. [ABSTRACT FROM AUTHOR]

The calibration and accuracy analysis of a novel, low-cost, adaptable mobile laser scanning (MLS) system using a Velodyne HDL-32E laser scanner and an Oxford Technical Solutions Inertial+2 inertial navigation system, is described. First, a static calibration of the laser scanner is discussed. The static calibration is shown to improve the overall relative accuracy of point cloud data from the scanner by approximately 20% over the manufacturer-supplied calibration. Then, the determination of system boresight angles and lever-arm offsets using a planar patch least-squares approach is presented. Finally, the calibrated and boresighted MLS is operated in a backpack mode to acquire multiple data sets in an area that contains dense ground control acquired using static terrestrial laser scanning (TLS) and a high-end, survey-grade MLS. The dense ground control is used to examine several methods of estimating the overall errors of the backpack MLS system. Detailed comparison of the MLS data with the TLS and survey-grade MLS control shows that, despite the system's low cost, it is able to reliably collect point cloud data with greater than 10-cm three-dimensional root-mean-square error accuracy. [ABSTRACT FROM AUTHOR]