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Mobile Laser Scanning (MLS) system can provide high-density and accurate 3D point clouds that enable rapid pavement crack detection for road maintenance tasks. Supervised learning-based algorithms have been proved pretty effective for handling such a large amount of inhomogeneous and unstructured point clouds. However, these algorithms often rely on a lot of annotated data, which is labor-intensive and time-consuming. This paper presents a semi-supervised point-level approach to overcome this challenge. We propose a graph-widen module to construct a reasonable graph structure for point clouds, increasing the detection performance of graph convolutional networks (GCN). The constructed graph characterizes the local features from a small amount of annotated data, avoiding information loss and dramatically reduces the dependence on annotated data. The MLS point clouds acquired by a commercial RIEGL VMX-450 system are used in this study. The experimental results demonstrate that our method outperforms the state-of-the-art point-level methods in terms of recall, F1 score, and efficiency while achieving comparable accuracy.

The variation in point cloud density is driven by many different factors. This variation is expected to affect the quality of the information extracted from the point clouds, however, the extent to which these variations impact the ability to accurately extract and assess geometric features of highways from point cloud data are unknown. This paper investigates the impacts of point density reduction on the extraction and assessment of four critical geometric features. The density of light detection and ranging (LiDAR) data was first reduced and the different features were extracted at varying levels of point density and on a selection of different highway segments in Alberta, Canada. The information obtained at lower point density was then compared to what was obtained at 100% point density. It was found that clearance assessments and sight distance assessments had low sensitivity to reductions in point density (i.e., reducing the point density to as low as 10% of the original data (30 ppm2 on the pavement surface) yielded results comparable to what was obtained at 100% density (300 ppm2) In contrast, for cross section slope estimation and curve attribute estimation higher sensitivity to point density was observed. These findings are critical for transportation agencies considering the adoption of LiDAR technology to manage elements of their infrastructure and for researchers developing data processing tools and algorithms for the semantic segmentation of transportation features from remotely sensed point clouds.

Providing sufficient Available Sight Distance (ASD) that meets the minimum design requirements is crucial for highway safety. Previous work on sight distance assessment focused on Stopping Sight Distance (SSD) with little attention given to Passing Sight Distance (PSD). Insufficient PSD could lead to severe collisions such as head-on and sideswipe crashes. To address this gap, this paper introduces an automated method for PSD assessment on two-lane highways using mobile Light Detection and Ranging (LiDAR) data. The procedure involved extracting centerline lane marking, defining passing-allowed and passing-prohibited regions, computing the ASD, and comparing the existing centerline marking pattern (i.e., passing and no-passing zones) to a proposed lane marking that is based on the ASD for passing maneuvers. Regions that meet the design standards, substandard zones, and non-optimal design regions were all defined. A reallocation of PSD zones was conducted based on the ASD including modifying the existing lane marking pattern, which resulted in increasing the total length of passing zones by up to 20%, providing more, but safer, passing opportunities. A high-level safety assessment of historical collisions showed clusters of crashes along regions where passing is currently allowed at locations where the ASD is less than standard requirements. The proposed framework represents a tool by which transportation agencies could assess PSD, upgrade the design of existing highways, and investigate the consequences of PSD limitations to ensure compliance with standards during highway service life.

The value of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), which is the predominant commercial species in the Pacific Northwest, depends on tree verticality; trees with the same dimensions can differ substantially in value due to lean. The objective of this study was to assess the impact of tree leaning on estimation of stem dimensions using high-density terrestrial mobile lidar point clouds. We estimated lean with two metrics: the horizontal distance between stem centers at 1.3 m and 18 m, and the mean of seven successive lean angles along the tree bole (at 1, 3, 5, 7, 10, 12, and 15 m). For modeling, we used four existing taper equations and three existing volume equations. For trees leaning >2°, we enhanced the existing volume models by including lean as a predictor. Because lean estimates depend on the distribution and number of points describing the stem, we found that including the distance from scanner to tree improved the computed volume. When diameter at breast height was replaced with diameter at heights between 7 and 10 m, the volume models for leaning trees improved significantly, whereas the vertical trees had favorable results with heights between 5 and 15 m. Our study suggests that lean magnitude improves the estimation of stem volume when lean is >2°.

The majority of weather-related crashes in the U.S. occur on wet pavement, potentially due to hydroplaning. However, hydroplaning potential is often not considered in network-level pavement conditi...

Cycling lane centerlines (CLC) play an important role in the evaluation of safety-related conditions and guiding systems for cyclists along road corridors. The unavailability of design files or undocumented changes in the road infrastructures after improvements has created great difficulty in delineating CLC on existing roads. In this study, mobile laser scanning (MLS) data are introduced into this domain and a four-step semi-automated framework is proposed for generating CLC on roads with roadside barriers (RB). First, MLS data are restructured into the aligned scan-pattern grid using the mapping trajectory data. Second, a rasterization-based clustering approach is applied to segment the off-ground objects from the reorganized MLS data. Third, the RB amongst the segmented objects are identified using a sequential application of the k-Means clustering method and the proposed unidirectional growing method. Finally, the moving average technique and natural cubic spline are applied to generate CLC from the critical positions alongside the identified RB. Testing on three road sections with different types of RB demonstrated that the developed framework can successfully generate CLC from MLS data in the presence of considerable noises. The results also show that the proposed procedure shows better accuracy performance on processing roads with wide RB than a road with narrow RB.

Visibility determination is a key requirement in a wide range of national and urban applications, such as national security, landscape management, and urban design. Mobile LiDAR point clouds can de...

Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %.

This paper proposes a framework for automatic classification of mobile laser scanner (MLS) point cloud using multi-faceted multi-object convolutional neural network (MMCN). The proposed method take...

Mobile LiDAR systems (MLS) are rapid and accurate technologies for acquiring three-dimensional (3D) point clouds that can be used to generate 3D models of road environments. Because manual extraction of desirable features such as road traffic signs, trees, and pavement markings from these point clouds is tedious and time-consuming, automatic information extraction of these objects is desirable. This paper proposes a novel automatic method to extract pavement lane markings (LMs) using point attributes associated with the MLS point cloud based on fuzzy inference. The proposed method begins with dividing the MLS point cloud into a number of small sections (e.g. tiles) along the route. After initial filtering of non-ground points, each section is vertically aligned. Next, a number of candidate LM areas are detected using a Hough Transform (HT) algorithm and considering a buffer area around each line. The points inside each area are divided into “probable-LM” and “non-LM” clusters. After extracting geometric and radiometric descriptors for the “probable-LM” clusters and analyzing them in a fuzzy inference system, true-LM clusters are eventually detected. Finally, the extracted points are enhanced and transformed back to their original position. The efficiency of the method was tested on two different point cloud datasets along 15.6 km and 9.5 km roadway corridors. Comparing the LMs extracted using the algorithm with the manually extracted LMs, 88% of the LM lines were successfully extracted in both datasets.

Correctly describing the deterioration patterns is of great importance for the conservation of built cultural heritage. Also, their rapid in situ quantification is an essential requisite when studying exposed stone objects, to define satisfactory restoration actions and to avoid greater damage due to the application of incorrect treatments. The research described in this paper targets developing a methodology to compute the extent of different deterioration patterns on a granitic cultural heritage object using as a case study an overpass bridge called Puente Nuevo in Ourense (NW Spain). The applied methodology consisted of the combination of the geometric and radiometric information collected from a non-invasive and in situ mobile LiDAR system (MLS) and micro destructive analytical techniques. Laser scanning points were used as a base to create orthophotos, being their intensity values the input for an unsupervised classification method to determine the areas occupied for those different deterioration patterns. In parallel, as part of this multi-analytical approach different conventional analytical techniques (X ray diffraction, Fourier transform infrared spectroscopy and optical microscopy) were applied to chemically and mineralogically characterize those deterioration patterns, namely, gypsum black crust, calcite encrustations and biological colonization (mosses). LiDAR technology allowed to determine three different clusters considering the reflectance of those surfaces: uncoated granite, calcite encrustations and black crusts and biological colonization patinas in a combined cluster due to their close reflectance spectra. The computed extent and the location of those deterioration patterns will allow to identify the most suitable cleaning treatment saving money and time.

The California Baseline Ozone Transport Study (CABOTS) was conducted in the late spring and summer of 2016 to investigate the influence of long-range transport and stratospheric intrusions on surface ozone (O3) concentrations in California with emphasis on the San Joaquin Valley (SJV), one of two extreme ozone non-attainment areas in the US. One of the major objectives of CABOTS was to characterize the vertical distribution of O3 and aerosols above the SJV to aid in the identification of elevated transport layers and assess their surface impacts. To this end, the NOAA Earth System Research Laboratory (ESRL) deployed the Tunable Optical Profiler for Aerosol and oZone (TOPAZ) mobile lidar to the Visalia Municipal Airport (36.315∘ N, 119.392∘ E) in the central SJV between 27 May and 7 August 2016. Here we compare the TOPAZ ozone retrievals with co-located in situ surface measurements and nearby regulatory monitors and also with airborne in situ measurements from the University of California at Davis–Scientific Aviation (SciAv) Mooney and NASA Alpha Jet Atmospheric eXperiment (AJAX) research aircraft. Our analysis shows that the lidar and aircraft measurements agree, on average to within 5 ppbv, the sum of their stated uncertainties of 3 and 2 ppbv, respectively.

Mobile wheel-based LiDAR systems are capable of collecting a dense, accurate point cloud efficiently. Such systems provide accurate geospatial information of a scene, and thus show great potential to serve as an alternative infrastructure monitoring tool. In this study, two different surveys of runway 10/28 at Purdue University Airport (KLAF) were conducted. The surveys were performed in April and July of 2018 before and after a partial runway pavement resurfacing project. The study had two objectives: (1) evaluate the positional accuracy of derived features, and (2) evaluate LiDAR’s ability to detect changes in longitudinal/transversal profiles. The former used 10 static features in the point cloud; the latter used longitudinal and transversal profiles over a 1 mile-long runway section. For the 10 static features (runway lights), the root mean square errors (RMSE) of the coordinate differences are 0.13 ft, 0.08 ft, and 0.09 ft along the x, y, and z-directions, respectively. Considering the range and positional accuracy of the LiDAR and implemented GNSS/INS units, respectively, these RMSE values are acceptable. The derived numerical results from the longitudinal and cross-sectional profiles showed close alignment of pavement elevation where there was no construction activity and a 0.3 ft increase in elevation for the pavement section that was resurfaced. This paper provides a mechanism for evaluating the spatial accuracy of mobile LiDAR data and demonstrates its potential for mapping airfield infrastructure. As the technology improves, it is anticipated that cost-effective solutions yielding sub-centimeter accuracy will be available in next 18 months. Such availability, together with the established repeatability from this study, will stimulate more research and practical applications implementing mobile LiDAR systems.

We test the accuracy and density of mobile lidar-based scanners and compare them with the performance of a static scanner. This is achieved by systematically scanning a rectangular target at a growing distance, collating the statistics of the scans and comparing them. This study shows that the accuracy of the scans outputted by the static scanner is about 20 times better at 5 meters than those produced by the mobile devices and this gap further increases along with the distance. The density, measured in points per second, drops along with the distance for all the tested devices, with mobile scanners outperforming their static counterparts.

Accurate and rapid detection and mapping of guardrails play an essential role in road maintenance, asset management, and safety audits. This paper proposes an automated approach for guardrail detection and mapping in rural highway environments from mobile laser scanning point clouds. First, the scanning vehicle trajectory is extracted, and a buffer zone is constructed to remove points far from the roadside. Then multiple geometric filters and statistical outlier removal are applied to find the candidate guardrail points. The verticality of each point is calculated to further refine the detection. Next, a density-based clustering algorithm is used to group the remaining points into clusters. Finally, the detected guardrails are plotted on Google Earth to create an inventory map. Experiments on three rural highways demonstrate that the proposed algorithm can detect and map guardrails automatically with high accuracy.

While mobile LiDAR sensors are increasingly used to scan in ecology and forestry applications, reconstruction and characterisation are typically carried out offline (to the best of our knowledge). Motivated by this, we present an online LiDAR system which can run on a handheld device to segment and track individual trees and identify them in a fixed coordinate system. Segments relating to each tree are accumulated over time, and tree models are completed as more scans are captured from different perspectives. Using this reconstruction we then fit a cylinder model to each tree trunk by solving a least-squares optimisation over the points to estimate the Diameter at Breast Height (DBH) of the trees. Experimental results demonstrate that our system can estimate DBH to within $\sim$7 cm accuracy for 90% of individual trees in a forest (Wytham Woods, Oxford), Comment: 7 Pages, 11 figures, Accepted for 10th European Conference on Mobile Robotics (ECMR 2021)

Accurately quantifying the concentration and transport flux of atmospheric fine particulate matter (PM2.5) is vital when attempting to thoroughly identify the pollution formation mechanism. In this study, the mobile lidar measurements in Beijing on heavily polluted days in December from 2015 to 2018 are presented. The lidar was mounted on a vehicle, which could perform measurements along designated routes. On the basis of mobile lidar measurements along closed circuits of the 6th Ring Road around Beijing, the spatial distribution and transport flux of PM2.5 in Beijing were determined with information of wind field. In the spatial distribution, both the concentration and transport of PM2.5 were revealed to be more significant in the southern section of Beijing. The regional transport layer at heights

Frequent surveys using modern remote sensing technology enable observations of subaerial beach morphology and surface sediment type (sand vs. gravel/cobble) with unprecedented spatial and temporal resolution. Here we show weekly terrestrial mobile light detection and ranging (LiDAR) observations of cusps on a mixed sediment beach, consisting of sand and gravel/cobble, spanning ∼2 km alongshore. Surface gravel/cobble coverage varied from almost complete to none (e.g., all sand), over times ranging from weekly to seasonal, and with location. Cusp morphologies also varied in space and time. Cusps with 11–20 m alongshore wavelengths (L) primarily developed in winter and were composite with gravel‐cobble horns and sandy bays. Cusps with L between 21 and 40 m developed throughout the year and were either composite or entirely sandy. Previously not reported, composite mega cusps (L between 200 and 400 m) occurred in winter. Composite cusps, with L between 11 and 40 m, were clustered in gravel/cobble‐rich mega cusp bays and absent on the sandier mega cusp horns.

The Oregon Department of Transportation (ODOT) has been using survey-grade mobile Light Detection and Ranging (LiDAR) technology to scan and create three-dimensional (3D) representations of...

Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With rapid developments of mobile laser scanning (MLS) systems, massive point clouds are available for scene understanding, but publicly accessible large-scale labeled datasets, which are essential for developing learning-based methods, are still limited. This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of point clouds and consists of about 78.3 million points with 8 labeled object classes. Baseline experiments for semantic segmentation were conducted and the results confirmed the capability of this dataset to train deep learning models effectively. Toronto-3D is released to encourage new research, and the labels will be improved and updated with feedback from the research community., Comment: Toronto-3D dataset can be downloaded at https://github.com/WeikaiTan/Toronto-3D

The recent fast development in computer vision and mobile sensor technology such as mobile LiDAR and RGB-D cameras is pushing the boundary of the technology to suit the need of real-life applicatio...

Mobile LiDAR Systems (MLSs) have recently been recognized as an effective way to extract road markings. Although existing studies have achieved good accuracy (about 90%) in road marking extraction, the majority of them are based on image processing methods; only a few researchers directly use laser points, especially for the recognition and assessment of road markings. This study introduces a three-step automated method for the extraction, recognition, and maintenance of road markings based on the intensity information from the MLS data: (1) an automated mechanism to filter the ground surface in laser data, (2) an adaptive block and multi-threshold method to detect road markings, (3) an automated method to achieve the classification, recognition, and assessment of road markings. Qualitative and quantitative analyses based on experimental datasets with eight types of road markings were used to evaluate the feasibility and robustness of the proposed method. Experimental results show that the average values of completeness (CPT), correctness (CRT), and F-measure of the road marking detection results are 94.35%, 98.35%, and 95.7% and the average values of CPT, CRT, and quality (QUA) of recognition results are 99.0%, 93.2%, and 92.3%, respectively, indicating that the proposed method is feasible and effective. The detection and recognition results were used to reconstruct, improve, and update the road features database; provide guidelines for road applications and maximum assistance for road maintenance; and deliver a valuable solution for maintenance and management of constructed facilities.

The digital mapping of road environment is an important task for road infrastructure inventory and urban planning. Automatic extraction and classification of pole-like objects can remarkably reduce mapping cost and enhance work efficiency. Therefore, this paper proposes a voxel-based method that automatically extracts and classifies three-dimensional (3-D) pole-like objects by analyzing the spatial characteristics of objects. First, a voxel-based shape recognition is conducted to generate a set of pole-like object candidates. Second, according to their isolation and vertical continuity, the pole-like objects are detected and individualized using the proposed circular model with an adaptive radius and the vertical region growing algorithm. Finally, several semantic rules, consisting of shape features and spatial topological relationships, are derived for further classifying the extracted pole-like objects into four categories (i.e., lamp posts, utility poles, tree trunks, and others). The proposed method was evaluated using three datasets from mobile LiDAR point cloud data. The experimental results demonstrate that the proposed method efficiently extracted the pole-like objects from the three datasets, with extraction rates of 85.3%, 94.1%, and 92.3%. Moreover, the proposed method can achieve robust classification results, especially for classifying tree trunks.

Kim, C.H.; Kim, H.W.; Park, C.H.; Kim, W.H.; Lee, M.H.; Choi, S.Y., and Do, J.D., 2018. Coastline change measurement using shipborne mobile LiDAR in Anmok Beach, Gangneung, Korea. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 601–605. Coconut Creek (Florida), ISSN 0749-0208. Coastal areas, used as human utilization areas like leisure space, medical care, ports, and power plants, etc., are regions that are continuously changing and interconnected with ocean and land. Regular monitoring of coastline changes is essential at key locations with such volatility. For effective monitoring coastal changes, KIOST (Korea Institute of Ocean Science & Technology) has constructed a shipborne mobile LiDAR system. The shipborne mobile LiDAR system, installed in a research vessel, comprised a terrestrial LiDAR (RIEGL LMS-420i), an IMU (MAGUS Inertial+), a RTKGNSS (L...

This study aims at building a robust method for detecting and classifying traffic signs from mobile LiDAR point clouds and digital images. First, this method detects traffic signs from mobile LiDAR point clouds with regard to a prior knowledge of road width, pole height, reflectance, geometrical structure, and traffic-sign size. Then, traffic-sign images are segmented by projecting the detected traffic-sign points onto the digital images. Afterward, the segmented traffic-sign images are normalized for automatic classification with a given image size. Finally, a traffic-sign classifier is proposed based on a supervised Gaussian–Bernoulli deep Boltzmann machine model. We evaluated the proposed method using datasets acquired by a RIEGL VMX-450 system. The traffic-sign detection accuracy of 86.8% was achieved; through parameter sensitivity analysis, the overall performance of traffic-sign classification achieved a recognition rate of 93.3%. The computational performance showed that our method provides a promising solution to traffic-sign detection and classification using mobile LiDAR point clouds and digital images.

Ensuring adequate pavement cross-slope on highways can improve driver safety by reducing the potential for ponding to occur or vehicles to hydroplane. Mobile laser scanning (MLS) systems provide a rapid, continuous, and cost-effective means of collecting accurate 3D coordinate data along a corridor in the form of a point cloud. This study provides an evaluation of MLS systems in terms of the accuracy and precision of collected cross-slope data and documentation of procedures needed to calibrate, collect, and process this data. Mobile light detection and ranging (LiDAR) data were collected by five different vendors on three roadway sections. The results indicate the difference between ground control adjusted and unadjusted LiDAR derived cross-slopes, and field surveying measurements less than 0.19% at a 95% confidence level. The unadjusted LiDAR data incorporated corrections from an integrated inertial measurement unit and high-accuracy real-time kinematic GPS, however it was not post-processed adjusted with ground control points. This level of accuracy meets suggested cross-slope accuracies for mobile measurements (±0.2%) and demonstrates that mobile LiDAR is a reliable method for cross-slope verification. Performing cross-slope verification can ensure existing pavement meets minimum cross-slope requirements, and conversely is useful in identifying roadway sections that do not meet minimum standards, which is more desirable than through crash reconnaissance where hydroplaning was evident. Adoption of MLS would enable the South Carolina Department of Transportation (SCDOT) to address cross-slope issues through efficient and accurate data collection methods.

Collecting precise as-built data is essential for tracking construction progress. Three-dimensional models generated from such data capture the as-is conditions of the structures, providing valuable information for monitoring existing infrastructure over time. As-built data can be acquired using a wide range of remote sensing technologies, among which mobile LiDAR is gaining increasing attention due to its ability to collect high-resolution data over a relatively large area in a short time. The quality of mobile LiDAR data depends not only on the grade of onboard LiDAR scanners but also on the accuracy of direct georeferencing information and system calibration. Consequently, millimeter-level accuracy is difficult to achieve. In this study, the performance of mapping-grade and surveying-grade mobile LiDAR systems for bridge monitoring is evaluated against static laser scanners. Field surveys were conducted over a concrete bridge where grinding was required to achieve desired smoothness. A semi-automated, feature-based fine registration strategy is proposed to compensate for the impact of georeferencing and system calibration errors on mobile LiDAR data. Bridge deck thickness is evaluated using surface segments to minimize the impact of inherent noise in the point cloud. The results show that the two grades of mobile LiDAR delivered thickness estimates that are in agreement with those derived from static laser scanning in the 1 cm range. The mobile LiDAR data acquisition took roughly five minutes without having a significant impact on traffic, while the static laser scanning required more than three hours.

Mobile LiDAR Scanning (MLS) can collect 3-dimensional (3D) road and road-related geospatial information accurately and efficiently. Pole-like objects located in road environment are important street furniture and they are necessary information in road inventory and road mapping. The automatic detection and classification of pole-like road objects from mobile LiDAR data can greatly reduce the cost and improve the efficiency. This paper provides a complete workflow for the detection and classification of pole-like road objects from mobile LiDAR data in motorway environment. The major workflow includes three steps: data preprocessing, pole-like road objects detection and pole-like road objects classification. In data preprocessing step, ground points are removed by an automatic ground filtering algorithm, and then off-ground points are clustered into segments and the overlapped segments containing pole-like road objects are further separated through an iterative min-cut based segmentation approach. In detection step, filters utilizing both prior and shape information are used to detect the target objects. In classification step, features of objects are calculated and classified using Random Forest classifier. Our method was tested on two datasets scanned in motorway environment, and the results showed that the Matthews correlation coefficient of the two datasets in detection step was 93.7% and 95.9% respectively and the overall accuracy of the two datasets in classification step was 96.5% and 97.9% respectively.

Edges in mobile light detection and ranging (lidar) point clouds are important for many applications but usually overlooked. In this letter, we propose a fast edge extraction method for mobile lidar. First, an edge index based on geometric center is introduced and then gradients in unorganized 3-D point clouds are defined. By analyzing the ratio between eigenvalues, edge candidates can be detected. Finally, an edge linking algorithm named graph snapping is proposed. The method is tested extensively and the experimental results demonstrate that the proposed method is able to quickly extract most of 3-D edges with higher accuracy than the existing methods.

Three-dimensional (3D) data of roadways are frequently used for extraction of detailed roadway information which is essential for several planning and engineering applications. Recent past has seen...

A high percentage of forest fires take place around roads. These infrastructures provide an escape route for the population in case of fire. Optimization of forest management in the surroundings of roads is a necessary task in terms of wildfires prevention and mitigation of their effects. Therefore, it is essential to avoid the horizontal continuity of vegetation across roads. A methodology for the measurement of canopy area over roads is developed and based on mobile LiDAR (Light Detection and Ranging) point clouds. The acquisition of LiDAR data is done by Lynx Mobile Mapper System from the University of Vigo. The methodology is automated using LiDAR data processing (M-estimator Sample Consensus and near neighbour algorithms) and image processing techniques (rasterization and binarization). The developed algorithms are tested on a study area, the DP-3606 road (Spain). Results are compared with ground truth data of the canopy projected on the road. The best obtained results present a mean geometric error of 2.82% for 0.25 m resolution and 104.02% for 2 m resolution. Furthermore, the higher the pixel size, the greater the error was obtained with a linear correlation value of 0.99. Diputación de Pontevedra | Ref. 17/410.1720.789.02

Because trees can positively influence local environments in urban ecosystems, it is important to measure their morphological characteristics, such as height and diameter at breast height (DBH). However, measuring these data for each individual tree is a time-consuming process that requires a great deal of manpower. In this study, we investigated the feasibility of using mobile LiDAR to estimate tree height and DBH along urban streets and in urban parks. We compared measurements from a mobile LiDAR unit with field measurements of tree height and DBH in urban parks and streets. The height-above-ground and Pratt circle fit methods were applied to calculate tree height and DBH, respectively. The LiDAR-estimated tree heights were highly accurate albeit slightly underestimated, with a root mean square error of 0.359m for the street trees and 0.462m for the park trees. On the other hand, the estimated DBHs were moderately accurate and overestimated, with a root mean square error of 3.77cm for the street trees and 8.95cm for the park trees. Densely planted trees in the park and obstacles in urban areas result in “shadows” (areas with no data), reducing accuracy. Irregular trunk shapes and scanned data that did not include full data point coverage of every trunk were the reasons for the errors. Despite these errors, this study highlights the potential of tree measurements obtained with mobile terrestrial LiDAR platforms to be scaled up from point-based locations to neighborhood-scale and city-scale inventories.

Recently, researchers have paid significant attention to problems related to object detection and computer vision for autonomous vehicles. Such vehicles offer many benefits, including their ability to help address transportation-related issues such as safety concerns, traffic jams, and overall mobility. Multi-beam ‘light detection and ranging’ (LiDAR) is one of the main sensors that is used to sense and detect objects by creating a point cloud data map of the surrounding environment. Current object detection tasks that use only mobile LiDAR data divide the entire area into cubes and employ image object detection methods. Such an approach poses challenges due to the third dimension that increases computational time, which thus requires a tradeoff between performance and time optimization. In this paper, we propose a new approach to detect objects using point cloud data by investigating the shapes of the objects. To this end, we developed a method based on topological data analysis achieved via persistent homology to analyze the qualitative properties of the data. To the best of our knowledge, our work is the first to develop topological data analysis for real-world mobile LiDAR point cloud data exploration. The evaluation result shows a high accuracy classification result using features extracted from barcodes.

The amount of sight distance available to drivers is an extremely critical design element with significant impacts on the safe operation of highways. If the available sight distance on a roadway is limited, this reduces the ability of drivers to safely carry out certain maneuvers such as stopping or passing. Obstructions to sight distance are often caused by a range of different factors. This includes roadside vegetation overgrowing into the road's right of way and obstructions related to the road's alignment. To ensure that sufficient sight distance is available throughout a road's service life, transportation agencies are required to conduct regular long site visits to identify the existence of any obstructions while identifying the types of those obstructions. Although many studies have attempted quantifying available sight distance in the past, not much work has been done to automatically identify and study the types of obstructions causing sight distance limitations. This information is critical to entities looking to decide on the appropriate construction activity required to alleviate sight distance limitations. To fill in this gap, this paper proposes a fully automated novel method to assess sight distance on highways scanned using mobile LiDAR technology, while also proposing a method for the automated classification of the type of obstruction detected. The algorithm is tested on multiple roadway segments in Alberta, Canada and was successful in identifying limitations in sight distance and classifying obstructions into different types of objects.

Inaccurate extrinsic parameters are one of the major errors of a mobile LiDAR system (MLS). As different types of sensors with incommensurable precisions are integrated in an MLS, the extrinsic parameters cannot be easily isolated and estimated, especially in an automatic mode. To address this issue, this paper proposes an automatic extrinsic self-calibration method for an MLS based on planar and spherical features. First, the planar and spherical features are automatically extracted from scanned point cloud data of different strips using the Random Sample Consensus algorithm, and the corresponding features are matched after the extraction. Secondly, a rigorous relationship is established between the direct geo-referencing equation, the extrinsic parameters, and the geometric constraint model. Thirdly, the extrinsic parameters are calibrated by minimizing the sum of the squares of distances from points on the feature surface to the matched reference feature. Four datasets collected by using four types of MLS were used to verify the proposed method. As a result of combining two geometric features into a single self-calibration adjustment, the experimental results show that the proposed method is superior to the conventional plane-based method in terms of the positional accuracy. The standard deviation of the distance between the check features of the four datasets collected by several mobile platforms before (after) extrinsic calibration were 0.051 m (0.024 m), 0.060 m (0.018 m), 0.029 m (0.009 m), and 0.354 m (0.070 m), which demonstrated the high compatibility and practicality of the proposed method.

Inadequate and ineffective roadway and roadside drainage causes highway pavements to fail prematurely. For rehabilitation techniques to perform as desired, surface and subsurface drainage conditions must first be addressed. Mobile lidar is emerging as a safe and effective tool for collecting vast amounts of surface data that can assist in developing drainage designs. This paper presents a case study on the application of mobile lidar to develop a preliminary drainage design on US-75 in north Texas. A design is provided for an underdrain system that includes both longitudinal and lateral pipes. A roadside grading design is provided that works in concert with the underdrain design to move the water away from the pavement structure. Design constraints and challenges include a roadside ditch flow line higher than the pavement structure, slope stability concerns along the frontage road, front slope steepness along a high-speed corridor, and depth of cut within the pavement for installation of the underdrain. The preliminary design, developed from data collected with mobile lidar, was provided to the Texas Department of Transportation. This design has already been used to improve the drainage in the roadside ditch.

Traffic sign readability can be affected by the existence of dirt on traffic sign faces. However, among damaged signs, dirty traffic signs are unique since their damage is not permanent and they just can be cleaned instead of replaced. This study aimed to identify the most important factors contributing to traffic sign dirt. To do so, a large number of traffic signs in Utah were measured by deploying a vehicle instrumented with mobile LiDAR imaging and digital photolog technologies. Each individual daytime digital image was inspected for dirt. Location and climate observations obtained from official sources were compiled using ArcGIS throughout the process. To identify contributing factors to traffic sign dirt, the chi-square test was employed. To analyze the data and rank all of the factors based on their importance to the sign dirt, Random forests statistical model was utilized. After analyzing the data, it can be concluded that ground elevation, sign mount height, and air pollution had the highest effect on making traffic signs dirty. The findings of this investigation assist transportation agencies in determining traffic signs with a higher likelihood of sign dirt. In this way, agencies would schedule to clean such traffic signs more frequently.

The applications of laser scanning technology are rapidly expanding in the civil engineering domain. LiDAR technology is now commonly used in the surveying and monitoring of large infrastructures. In particular, tunnels have become key transport infrastructures, subjected to maintenance processes that allow quality checks for tunnel modifications or tunnel clearance and profile checks. The research described in this paper targets developing an approach to semi-automatically retrieve the tunnel vertical clearance based on ground based mobile LiDAR data. The steps of this approach include extraction of cross sections orthogonal to the vehicle trajectory and road markings based on radiometric information, and conversion of cross section to a two-dimensional profile to estimate the vertical clearance. The validation of the developed approach is done using real-life case study, a road tunnel in southern Galicia, Spain. An accuracy of 100% in detection of cross sections is achieved. Only one of the cross sections shows a relative error in vertical clearance measurement higher than 1%. The results demonstrated the effectiveness of the developed approach for computing vertical clearances and demonstrating that tunnel management activities can definitely benefit from using mobile LiDAR by minimizing survey time and increasing productivity in dangerous environments.