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Concrete surface roughness is integral to interface shear resistance and overall integrity between concrete cast at different times, influencing structure safety. This interface is found at common construction joints that enable composite action between precast and cast-in-place concrete elements or monolith behaviour between two cast-in-place pours. Traditional on-site roughness evaluation relies on qualitative methods, such as visual comparison with predefined surface profiles. These assessment methods are subjective, time-consuming, and inconsistent. The absence of quantitative methods creates a notable gap in the data-to-decision framework. Recent advancements in smartphone LiDAR technology hold potential to provide a solution. Our study introduces a novel method that leverages smartphone LiDAR technology to precisely measure concrete roughness quantitatively. Data is acquired from the LiDAR system in the form of a point cloud, which captures the three-dimensional (3D) structure of the surface. Our study comprises comprehensive laboratory experiments to assess LiDAR operability, followed by on-site experiments for roughness quantification across five sites with varying levels of concrete roughness. The LiDAR data are compared with ground truth 3D data collected using a structured light sensor. The results demonstrate that the proposed method presents a reliable alternative for measuring concrete surface roughness in the field. [ABSTRACT FROM AUTHOR]

This article examines the potential of low-cost LiDAR technology for 3D modeling and assessment of the degradation of historic buildings, using a section of the Koszalin city walls in Poland as a case study. Traditional terrestrial laser scanning (TLS) offers high accuracy but is expensive. The study assessed whether more accessible LiDAR options, such as those integrated with mobile devices such as the Apple iPad Pro, can serve as viable alternatives. This study was conducted in two phases—first assessing measurement accuracy and then assessing degradation detection—using tools such as the FreeScan Combo scanner and the Z+F 5016 IMAGER TLS. The results show that, while low-cost LiDAR is suitable for small-scale documentation, its accuracy decreases for larger, complex structures compared to TLS. Despite these limitations, this study suggests that low-cost LiDAR can reduce costs and improve access to heritage conservation, although further development of mobile applications is recommended. [ABSTRACT FROM AUTHOR]

Copyright of Turkey Lidar Journal / Türkiye Lidar Dergisi is the property of Mersin University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigated the integration of LiDAR technology in cut-to-length (CTL) harvesting machines to enhance tree selection accuracy and efficiency. In the evolution of CTL forest machines towards improving operational efficiency and operator conditions, challenges persist in manual tree selection during thinning operations, especially under unmarked conditions and complex environments. These can be improved due to advances in technology. We studied the potential of LiDAR systems in assisting harvester operators, aiming to mitigate workload, reduce decision errors, and optimize the harvesting workflow. We used both synthetic and real-world 3D point cloud data sets for tree stem defect analysis. The former was crafted using a 3D modelling engine, while the latter originated from forest observations using 3D LiDAR on a CTL harvester. Both data sets contained instances of tree stem defects that should be detected. We demonstrated the potential of LiDAR technology: The analysis of synthetic data yielded a Root Mean Square Error (RMSE) of 0.00229 meters (m) and an RMSE percentage of 0.77%, demonstrating high detection accuracy. The real-world data also showed high accuracy, with an RMSE of 0.000767 m and an RMSE percentage of 1.39%. Given these results, we recommend using on-board LiDAR sensor technologies for collecting and analyzing data on tree/forest quality in real-time. This will help overcome existing barriers and drive forest operations toward enhanced efficiency and sustainability. [ABSTRACT FROM AUTHOR]

Mobile LiDAR technology is a powerful tool that accurately captures spatial information about typical static objects in road scenes. However, the precise extraction and classification of these objects pose persistent technical challenges. In this paper, we employ a deep learning approach to tackle the point cloud classification problem. Despite the popularity of the PointNet++ network for direct point cloud processing, it encounters issues related to insufficient feature learning and low accuracy. To address these limitations, we introduce a novel layer-wise optimization network, LO-Net. Initially, LO-Net utilizes the set abstraction module from PointNet++ to extract initial local features. It further enhances these features through the edge convolution capabilities of GraphConv and optimizes them using the "Unite_module" for semantic enhancement. Finally, it employs a point cloud spatial pyramid joint pooling module, developed by the authors, for the multiscale pooling of final low-level local features. Combining three layers of local features, LO-Net sends them to the fully connected layer for accurate point cloud classification. Considering real-world scenarios, road scene data often consist of incomplete point cloud data due to factors such as occlusion. In contrast, models in public datasets are typically more complete but may not accurately reflect real-world conditions. To bridge this gap, we transformed road point cloud data collected by mobile LiDAR into a dataset suitable for network training. This dataset encompasses nine common road scene features; hence, we named it the Road9 dataset and conducted classification research based on this dataset. The experimental analysis demonstrates that the proposed algorithm model yielded favorable results on the public datasets ModelNet40, ModelNet10, and the Sydney Urban Objects Dataset, achieving accuracies of 91.2%, 94.2%, and 79.5%, respectively. On the custom road scene dataset, Road9, the algorithm model proposed in this paper demonstrated outstanding classification performance, achieving a classification accuracy of 98.5%. [ABSTRACT FROM AUTHOR]

A mobile lidar was designed for ozone sensing at wavelengths of 299 and 341 nm and put into operation. The lidar covers the altitude range from 0.1 to 12 km with a spatial resolution of lidar signals from 1.5 to 150 m. The specification of the mobile lidar is given; the results of an in situ experiment on laser sensing of the atmosphere in Tomsk are presented. The lidar echoes and ozone profiles retrieved confirm the information content of lidar measurements in the troposphere. The lidar and satellite (MetOp) measurements are compared. [ABSTRACT FROM AUTHOR]

This article presents a system for recording 3D point clouds of riverbanks with a mobile lidar mounted on an uncrewed water vehicle. The focus is on the orientation of the platform and the lidar sensor. Rivers are areas where the conditions for highly accurate GNSS can be sub-optimal due to multipath effects from the water and shadowing effects by bridges, steep valleys, trees, or other objects at the riverbanks. Furthermore, a small measurement platform may have an effect on the accuracy of orientations measured by an IMU; for instance, caused by electromagnetic fields emitted by the boat rotors, the lidar, and other hardware decreasing IMU accuracy. As an alternative, we use exterior orientation parameters obtained by photogrammetric methods from the images of a camera on the boat capturing the riverbanks in time-lapse mode. Using control points and tie points on the riverbanks enables georeferenced position and orientation determination from the image data, which can then be used to transform the lidar data into a global coordinate system. The main influences on the accuracy of the camera orientations are the distance to the riverbanks, the size of the banks, and the amount of vegetation on them. Moreover, the quality of the camera orientation-based lidar point cloud also depends on the time synchronization of camera and lidar. The paper describes the data processing steps for the geometric lidar–camera integration and delivers a validation of the accuracy potential. For quality assessment of a point cloud acquired with the described method, a comparison with terrestrial laser scanning has been carried out. [ABSTRACT FROM AUTHOR]

The human population is constantly increasing throughout the world, and accordingly, construction is increasing in the same way. Therefore, there is an emergence of irregular and unplanned urbanization. In order to achieve the goal of preventing irregular and unplanned urbanization, it is necessary to monitor the cadastral borders quickly. In this sense, the concept of a sensitive, up-to-date, object-based, 3D, and 4D (4D, 3D + time) cadastral have to be a priority. Therefore, continuously updating cadastral maps is important in terms of sustainability and intelligent urbanization. In addition, due to the increase in urbanization, it has become necessary to update the cadastral information system and produce 3D cadastral maps. However, since there are big problems in data collection in urban areas where construction is rapid, different data-collection devices are constantly being applied. While these data-collection devices have proven themselves in terms of accuracy and precision, new technologies have started to be developed in urban areas especially, which is due to the increase in human population and the influence of environmental factors. For this reason, LiDAR data collection methods and the SLAM algorithm can offer a new perspective for producing cadastral maps in complex urban areas. In this study, 3D laser scanning data obtained from a portable sensor based on the SLAM algorithm are tested, which is a relatively new approach for cadastral surveys in complex urban areas. At the end of this study, two different statistical comparisons and accurate analyses of the proposed methodology with reference data were made. First, WMLS data were compared with GNSS data and RMSE values for X, Y, and Z, and were found to be 4.13, 4.91, and 7.77 cm, respectively. In addition, WMLS length data and cadastral length data from total-station data were compared and RMSE values were calculated as 4.76 cm. [ABSTRACT FROM AUTHOR]

Mobile LiDAR technology is a powerful tool that accurately captures spatial information about typical static objects in road scenes. However, the precise extraction and classification of these objects pose persistent technical challenges. In this paper, we employ a deep learning approach to tackle the point cloud classification problem. Despite the popularity of the PointNet++ network for direct point cloud processing, it encounters issues related to insufficient feature learning and low accuracy. To address these limitations, we introduce a novel layer-wise optimization network, LO-Net. Initially, LO-Net utilizes the set abstraction module from PointNet++ to extract initial local features. It further enhances these features through the edge convolution capabilities of GraphConv and optimizes them using the “Unite_module” for semantic enhancement. Finally, it employs a point cloud spatial pyramid joint pooling module, developed by the authors, for the multiscale pooling of final low-level local features. Combining three layers of local features, LO-Net sends them to the fully connected layer for accurate point cloud classification. Considering real-world scenarios, road scene data often consist of incomplete point cloud data due to factors such as occlusion. In contrast, models in public datasets are typically more complete but may not accurately reflect real-world conditions. To bridge this gap, we transformed road point cloud data collected by mobile LiDAR into a dataset suitable for network training. This dataset encompasses nine common road scene features; hence, we named it the Road9 dataset and conducted classification research based on this dataset. The experimental analysis demonstrates that the proposed algorithm model yielded favorable results on the public datasets ModelNet40, ModelNet10, and the Sydney Urban Objects Dataset, achieving accuracies of 91.2%, 94.2%, and 79.5%, respectively. On the custom road scene dataset, Road9, the algorithm model proposed in this paper demonstrated outstanding classification performance, achieving a classification accuracy of 98.5%.

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Seismic lines are narrow, linear corridors cleared through forests for oil and gas exploration. Their inconsistent recovery has led to Alberta's forests being highly fragmented, resulting in the need for seismic line restoration programs and subsequent monitoring. Light detection and ranging (LiDAR) is becoming an increasingly popular technology for the fast and accurate measurement of forests. Mobile LiDAR scanners (MLS) are emerging as an alternative to traditional aerial LiDAR due to their increased point cloud density. To determine whether MLS could be effective for collecting vegetation data on seismic lines, we sampled 17 seismic lines using the Emesent Hovermap™ in leaf-on and leaf-off conditions. Processing the LiDAR data was conducted with GreenValley International's LiDAR 360 software, and data derived from the point clouds were compared to physically measured field data. Overall, the tree detection algorithm was unsuccessful at accurately segmenting the point clouds. Complex vegetation environments on seismic lines, including small conifers with obscured stems or extremely dense and tall shrubs with overlapping canopies, posed a challenge for the software's capacity to differentiate trees As a result, tree densities and diameters were overestimated, while tree heights were underestimated. Exploration of alternative algorithms and software is needed if measuring vegetation data on seismic lines with MLS is to be implemented. [ABSTRACT FROM AUTHOR]

Emerging mobile LiDAR mapping systems exhibit great potential as an alternative for mapping urban environments. Such systems can acquire high-quality, dense point clouds that capture detailed information over an area of interest through efficient field surveys. However, automatically recognizing and semantically segmenting different components from the point clouds with efficiency and high accuracy remains a challenge. Towards this end, this study proposes a semantic segmentation framework to simultaneously classify bridge components and road infrastructure using mobile LiDAR point clouds while providing the following contributions: 1) a deep learning approach exploiting graph convolutions is adopted for point cloud semantic segmentation; 2) cross-labeling and transfer learning techniques are developed to reduce the need for manual annotation; and 3) geometric quality control strategies are proposed to refine the semantic segmentation results. The proposed framework is evaluated using data from two mobile mapping systems along an interstate highway with 27 highway bridges. With the help of the proposed cross-labeling and transfer learning strategies, the deep learning model achieves an overall accuracy of 84% using limited training data. Moreover, the effectiveness of the proposed framework is verified through test covering approximately 42 miles along the interstate highway, where substantial improvement after quality control can be observed.

Extracting street trees from mobile Light Detection and Ranging (LiDAR) point clouds is still encountering challenges, such as low extraction accuracy and poor robustness in complex urban environment, and difficulty in the segmentation of overlapping trees. To solve these problems, this paper proposed a street tree extraction and segmentation method based on spatial geometric features of object primitives. In this paper, mobile LiDAR point clouds were first segmented into object primitives based on the proposed graph segmentation method, which can release the computation burden effectively. According to the spatial geometric features of the segmented object primitives, stem points were extracted. In doing so, the robustness and accuracy for stem detecting can be improved. Furthermore, voxel connectivity analysis and individual tree optimization were combined successively. In doing so, the neighboring trees could be separated successfully. Four datasets located in Henan Polytechnic University, China, were used for validating the performance of the proposed method. The four mobile LiDAR point clouds contained 106, 45, 76, and 46 trees, respectively. The experimental results showed that the proposed method can achieve the performance of individual tree separation in all the four testing plots. Compared to the other three methods, the proposed method can make a good balance between the commission and omission errors and achieved the highest average F1 scores. [ABSTRACT FROM AUTHOR]

This article presents a system for recording 3D point clouds of riverbanks with a mobile lidar mounted on an uncrewed water vehicle. The focus is on the orientation of the platform and the lidar sensor. Rivers are areas where the conditions for highly accurate GNSS can be sub-optimal due to multipath effects from the water and shadowing effects by bridges, steep valleys, trees, or other objects at the riverbanks. Furthermore, a small measurement platform may have an effect on the accuracy of orientations measured by an IMU; for instance, caused by electromagnetic fields emitted by the boat rotors, the lidar, and other hardware decreasing IMU accuracy. As an alternative, we use exterior orientation parameters obtained by photogrammetric methods from the images of a camera on the boat capturing the riverbanks in time-lapse mode. Using control points and tie points on the riverbanks enables georeferenced position and orientation determination from the image data, which can then be used to transform the lidar data into a global coordinate system. The main influences on the accuracy of the camera orientations are the distance to the riverbanks, the size of the banks, and the amount of vegetation on them. Moreover, the quality of the camera orientation-based lidar point cloud also depends on the time synchronization of camera and lidar. The paper describes the data processing steps for the geometric lidar–camera integration and delivers a validation of the accuracy potential. For quality assessment of a point cloud acquired with the described method, a comparison with terrestrial laser scanning has been carried out.

Clear and distinctive pavement markings play a critical role in providing traffic information and avoiding traffic crashes at night and in adverse weather conditions. Periodically inspecting pavement marking is essential but requires vast human and material resources currently. This paper attempts to use the low-channel LiDAR sensor to detect and evaluate the stain, wear, and cracks of pavement marking based on the research of Pike and Che that the retroreflectivity of pavement markings is highly correlated with the laser intensity of LiDAR. Thus, the deployment location of mobile LiDAR based on its built-in characteristics and mechanical structure is optimized. An optimization model is formed and solved with the elitist preservation genetic algorithm (EGA). The results show that the best installation height is 0.5 m. The rotation angle of LiDAR is 128.4°, 148.0° in the urban and highway scenarios, respectively. The field test results show that the pavement marking can obtain best the coverage, density, and effective use of LiDAR point cloud with the above setting. The field test’s mean relative error (MRE) is less than 5% and 10% in vertical installation and vertical installation with an inclination, respectively. [ABSTRACT FROM AUTHOR]

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 depict the urban built environment with a high level of details and accuracy. However, few three-dimensional visibility approaches have been developed for the street-level point-cloud data. Accordingly, an approach based on mobile LiDAR point clouds has been developed to map the three-dimensional visibility at the street level. The method consists of five steps: voxelization of point-cloud data, construction of lines-of-sight, construction of sectors of sight, construction of three-dimensional visible space, and calculation of volume index. The proposed approach is able to automatically measure the volume of visible space and openness at any viewpoint along a street. This approach has been applied to three study areas. The results indicated that the proposed approach enables accurate simulation of visible space as well as high-resolution (1 m × 1 m) mapping of the visible volume index. The proposed approach can make a contribution to the improvement of urban planning and design processes that aim at developing more sustainable built environments.

[Display omitted] Trees are important road-side objects, and their geometric information plays an essential role in road studies and safety analyses. This paper proposes an efficient method for the automated creation of a road-side tree inventory using Mobile Terrestrial Lidar System (MTLS) point clouds. In the proposed method ground points are filtered through preprocessing to reduce processing time. Next, tree trunks are detected by performing a Hough Transform (HT) algorithm on several generated raster images from the point clouds. By initiating an approximate area of a tree's foliage through a Voronoi Tessellation (VT) algorithm, the accurate boundary of the foliage is identified by applying Active Contour (AC) models. By extracting the points within this foliage boundary the geometric characteristics of each tree are obtained. This method was evaluated with two sample point clouds from different MTLS systems, and the algorithm correctly extracted all of the trees from both datasets. Additionally, comparing the calculated parameters with manually observed measures, the accuracy of the obtained geometric parameters were promising. [ABSTRACT FROM AUTHOR]

Abstract 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.

Highly accurate 2D maps can supply basic geospatial information for efficient and accurate indoor building modeling. However, problematic scenarios, which are characterized by few features, similar components and large scales, seriously influence data association and cumulative error elimination, and thus degrade simultaneous localization and mapping (SLAM)-based mapping quality. In this paper, a cross-correction LiDAR SLAM method is proposed for constructing high-accuracy 2D maps of problematic scenarios. The method comprises two models. The first model, namely, pose correction for rough mapping (PCRM), increases the data association capacity and generates a rough map with cumulative errors. In the PCRM model, a rough mapping module is developed against the scenario with few features for accurate data association. This module improves the robustness of the data association by using the initial poses from the local pose correction module, especially in similar-component scenarios. The other is a map correction for pose optimization (MCPO) model, which enhances cumulative error elimination capacity. Here, a block-based local map correction module is proposed that takes both map and pose into consideration to construct accurate constraints. The constraints are then added to the global pose optimization module to significantly reduce the cumulative error of the rough map and thus construct a high-accuracy 2D map. The results demonstrate the superiority of our method over 5 other state-of-the-art methods in problematic scenarios. The overall performance of our method in these two scenarios is approximately 1 cm and 0.2% in terms of the absolute and relative map errors, respectively. Moreover, the modeling results demonstrate that our method can be applied to the efficient and accurate indoor modeling. [ABSTRACT FROM AUTHOR]

Copyright of Geomatik is the property of Murat Yakar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. Plain Language Summary: Improved understanding of beach morphology is needed for coastal planning and hazard mitigation. Using remote sensing‐based survey technology, this study describes about 1 year of weekly observations of cusps, semicircular alongshore rhythmic patterns, along an ∼2 km beach consisting of sands and larger grain size sediments (gravels and cobbles). Cusp shape (e.g., horizontal and vertical undulations), alongshore rhythmic spacing patterns, and surface sediment types varied in space and time. The observation revealed previously unreported, composite mega cusps with rhythmic 200–400 m spacing and surface sediment zonations (sand versus gravel/cobble). The results highlight the potential of repeated surveys with modern remote sensing technology to explore the highly dynamic and complex behavior of mixed sediment beach morphology. Key Points: Cusps composed of sand, gravel/cobble, and mixed surface sediment types with 11–400 m alongshore wavelengths were observedCusp morphology (e.g., wavelengths and amplitudes) and surface sediment types considerably vary temporally and spatiallyFrequent surveys with high spatial resolution show highly dynamic and complex behavior of mixed sediment beach morphology [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mechanically stabilized earth (MSE) walls rely on its weight to resist the destabilizing earth forces acting at the back of the reinforced soil area. MSE walls are a common infrastructure along national and international transportation corridors as they are low-cost and have easy-to-install precast concrete panels. The usability of such transportation corridors depends on the safety and condition of the MSE wall system. Consequently, MSE walls have to be periodically monitored according to prevailing transportation asset management criteria during the construction and serviceability life stages to ensure that their predictable performance measures are met. To date, MSE walls are monitored using qualitative approaches such as visual inspection, which provide limited information. Aside from being time-consuming, visual inspection is susceptible to bias due to human subjectivity. Manual and visual inspection in the field has been traditionally based on the use of a total station, geotechnical field instrumentation, and/or static terrestrial laser scanning (TLS). These instruments can provide highly accurate and reliable performance measures; however, their underlying data acquisition and processing strategies are time-consuming and not scalable. The proposed strategy in this research provides several global and local serviceability measures through efficient processing of point cloud data acquired by a mobile LiDAR system (MLS) for MSE walls with smooth panels without the need for installing any targets. An ultra-high-accuracy vehicle-based LiDAR data acquisition system has been used for the data acquisition. To check the viability of the proposed methodology, a case study has been conducted to evaluate the similarity of the derived serviceability measures from TLS and MLS technologies. The results of that comparison verified that the MLS-based serviceability measures are within 1 cm and 0.3° of those obtained using TLS and thus confirmed the potential for using MLS to efficiently acquire point clouds while facilitating economical, scalable, and reliable monitoring of MSE walls. [ABSTRACT FROM AUTHOR]

Do, J.D.; Chang, Y.S.; Kim, C.H.; Kim, W-.H.; Lee, B-.G.; Wie, G. J., and Jin, J.-W., 2020. Measurement of nearshore seabed bathymetry using airborne/mobile LiDAR and multibeam sonar at Hujeong Beach, Korea. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1067-1071. Coconut Creek (Florida), ISSN 0749-0208. Accurate bathymetric measurements are important, especially in nearshore areas, because coastal sediment transport and the resulting beach erosion/accretion processes are significantly affected by seabed topography. At Hujeong Beach in Korea, for example, local shorelines show severe spatiotemporal variations as a result of seabed topography including underwater rocks. The accuracy of bathymetry measurements are not guaranteed, however, because high-accuracy acoustic sensors—such as multi-beam echo sounders—have a limited measurement capability. In this study, we investigated the accuracy of the nearshore bathymetry at Hujeong Beach using airborne LiDAR (a-LiDAR) data from May, 2017. One of the benefits of a-LiDAR is its ability to conduct both onshore and offshore mapping by detecting laser pulses reflected from land, the sea surface, and seabeds. The a-LiDAR measurements were validated onshore in a sloped roadside area using data measured via ship-mounted mobile LiDAR (m-LiDAR). A high agreement was found between the two datasets, with R-squared values of 0.9999 and mean square errors less than 0.05 m2. In measurements of seabed bathymetry, a-LiDAR agreed well with measurements from a multi-beam echo sounder over a sandy seabed and showed an accuracy similar to the m-LiDAR validation results. However, the accuracy of a-LiDAR measurements decreased significantly over rocky seabed; the mean square error compared to the multi-beam data increased to 0.6–0.8 m2. The high a-LiDAR errors over this rocky area may be a consequence of the irregular shape of its surface. Return signals from underwater rocks of varying shapes may be spread over wider angles, causing signal interference and reducing the measurement resolution in the area. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

First Floor Elevation (FFE) of a house is crucial information for flood management and for accurately assessing the flood exposure risk of a property. However, the lack of reliable FFE data on a large geographic scale significantly limits efforts to mitigate flood risk, such as decision on elevating a property. The traditional method of collecting elevation data of a house relies on time-consuming and labor-intensive on-site inspections conducted by licensed surveyors or engineers. In this paper, we propose an automated and scalable method for extracting FFE from mobile LiDAR point cloud data. The fine-tuned yolov5 model is employed to detect doors, windows, and garage doors on the intensity-based projection of the point cloud, achieving an mAP@0.5:0.95 of 0.689. Subsequently, FFE is estimated using detected objects. We evaluated the Median Absolute Error (MAE) metric for the estimated FFE in Manville, Ventnor, and Longport, which resulted in values of 0.2 ft, 0.27 ft, and 0.24 ft, respectively. The availability of FFE data has the potential to provide valuable guidance for setting flood insurance premiums and facilitating benefit-cost analyses of buyout programs targeting residential buildings with a high flood risk. • An automated method is developed to extract first floor elevation for buildings from mobile LiDAR data. • The method trains a deep learning model on projected building facade LiDAR image to extract first floor elevation. • Mobile LiDAR data of three communities with varied building topologies were used to test the method. • The proposed method is capable of deriving accurate first floor elevation data to support floodplain management. [ABSTRACT FROM AUTHOR]

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.

The assembly error of the supporting component in Mobile Lidar has an inevitable influence on the positioning accuracy and the system error. In this paper, we applied the multi-body system kinematics principle and the homogeneous coordinate transformation to infer the final pointing error formula which influences the three-axis error model of the Mobile Lidar. The influence of each error item on the positioning accuracy (pointing accuracy) of radar system is analyzed by computer simulation, and the motion law between each axis of radar supporting component is discussed, which has laid the base for researching the positioning accuracy of Mobile Lidar. [ABSTRACT FROM AUTHOR]

• Composition and extent of deterioration forms on the Nuevo Puente were detected. • Orthophotos acquired by a mobile LiDAR system allowed to quantify the deterioration pattern extent. • Black crust, calcite encrustation and biological colonization patinas were detected by XRD FTIR and optical microscopy. • Black crust and biological colonization exhibited similar reflectance levels. 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. [ABSTRACT FROM AUTHOR]

Road markings that provide instructions for unmanned driving are important elements in high-precision maps. In road information collection technology, multi-beam mobile LiDAR scanning (MLS) is currently adopted instead of traditional mono-beam LiDAR scanning because of the advantages of low cost and multiple fields of view for multi-beam laser scanners; however, the intensity information scanned by multi-beam systems is noisy and current methods designed for road marking detection from mono-beam point clouds are of low accuracy. This paper presents an accurate algorithm for detecting road markings from noisy point clouds, where most nonroad points are removed and the remaining points are organized into a set of consecutive pseudo-scan lines for parallel and/or online processing. The road surface is precisely extracted by a moving fitting window filter from each pseudo-scan line, and a marker edge detector combining an intensity gradient with an intensity statistics histogram is presented for road marking detection. Quantitative results indicate that the proposed method achieves average recall, precision, and Matthews correlation coefficient (MCC) levels of 90%, 95%, and 92%, respectively, showing excellent performance for road marking detection from multi-beam scanning point clouds.

Mechanically stabilized earth (MSE) walls rely on its weight to resist the destabilizing earth forces acting at the back of the reinforced soil area. MSE walls are a common infrastructure along national and international transportation corridors as they are low-cost and have easy-to-install precast concrete panels. The usability of such transportation corridors depends on the safety and condition of the MSE wall system. Consequently, MSE walls have to be periodically monitored according to prevailing transportation asset management criteria during the construction and serviceability life stages to ensure that their predictable performance measures are met. To date, MSE walls are monitored using qualitative approaches such as visual inspection, which provide limited information. Aside from being time-consuming, visual inspection is susceptible to bias due to human subjectivity. Manual and visual inspection in the field has been traditionally based on the use of a total station, geotechnical field instrumentation, and/or static terrestrial laser scanning (TLS). These instruments can provide highly accurate and reliable performance measures; however, their underlying data acquisition and processing strategies are time-consuming and not scalable. The proposed strategy in this research provides several global and local serviceability measures through efficient processing of point cloud data acquired by a mobile LiDAR system (MLS) for MSE walls with smooth panels without the need for installing any targets. An ultra-high-accuracy vehicle-based LiDAR data acquisition system has been used for the data acquisition. To check the viability of the proposed methodology, a case study has been conducted to evaluate the similarity of the derived serviceability measures from TLS and MLS technologies. The results of that comparison verified that the MLS-based serviceability measures are within 1 cm and 0.3° of those obtained using TLS and thus confirmed the potential for using MLS to efficiently acquire point clouds while facilitating economical, scalable, and reliable monitoring of MSE walls.

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. [ABSTRACT FROM AUTHOR]

Abstract In the recent years, mobile LiDAR data has become an important data source for building mapping. However, it is challenging to extract building instances in residential areas where buildings of different structures are closely distributed and surrounded by cluttered objects such as vegetations. In this paper, we present a new “localization then segmentation” framework to tackle these problems. First, a hypothesis and selection method is proposed to localize buildings. Rectangle proposals which indicate building locations are generated using projections of vertical walls obtained by region growing. The selection of rectangles is formulated as a constrained maximization problem, which is solved by linear programming. Then, point clouds are divided into groups, each of which contains one building instance. A foreground-background segmentation method is then proposed to extract buildings from complex surroundings in each group. Based on the graph of points, an objective function which integrates local geometric features and shape priors is minimized by the graph cuts. The experiments are conducted in two large and complex scenes, Calgary and Kentucky residential areas. The completeness and correctness of building localization in the former dataset are 87.2% and 91.34%, respectively. In the latter dataset, the completeness and correctness of building localization are 100% and 96.3%, respectively. Based on the tests, our binary segmentation method outperforms existing methods regarding the F1 measure. These results demonstrate the feasibility and effectiveness of our framework in extracting instance-level residential buildings from mobile LiDAR point clouds in suburban areas. [ABSTRACT FROM AUTHOR]

The georeferenced information acquisition for large scales digital terrain models generation is usually a rather time-consuming and costly process. The use of point clouds gathered by mobile LiDAR systems therefore appears as a possible source for the creation of those models, particularly in remote areas where security issues requires a maximum speed field collection information. However, the filtering challenge to separate the points that represents the terrain surface from the remaining, in an automat ic and consistent way, is an open challenge that continues to arouse the interest of researchers. Using a multigrid filtering method, this work presents a comparative analysis of different shapes of regular grid cells, including the implementation algorithms for each cell shape. Also, a coordinate grid concept applied to each cell shape is proposed. Finally, a discussion about the obtained results of the method application using point clouds collected in different environments from different ground LiDAR systems are presented. [ABSTRACT FROM AUTHOR]

Mobile Light Detection And Ranging (LiDAR) point clouds have the characteristics of complex and incomplete scenes, uneven point density and noises, which raises great challenges for automatically interpreting 3D scene. Aiming at the problem of 3D point cloud classification, we propose a probabilistic graphical model for automatic classification of mobile LiDAR point clouds in this paper. First, the super-voxels are generated as primitives based on the similar geometric and radiometric properties. Second, we construct point-based multi-scale visual features that are used to describe the texture information at various scales. Third, the topic model is used to analyze the semantic correlations among points within super-voxels to establish the semantic representation, which is finally fed into the proposed probabilistic graphical model. The proposed model combines Bayesian network and Markov random fields to obtain locally continuous and globally optimal classification results. To evaluate the effectiveness and the robustness of the proposed method, experiments were conducted using mobile LiDAR point clouds for three types of street scenes. Experimental results demonstrate that our proposed model is efficient and robust for extracting vehicles, buildings, street trees and pole-like objects, with overall accuracies of 98.17%, 97.41% and 96.81% respectively. Moreover, compared with other existing methods, our proposed model can provide higher classification correctness, specifically for small objects such as cars and pole-like objects. [ABSTRACT FROM AUTHOR]

For mobile LiDAR point cloud data, a new hybrid index structure combining global KD-tree and local Octree is proposed to improve the efficiency of data organization and management, which is named as KD-OcTree index. Firstly, global KD-tree reconstructs the spatial neighborhood relations by defining the segmenting dimension and segmenting planes, for the purpose of ensuring the balance of the whole index. Then, local Octree is constructed in the leafs of KD-tree, which can avoid some shortcomings such as the unbalance of point cloud distribution, deeper Octree, large amount of non-point space, and so on. Lastly, we take three real scenes' point clouds as test data to process. The experimental results and comparative analysis show that the KD-OcTree index can not only improve the speed of constructing index and neighborhood searching, but also improve the effect of data-processing and influence the reliability of classification. [ABSTRACT FROM AUTHOR]

A critical deficiency in any one or a combination of three transportation system characteristics: the driver, roadway, or vehicle can contribute to an elevated crash risk for the motoring public. Traffic signs often convey critical information to drivers. However, traffic signs are only effective when clearly visible and legible. Traffic sign vandalism that is exclusively the results of humans causes both sign legibility and visibility to deteriorate. Transportation agencies spend a significant amount of money to repair or replace vandalized signs. This study was conducted to identify which traffic signs are more vulnerable to vandalism. To do this, a mobile-based vehicle collected data of over 97000 traffic signs managed by the Utah Department of Transportation (UDoT), US. The vandalized signs were identified by a trained operator through inspection of daytime digital images taken of each individual sign. Location data obtained from online sources combined with the traffic sign data were imported into ArcGIS to acquire localized conditions for each individual sign. According to the chi-square test results, the association between vandalism and traffic sign attributes and localized conditions, including background color, size, mount height, exposure, land cover, and road type was evident. After employing the random forests model, the most important factors in making signs vulnerable to vandalism were identified. [ABSTRACT FROM AUTHOR]

ABSTRACT 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 (LEICA GS15 GS25), and a fixed platform. The shipborne mobile LiDAR system is much more effective than terrestrial LiDAR systems for the measurements without shadow zones in foreshore areas. We conducted coast area measurements at January 2016 (winter season) and September 2016 (summer season) in Anmok Beach, Gangneung, Korea. The study area is located in the north of the Gangneung port. Coastal erosion occurs frequently in the study area and coastal terrain changes continuously. Based on the measured mobile LiDAR data, the coastline of the Anmok Beach was extracted. Compared to the winter coastline, the summer coastline has undergone many changes. The shoreline curvature of the summer was larger than that of the winter and the summer coastline alternated between landward migration and seaward migration in the northern part of the study area. In the southern part, the landward migration of the shoreline mainly appeared. The concomitant wave data, including wave directions, significant wave height, and energy spectrum, showed strong correlation with the coastline change. The statistical results of high wave data indicated that high waves coming from NNE and NE in winter and from ENE in summer were dominant. Effective monitoring of the coastline changes using the shipborne mobile LiDAR system will be able to contribute to coastal erosion management and response. [ABSTRACT FROM AUTHOR]

This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds. [ABSTRACT FROM AUTHOR]