Mobile Light Detection And Ranging Research Articles

Aboveground woody biomass estimation of young bioenergy plantations of Populus and its hybrids using mobile (backpack) LiDAR remote sensing

Woody aboveground biomass (AGB) including short-rotation Populus is used as a feedstock for renewable and carbon-neutral bioenergy. While woody AGB can be estimated with allometric equations requiring labor-intensive field data, remote sensing technologies like mobile terrestrial light detection and ranging (LiDAR) can estimate woody AGB quickly and accurately. Therefore, the goals of this study were to develop a model to predict woody AGB of 2-year-old Populus spp. from three taxa (P. deltoides, P. deltoides × P. maximowiczii and P. deltoides × P. trichocarpa) using allometric (height and diameter at breast height (DBH)) or LiDAR-derived metrics from a mobile terrestrial (backpack) system. Likewise, we sought to compare LiDAR-estimated tree height and DBH with field-measured values. We found that a taxa-specific model containing LiDAR-measured tree height, crown volume, and taxa interactions with the height of the 10th percentile, and the density of the lowest interval (density metric 0) explained 84 % of the variation in woody AGB with a root mean square error (RMSE) of 28.7 % and performed slightly better than the allometric model. The best model excluding taxa had a slightly higher RMSE but lower bias than the allometric model. LiDAR-derived tree heights were highly correlated with field-measured heights, but DBH could not be estimated accurately. Therefore, terrestrial mobile LiDAR systems can accurately estimate woody AGB and tree height of Populus in short rotation systems to aid in the fast and efficient quantification of woody bioenergy production and renewable energy resources.

Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data

The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B+-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems.

Wind Profile Reconstruction Based on Convolutional Neural Network for Incoherent Doppler Wind LiDAR

The rapid development of artificial intelligence (AI) and deep learning has revolutionized the field of data analysis in recent years, including signal data acquired by remote sensors. Light Detection and Ranging (LiDAR) technology is widely used in atmospheric research for measuring various atmospheric parameters. Wind measurement using LiDAR data has traditionally relied on the spectral centroid (SC) algorithm. However, this approach has limitations in handling LiDAR data, particularly in low signal-to-noise ratio (SNR) regions. To overcome these limitations, this study leverages the capabilities of customized deep-learning techniques to achieve accurate wind profile reconstruction. The study uses datasets obtained from the European Centre for Medium Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) and the mobile Incoherent Doppler LiDAR (ICDL) system constructed by the University of Science and Technology of China. We present a simulation-based approach for generating wind profiles from the statistical data and the associated theoretical calculations. Whereafter, our team constructed a convolutional neural network (CNN) model based on the U-Net architecture to replace the SC algorithm for LiDAR data post-processing. The CNN-generated results are evaluated and compared with the SC results and the ERA5 data. This study highlights the potential of deep learning-based techniques in atmospheric research and their ability to provide more accurate and reliable results.

Segmentation of LiDAR point cloud data in urban areas using adaptive neighborhood selection technique.

Semantic segmentation of urban areas using Light Detection and Ranging (LiDAR) point cloud data is challenging due to the complexity, outliers, and heterogeneous nature of the input point cloud data. The machine learning-based methods for segmenting point clouds suffer from the imprecise computation of the training feature values. The most important factor that influences how precisely the feature values are computed is the neighborhood chosen by each point. This research addresses this issue and proposes a suitable adaptive neighborhood selection approach for individual points by completely considering the complex and heterogeneous nature of the input LiDAR point cloud data. The proposed approach is evaluated on high-density mobile and low-density aerial LiDAR point cloud datasets using the Random Forest machine learning classifier. In the context of performance evaluation, the proposed approach confirms the competitive performance over the state-of-the-art approaches. The computed accuracy and F1-score for the high-density Toronto and low-density Vaihingen datasets are greater than 91% and 82%, respectively.

Intraoperative mixed-reality spinal neuronavigation system: a novel navigation technique for spinal intradural pathologies.

The objective of this study was to assess the intraoperative accuracy and feasibility of 3D-printed marker-based mixed-reality neurosurgical navigation for spinal intradural pathologies. The authors produced 3D segmentations of spinal intradural tumors with neighboring structures by using combined CT and MRI, and preoperative registration of pathology and markers was successfully performed. A patient-specific, surgeon-facilitated application for mobile devices was built, and a mixed-reality light detection and ranging (LIDAR) camera on a mobile device was employed for cost-effective, high-accuracy spinal neuronavigation. Mobile device LIDAR cameras can successfully overlay images of virtual tumor segmentations according to the position of a 3D-printed marker. The surgeon can visualize and manipulate 3D segmentations of the pathology intraoperatively in 3D. A 3D-printed marker-based mixed-reality spinal neuronavigation technique was performed in spinal intradural pathology procedures and has potential to be clinically feasible and easy to use for surgeons, as well as being time saving, cost-effective, and highly precise for spinal surgical procedures.

Longitudinal Degradation of Pavement Marking Detectability for Mobile LiDAR Sensing Technology in Real-World Use

Recent advancements in vehicle automation and driver-assistance systems that detect pavement markings has increased the importance of the detectability of pavement markings through various sensor modalities across weather and road conditions. Among the sensing techniques, light detection and ranging (LiDAR) sensors have become popular for vehicle-automation applications. This study used low-cost mobile multi-beam LiDAR to assess the performance of several types of pavement marking materials installed on a limited-access highway in various conditions, and quantified the degradation in detection performance over three years. Four marking materials, HPS-8, polyurea, cold plastic, and sprayable thermoplastic, were analyzed in the current study. LiDAR reflectivity data extracted from a total of 210 passes through the test sections were analyzed. A new detectability score based on LiDAR intensity data was proposed to quantify the marking detectability. The results showed that the pavement marking detectability varied across the material types over the years. The results provide guidance for selecting materials and developing maintenance schedules when marking detectability by LiDAR is a concern.

Designing and Building an Intelligent Pavement Management System for Urban Road Networks

Pavement maintenance plays a significant role in megacities. Managing complaints and scheduling road reviews are the two maintenance concerns under the intelligent pavement management system (PMS) plan. In contrast, if the damages are not treated immediately, they will increase over time. By leveraging accurate data from sensors, smart PMS will improve management capability, support sustainability, and drive economic growth in the road network. This research aimed to elaborate on the different modules of an intelligent city pavement network to advance to a sustainable city. First, a 3D mobile light detection and ranging (LiDAR) sensor, accompanied by a camera, was applied as the data collection tool. Although 3D mobile LiDAR data have gained popularity, they lack precise detection of pavement distresses, including cracks. As a result, utilizing RGB imaging may help to detect distresses properly. Two approaches were integrated alongside conducting the data analysis in this paper: (1) ArcGIS pro, developed by Esri Inc., which includes noise removal, digital elevation model (DEM) generation, and pavement and building footprint extraction; (2) the Mechanistic-Empirical Pavement Design Guide (AASHTOWare PMED), which was used to assess site specifications such as traffic, weather, subbase, and current pavement conditions in an effort to design the most appropriate pavement for each road section. For the 3D visualization module, CityEngine (a software from Esri) was used to provide the 3D city model. After implementing the research methodology, we drew the following conclusions: (1) using the AASHTOWare PMED method to make decisions about road maintenance and rehabilitation(M&R) actions can significantly speed up the decision-making process, essentially saving time and money and shortening the project’s duration; and (2) if the road conditions are similar, the smart geographical information system (GIS)-based PMS can make consistent decisions about road M&R strategies, i.e., the interference from human factors is less significant.

From Stationary to Mobile: Unleashing the Full Potential of Terrestrial LiDAR through Sensor Integration

This paper discusses a comprehensive methodology for transforming a static LiDAR (Light Detection and Ranging) system into a mobile mapping system. The initial step involves integrating various sensors, such as GNSS (Global Navigation Satellite System), IMU (Inertial Measurement Unit), and odometry sensors, into the system’s architecture to accurately estimate the position and orientation of the LiDAR sensor at any given time. Specialized algorithms compensate for the vehicle’s motion, and procedural requirements must be followed to ensure safe operation. Hardware integration is critical, requiring proper calibration and validation. Data processing workflows, including algorithms and software tools, play a crucial role in mobile LiDAR mapping systems. The quality and accuracy of the data depend on various factors, and practical considerations such as cost and time are also important. The proposed methodology provides a clear and effective way to transform a terrestrial LiDAR sensor into a mobile mapping system, increasing its flexibility and usability for various applications. However, the detailed analysis, system validation, and data processing details fall outside this paper’s scope and will be discussed in future publications.

Water leakage mapping in concrete railway tunnels using LiDAR generated point clouds

Detection of water leakage areas is one of the most important tasks of regular underground tunnel inspections. Current manual visual assessments are plagued by reduced efficiency and poor accuracy, requiring personnel to physically access tunnels to conduct assessments. This form of monitoring is no longer sustainable and thus a more cost-effective method is required. Light detection and ranging (LiDAR) is one such method and commonly known for its ability to generate high-density 3D coordinated point clouds of scanned environments. In addition to point coordinates an intensity value, highly dependent on surface properties and the backscattered energy of the laser beam, is recorded. In this study properties such as surface colour, roughness and state of saturation are reviewed. Results of systematic tests of different colour and concrete targets, scanned using a mobile LiDAR scanner, are discussed. The aim of the research was to prove the aptitude for remote sensing of water leakage areas in underground tunnels and provide an automated workflow to extract quantitative information of each leak. Field tests demonstrated the ability to rapidly capture, identify, extract and record areas of water leakage based on the intensity and spatial information of tunnel point cloud data. This is particularly useful as water ingress is known to degrade concrete, resulting in the earlier onset of corrosion, spalling and loss of strength. The LiDAR scanner used here proved capable of reducing survey time and provided inspectors with a complete 3D model of the tunnel which was supplemented with quantitative leakage information (location and area).

Use of Mobile Laser Scanning (MLS) to Monitor Vegetation Recovery on Linear Disturbances

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.

Research on Mapping Error Control of Underground Space Mobile LiDAR Constrained by Cooperative Targets

In the underground environment, it is difficult to obtain spatial three-dimensional data because of occlusion and its complexity. Mobile light detection and ranging (LiDAR) measurement technology has the ability to obtain three-dimensional spatial information quickly and accurately, but in the underground environment, because of the lack of global navigation satellite system (GNSS) signal in the integrated navigation system, the measurement accuracy decreases with the increase of time. In this paper, an extended Kalman filter-based loose mode is constructed using the real coordinates of the center of gravity of the target and the measurement information of the laser foothold. It provides an additional space-time reference for the integrated navigation system of the mobile LiDAR system and restricts the errors. The simulation results show that the proposed method can effectively improve the measurement accuracy of mobile LiDAR system and enhance the 3D spatial perception ability of underground space.

Automatic Registration for Panoramic Images and Mobile LiDAR Data Based on Phase Hybrid Geometry Index Features

The registration of panoramic images and mobile light detection and ranging (LiDAR) data is quite challenging because different imaging mechanisms and viewing angle differences generate significant geometric and radiation distortions between the two multimodal data sources. To address this problem, we propose a registration method for panoramic images and mobile LiDAR data based on the hybrid geometric structure index feature of phase. We use the initial GPS/IMU to transform the mobile LiDAR data into an intensity map and align the two images to complete registration. Firstly, a novel feature descriptor called a hybrid geometric structure index of phase (HGIFP) is built to capture the structural information of the images. Then, a set of corresponding feature points is obtained from the two images using the constructed feature descriptor combined with a robust false-match elimination algorithm. The average pixel distance of the corresponding feature points is used as the error function. Finally, in order to complete the accurate registration of the mobile LiDAR data and panoramic images and improve computational efficiency, we propose the assumption of local motion invariance of 3D–2D corresponding feature points and minimize the error function through multiple reprojections to achieve the best registration parameters. The experimental results show that the method in this paper can complete the registration of panoramic images and the mobile LiDAR data under a rotation error within 12° and a translation error within 2 m. After registration, the average error of rotation is about 0.15°, and the average error of translation is about 1.27 cm. Moreover, it achieves a registration accuracy of less than 3 pixels in all cases, which outperforms the current five state-of-the-art methods, demonstrating its superior registration performance.

Street Tree Extraction and Segmentation from Mobile LiDAR Point Clouds Based on Spatial Geometric Features of Object Primitives

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.

Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point Clouds

Adequate water pavement surface drainage on highways is crucial in minimizing the potential of hydroplaning. Highway cross slope has a significant effect of draining water laterally from the pavement surface. Currently, field surveying techniques and other manual measurement methods are used to collect cross slope data on a limited basis in most states, despite these methods being labor intensive and exposing personnel to traffic. Furthermore, field surveying techniques cannot provide continuous data and can only be conducted at sample-based locations. This study conducted a technical evaluation of the effectiveness of airborne LiDAR (light detection and ranging) scanning and mobile terrestrial LiDAR scanning systems in measuring pavement cross slopes. Cross slope data were extracted from the LiDAR point cloud at five selected test sections using two different methods: (i) end-to-end method using elevations only from the pavement edge lines to generate the cross slope; and (ii) 0.2 ft interval point extraction along the cross-section and using a fitted linear regression line as the basis for the cross slope. Cross slopes were also measured at test section locations using conventional surveying methods and compared with LiDAR-extracted cross slopes. Results demonstrate that LiDAR methods are reliable for collecting accurate pavement cross slopes and should be considered for the purpose of cross slope verification on a braod scale such as statewide to address cross slope and pavement surface drainage issues proactively.

LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems.

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

Comparing tree structures derived among airborne, terrestrial and mobile LiDAR systems in urban parks

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

Structure-Aware Subsampling of Tree Point Clouds

Light detection and ranging (LiDAR) technology has revolutionized forest analysis in past two decades. The increase of available LiDAR data volume is accelerating in recent years. However, the dense and large-volume point clouds may constitute challenges for proper data storage and processing. Point subsampling is often a prerequisite in this circumstance. Nonetheless, the commonly used uniform and random subsampling methods fail to preserve the topological details of branching structures, as they essentially drop points globally. This generates problems for studies on detailed branching structures, and currently there are no point subsampling methods designed for trees. In this letter, a structure-aware subsampling (SAS) method is proposed to tackle this issue. SAS relies on skeleton-adaptive clustering to subsample points locally, and maintain the global integrity simultaneously. The proposed method was tested and compared with uniform and random subsampling for retrieving key tree parameters including height, diameter at breast height (DBH), crown area, and wood volume based on geometrical reconstructions. Three datasets from terrestrial, mobile, and unmanned aerial vehicles (UAV) LiDAR platforms were tested. Results showed that SAS was able to achieve similar accuracies of structural parameters compared to the full-resolution data, even with a subsampling rate of over 90%. More importantly, at the same sampling rate, SAS faithfully preserved more points of thin branches compared to uniform and random subsampling. These results imply that the proposed method maintains the complex tree topology while significantly reduces the data size. This study provides a crucial advancement in LiDAR and forest applications, where data reduction still remains widely unexplored.

Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest

Light detection and ranging (LiDAR) has contributed immensely to forest mapping and 3D tree modelling. From the perspective of data acquisition, the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels. This research develops a general framework to integrate ground-based and UAV-LiDAR (ULS) data to better estimate tree parameters based on quantitative structure modelling (QSM). This is accomplished in three sequential steps. First, the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy. Next, redundancy and noise were removed for the ground-based/ULS LiDAR data fusion. Finally, tree modeling and biophysical parameter retrieval were based on QSM. Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest, including poplar and dawn redwood species. Generally, ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data. The fusion-derived tree height, tree volume, and crown volume significantly improved by up to 9.01%, 5.28%, and 18.61%, respectively, in terms of r RMSE. By contrast, the diameter at breast height (DBH) is the parameter that has the least benefits from fusion, and r RMSE remains approximately the same, because stems are already well sampled from ground data. Additionally, particularly for dense forests, the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR. Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests, whereby the improvement owing to fusion is not significant. • Mitigation of the limitations of tree localization in case of ground-based/ULS LiDAR data co-registration using virtual keypoints. • Fusion of ground-based/ULS LiDAR data based on removal of redundant and noisy points. • Investigation and evaluation of the utility of ground-based/ULS LiDAR data fusion for tree parameter estimation. • Ground-based/ULS LiDAR data fusion improved estimation of tree height, tree volume, and crown volume. • Ground-based/ULS LiDAR data fusion has strong potential in QSM of trees particularly in high-stand-density forest.

Urban vegetation segmentation using terrestrial LiDAR point clouds based on point non-local means network

Urban vegetation inventory at city-scale using terrestrial light detection and ranging (LiDAR) point clouds is very challenging due to the large quantity of points, varying local density, and occlusion effects, leading to missing features and incompleteness of data. This paper proposes a novel method, named Point Non-Local Means (PointNLM) network, which incorporates the supervoxel-based and point-wise for automatic semantic segmentation of vegetation from large scale complex scene point clouds. PointNLM captures the long-range relationship between groups of points via a non-local branch cascaded three times to describe sharp geometric features. Simultaneously, a local branch processes the position of scattered feature points and captures the low and high level features. Finally, we proposed a fusion layer of neighborhood max-pooling method to concatenate the long-range features, low level features and high level features for segmenting the trees. The proposed architecture was evaluated on three datasets, including two open access datasets of Semantic3D and Paris-Lille-3D, and an in-house dataset acquired by a commercial mobile LiDAR system. Experimental results indicated that the proposed method provides an efficient and robust result for vegetation segmentation, achieving an Intersection over Union (IoU) of 94.4%, F1-score of 92.7% and overall accuracy of 96.3%, respectively.

High-density mobile LiDAR for measuring urban streetscape features

This study investigates the feasibility of utilizing high-density mobile Light Detection and Ranging (LiDAR) to measure urban streetscape features. The results suggest that mobile LiDAR’s density allows for much smaller voxels than in the previous research and the ability to measure small urban streetscape features in 3D. This includes street trees, light/lampposts, street furniture, traffic and commercial signage, building window proportions, awnings, and enclosed courtyard restaurants. Moreover, mobile LiDAR facilitated measuring and categorizing these streetscape features in walkable, downtown-like streetscape environments. The ability to compartmentalize such streetscapes into smaller cubic foot voxels to be quantitatively measured and categorized could supplement or replace conventional audit-based streetscape measurement. This study introduces new methods—based on voxel data analysis—to compile accurate descriptive statistics of streetscape features and how they can be represented in 3D.