Hand-held Laser Scanner Research Articles

Geospatial aspects of building information modelling (BIM) based utilities management

The concept of Building Information Modelling (BIM) which was originally used for construction design has developed with increasing BIM dimensions from 3D, 4D, 5D, 6D, 7D to 10D and with the addition of semantics to 3D objects so that BIM allows it to be developed in broader management such as utility and assets management. In a utility management system, object data acquisition process will face some problem in varying object’s dimensions and location require the merging of several 3D data which is collected from different equipment with different accuracy and precision which for this research using the High Definition Surveying (HDS) method from Terrestrial Laser Scanning (TLS) and Handheld Laser Scanning (HLS) instruments. The problem that arises is in point cloud data merging process with different point densities, in this paper merging data will use Helmert 3D transformation method. Another problem is in the objects georeferencing into the Earth coordinate system. A georeferenced system is needed to integrate all of utility object into a utility management system in a building. This process is necessary if the development of utility database management will be linked with the creation of 3D models in the bigger scopes such as smart cities or narrower scopes such as smart buildings. The end result is a georeferenced BIM-based utility database management system, which allows for spatial analysis or automation using coordinate systems embedded in 3D objects.

Coarse-to-fine segmentation of individual street trees from side-view point clouds

Segmenting individual street trees from a street side-view point cloud is the first and key step of obtaining a street tree inventory. Using the classification-segmentation framework for individual tree segmentation makes tree detection simple and accurate, but segmenting overlapping trees is still challenging. To more accurately segment overlapping trees, a coarse-to-fine method for segmenting individual street trees from a side-view point cloud is proposed in this paper. Following the classification-segmentation framework, the tree points are first detected from the side-view street point cloud by a pointwise classifier fused from 13 local geometric features and then trained using random forest (RF). Second, the tree proposals are obtained by density-based spatial clustering of applications with noise (DBSCAN) clustering and detection error filtering. Third, the overlapping tree proposals are recognized by trunk identification, and the single tree proposals are directly output as individual trees. Fourth, the overlapping trees are roughly divided into individual tree proposals through vertical planes. Finally, individual trees with optimized contours are obtained by iteratively using DBSCAN clustering and k-nearest neighbor (k-NN) classification. The side-view point cloud of a 290 m-long urban street containing 77 street trees is captured by a hand-held mobile ZEB Horizon laser scanner. The tree detection attained an F1 score of 0.9916 with a precision of 0.9989 and a recall of 0.9864. For individual tree segmentation, the F1 score was 0.9745 with a precision of 0.9672 and a recall of 0.9819. Compared to two current classification-segmentation methods, the overlapping tree segmentation F1 scores were increased by 0.0914 and 0.0617, respectively. The proposed method can be applied to tree parameter extraction, which is an important urban forest inventory task and is crucial for urban forest management. In our experiment, the root mean squared error (RMSE) of the trunk diameter at breast height (DBH) estimation was 0.8485 cm.

Can a Hand-Held 3D Scanner Capture Temperature-Induced Strain of Mortar Samples? Comparison between Experimental Measurements and Numerical Simulations

The expected lifespan of cement-based materials, particularly concrete, is at least 50 years. Changes in the pore structure of the material need to be considered due to external influences and associated transport processes. The expansion behaviour of concrete and mortar during freeze–thaw attacks, combined with de-icing salt agents, is crucial for both internal and external damage. It is essential to determine and simulate the expansion behaviour of these materials in the laboratory, as well as detect the slow, long-term expansion in real structures. This study measures the expansion of mortar samples during freeze–thaw loading using a high-resolution hand-held 3D laser scanner. The specimens are prepared with fully or partially saturated pore structures through water storage or drying. During freeze–thaw experiments, the specimens are exposed to pure water or a 3% sodium chloride solution (NaCl). Results show contraction during freezing and subsequent expansion during thawing. Both test solutions exhibit similar expansion behaviour, with differences primarily due to saturation levels. Further investigations are required to explore the changing expansion behaviour caused by increasing microcracking resulting from continuous freeze–thaw cycles. A numerical analysis using a 3D coupled hygro-thermo-mechanical (HTM) model is conducted to examine the freeze–thaw behaviour of the mortar. The model accurately represents the freezing deformation during the freeze–thaw cycle.

Branch information extraction from Norway spruce using handheld laser scanning point clouds in Nordic forests

We showed that a mobile handheld laser scanner (HHLS) provides useful features concerning the wood quality-influencing external structures of trees. When linked with wood properties measured at a sawmill utilizing state-of-the-art X-ray scanners, these data enable the training of various wood quality models for use in targeting and planning future wood procurement. A total of 457 Norway spruce sample trees (Picea abies (L.) H. Karst.) from 13 spruce-dominated stands in southeastern Finland were used in the study. All test sites were recorded with a ZEB Horizon HHLS, and the sample trees were tracked to a sawmill and subjected to X-rays. Two branch extraction techniques were applied to the HHLS point clouds: 1) a method developed in this study that was based on the density-based spatial clustering of applications with noise (DBSCAN) and 2) segmentation-based quantitative structure model (treeQSM). On average, the treeQSM method detected 46% more branches per tree than the DBSCAN did. However, compared with the X-rayed references, some of the branches detected by the treeQSM may either be false positives or so small in size that the X-rays are unable to detect them as knots, as the method overestimated the whorl count by 19% when compared with the X-rays. On the other hand, the DBSCAN method only detected larger branches and showed a −11% bias in whorl count. Overall, the DBSCAN underestimated knot volumes within trees by 6%, while the treeQSM overestimated them by 25%. When we input the HHLS features into a Random Forest model, the knottiness variables measured at the sawmill were predicted with R2s of 0.47–0.64. The results were comparable with previous results derived with the static terrestrial laser scanners. The obtained stem branching data are relevant for predicting wood quality attributes but do not provide data that are directly comparable with the X-ray features. Future work should combine terrestrial point clouds with dense above-canopy point clouds to overcome the limitations related to vertical coverage.

RESEARCH ON THE 3D DOCUMENTATION SYSTEM OF CLASSICAL CHINESE GARDENS IN SCENIC AREAS – TAKING KUNSHAN SUIYUAN AS AN EXAMPLE

Abstract. Classical Chinese gardens built in scenic areas have characteristics in rich scales, multi-type elements, and a complex development history. Due to the designed landscape characteristics of "hidden the garden in bigger landscapes", it is often difficult to obtain high-quality heritage information through traditional surveying approaches. The aim of this research is to establish a digital 3D documentation technology system for the classical gardens in scenic areas. Suiyuan Garden, a classical Chinese garden with a history of more than 300 years, was used as a case study. We have integrated UAV low-altitude photogrammetry, handheld laser scanning, ground-mounted laser scanning, macro structured light scanning and other technologies to obtain the 3D data, and to build a digital twin by developing the technical workflow and data standard. The innovation of the research lies in: 1) We used a variety of digital mapping methods to record the pattern between the garden and the surrounding landscape; 2) We adopted a combination of handheld and ground-mounted laser scanning systems to build spatial structures and better cover the ground spaces; 3) We established a workflow for the conversion from point cloud to mesh model for different garden features, which can improve the usability of 3D heritage information; 4) We used the 3D cultural heritage modelling approach to improve the standards of the digital conservation of historic plants. The outcomes of this research could be used as the basis for the construction of Historic Landscape Information Model (HLIM) in the near future.

ANALYSIS OF POSSIBLE GEODETIC APPROACHES FOR 3D MODEL CREATION IN BIM

Abstract. The topic of this paper is the description and analysis of technologies for generating a geometric basis for BIM. The main element is an analysis of the capabilities of mobile laser scanning and the outputs of this method compared to other methods. The selected interior space was measured by different technologies, laser scanning, classical geodetic using total station, and manual measurement. The main device tested was the ZEB-REVO. It is a handheld mobile laser scanner, carried by an operator. It has a range of 40 m, with the accuracy in measured lengths is 1–3cm per 10 m depending on the type of surface. The scanning speed is 43,000 points per second. The device is equipped with an inertial measurement unit (IMU) and uses SLAM technology for trajectory definition. The device does not have a camera in the simplest configuration and the operator cannot monitor the scanning status. Nevertheless, measurement with this scanner is very simple. The instrument is mainly used for surveying construction objects, but it can be advantageously used for documentation of underground spaces or in e.g., forestry for defining DBH or volume of stockpiles. This device is not equipped with a GNSS unit, and the typical scanning time is 20–30 min without model deformation. Accuracy and output analysis was performed for all technologies used. The results are presented in tables and show the suitability of PLS (personal laser scanner) in general.

Exploring Influences of Leaves on Urban Species Identification Using Handheld Laser Scanning Point Cloud: A Case Study in Hong Kong

Urban tree species identification performs a significant role in tree management and the sustainable development of cities. Conventionally, multispectral or hyperspectral remote sensing images are applied to identify species. However, spectral profiles of trees on images are easily affected by surroundings and illuminations, resulting in urban trees of different species possibly having similar spectral features. The handheld laser scanning (HLS) technique can capture 3D structural information of trees and be confirmed to be effective in reducing the problem of spectral similarity through tree structural properties (TSP). TSP usually varies in different leaf conditions, especially TSP of tropical tree species. In this study, we investigated the effects of leaves on urban tropical tree species identification using HLS. A total of 89 metrics that characterized the TSP were evaluated, including 19 branches, 12 stems, 45 crowns, and 13 entire tree metrics. All metrics were derived under different leaf conditions. The correlation and importance of these metrics were further evaluated. Our results demonstrated that crown metrics perform the most important role in urban species identification in leaf-on and leaf-off conditions and that the combination of metrics derived in different leaf conditions can improve the identification accuracy. Furthermore, we discovered 9 robust metrics that perform well in all leaf conditions, including 3 crowns, 2 branches, 2 stems, and 2 entire tree metrics. These metrics give a deep understanding of numerous structural properties and provide a significant reference for the relevant structure-based classification of other tropical species. This study also illustrated that HLS could help to overcome the spectrum-related limitations and improve the efficiency of species identification and sustainable forest management.

A novel geometric feature-based wood-leaf separation method for large and crown-heavy tropical trees using handheld laser scanning point cloud

ABSTRACT As the prerequisite of non-destructively measuring structural parameters and leaf distribution, wood-leaf separation plays an essential role in forest inventories. However, fewer studies focus on the separation and measurement of large tropical trees with heavy crowns and complex branch structures. This study proposed a novel method to automatically separate the leaf and wood points of large tropical trees based on geometric features. Instead of identifying all wood points using the same rules, we used different methods to separate small and large wood components, respectively. The identification of small wood components was implemented mainly by the differences in point density and linear distribution pattern between leaf and wood points, while the identification of large wood components was implemented through the comprehensive analysis of verticality, linearity, anisotropy, as well as point density. To improve the separation accuracy and implementation effectiveness, the segment-wise and point-wise methods were combined in this study. The robustness and generalization of the proposed method were tested using two datasets, i.e. twenty large tropical trees with heavy crowns and twenty-four general tropical trees without heavy crowns. The separation results verified that the proposed method could achieve good separation of wood and leaf points of large crown-heavy tropical trees with the accuracy of up to 91.5%. The highest separation accuracy of general tropical trees was about 95.03%. The examination of the tropical trees without heavy crowns demonstrated that the proposed method has promising robustness and generalization ability. In addition, to fill the gap in the large tropical tree point clouds, an open-source dataset library was built for the wood-leaf separation research, including manually labelled 20 large crown-heavy tropical trees with different types of branch structures and basic structural parameters of each tree (tree height, crown spread, and diameter at the breast height (DBH)).

The Effect of The Number of Reference Points and Distribution on Coordinate Transformation in Underground Mining Measurements

This article focuses on examining the effect of the number and distribution of reference points on coordinate transformations in underground mining measurements using a handheld laser scanner. The points inside the mine were measured as reference using a Total Station. The same point cloud data was subjected to coordinate transformation with different numbers and elevations of reference points. It was observed that the homogeneous distribution of reference points used in the transformation increases precision in the horizontal and vertical directions. Both homogeneous and excessive use of transformation points result in the same good results in the horizontal, but in the vertical, it improves further. Using a large number of transformation points and using them at different elevations results in the same good results in the horizontal, but in the vertical, it increases precision even more. The article concludes that a sufficient number of homogeneously and heterogeneously distributed reference points are necessary for accurate coordinate transformation.

Assessing the Performance of a Handheld Laser Scanning System for Individual Tree Mapping—A Mixed Forests Showcase in Spain

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser Scanning (HLS) system to estimate tree attributes within a forest patch in central Spain. We describe the different stages of the HLS approach: field position, ground data collection, scanning path design, point cloud processing, alignment between detected trees and measured reference trees, and finally, the assessment of main tree structural attributes diameter at breast height (DBH) and tree height considering species and tree size as control factors. We surveyed 418 reference trees to account for omission and commission error rates over a 1 ha plot divided into 16 sections and scanned using two different scanning paths. The HLS-based approach reached a high of 88 and 92% tree detection rate for the best combination of scanning path and point cloud processing modes for the HLS system. The root mean squared errors for DBH estimates varied between species: errors for Pinus pinaster were below 2 cm for Scan 02. Quercus pyrenaica, and Alnus glutinosa showed higher error rates. We observed good agreement between ALS and HLS estimates for tree height, highlighting differences to field measurements. Despite the complexity of the mixed forest area surveyed, our results show that HLS is highly efficient at detecting tree locations, estimating DBH, and supporting tree height measurements as confirmed with airborne laser data used for validation. This study is one of the first HLS-based studies conducted in the Mediterranean mixed forest region, where variability in tree allometries and spacing and the presence of natural regeneration pose challenges for the HLS approach. HLS is a feasible, time-efficient, scalable technology for tree mapping in mixed forests with potential to support forest monitoring programmes such as national forest inventories lacking three-dimensional, remote sensing data to support field measurements.

Changes in soil surface properties under simulated rainfall and the effect of surface roughness on runoff, infiltration and soil loss

Soil erosion by water is a result of detachment of particles or small aggregates from the soil surface followed by transport of the detached material. One of the elements that affects surface runoff and soil erosion is the soil surface roughness (SSR). Prior research reports that increasing SSR reduces generation of runoff and soil loss. In addition to that, it is widely reported that across-slope oriented roughness is better at controlling soil and water losses. However, to date there have been few studies into the effect of both magnitude and orientation of SSR on runoff, infiltration and soil erosion at the sub process level (i.e. by raindrop splash and overland flow), occurring simultaneously. In this study, the effects of up-down-slope oriented SSR (Treatment A), across-slope oriented SSR (Treatment B) and random SSR (Treatment C) were compared, along with a smooth surface (Treatment D). A moderate slope gradient of 10 %, a simulated rainfall intensity of 90 mm hr−1 and storm durations of 15 or 30 min were considered. The SSR was measured using the chain method, before and after the rainfall event. Images of the soil surface were taken using a hand-held laser scanner to monitor the effect of rainfall on the surface morphology. The outcome of this study shows that rainfall erosivity increases the SSR of the initially smooth surface, but decreases that of the initially rough surface, particularly in the random SSR treatment, where the decrease in SSR was 64 % of the pre-rainfall condition. This was due to the effects of raindrop impacts and overland flow. The random SSR treatment generated significantly more runoff and soil loss, and less infiltration than all other treatments (p < 0.001), but for raindrop splash erosion, there was no significant difference between random SSR and the other treatments. Contrary to expectations, the across-slope oriented SSR did not always reduce runoff and soil erosion compared to the up-down-slope orientation. This can be explained by degradation of surface microtopography by rainfall and runoff, as confirmed by the post-rainfall SSR measurements.

Three-dimensional modeling of structures using a hand-held laser scanner (SLAM)

three-dimensional modeling of structures using a hand-held laser scanner (SLAM)

Application of Handheld Laser Scanner with SLAM Technology for Digital Archiving of Historical Cultural Assets

Application of Handheld Laser Scanner with SLAM Technology for Digital Archiving of Historical Cultural Assets

R-ProjNet: an optimal rotated-projection neural network for wood segmentation from point clouds

ABSTRACT This work aims to provide a deep learning framework to segment woods from tree point clouds. We develop a novel preprocessing layer before the classical sampling and convolution structure called the projection layer to organize 3D point clouds into 2D points. Input data are transformed into projection data along axis and planes for the subsequent convolution process, which helps decrease the complexity of networks. In order to obtain optimal and effective projection data for capturing local features, we formulate the 2D transformation in the learning process using two learnable angle parameters. The projection map is updated in the learning process for capturing geometric structure information, which plays an important role in wood point segmentation. Experiments show that we have achieved the loss and misclassification error of 0.41% and 8%, respectively, on wood points extraction from handheld laser scanning data. Besides, we also achieve the correctness, completeness and -score of 90.4%, 91.5% and 0.91, respectively, in a public vehicle laser scanning dataset.

PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage

Segmentation of plant point clouds to obtain high-precise morphological traits is essential for plant phenotyping. Although the fast development of deep learning has boosted much research on segmentation of plant point clouds, previous studies mainly focus on the hard voxelization-based or down-sampling-based methods, which are limited to segmenting simple plant organs. Segmentation of complex plant point clouds with a high spatial resolution still remains challenging. In this study, we proposed a deep learning network plant segmentation transformer (PST) to achieve the semantic segmentation of rapeseed plants point clouds acquired by handheld laser scanning (HLS) with the high spatial resolution, which can characterize the tiny siliques as the main traits targeted. PST is composed of: (i) a dynamic voxel feature encoder (DVFE) to aggregate the point features with the raw spatial resolution; (ii) the dual window sets attention blocks to capture the contextual information; and (iii) a dense feature propagation module to obtain the final dense point feature map. We then integrated PST with an instance segmentation head in the point grouping network (PointGroup) and developed PST-PointGroup (PG) to achieve the instance segmentation of the siliques. The results proved that PST and PST-PG achieved superior performance in semantic and instance segmentation tasks. For the semantic segmentation, the mean IoU, mean Precision, mean Recall, mean F1-score, and overall accuracy of PST were 93.96%, 97.29%, 96.52%, 96.88%, and 97.07%, achieving an improvement of 7.62, 3.28, 4.8, 4.25, and 3.88 percentage points compared to the second-best state-of-the-art network position adaptive convolution (PAConv). For instance segmentation, PST-PG reached 89.51%, 89.85%, 88.83% and 82.53% in mCov, mWCov, mPerc90, and mRec90, achieving an improvement of 2.93, 2.21, 1.99, and 5.9 percentage points compared to the original instance segmentation network PointGroup. This study extends the phenotyping of rapeseed plants in an end-to-end way and proves that the deep-learning-based point cloud segmentation method has a great potential for resolving dense plant point clouds with complex morphological traits.

Feature Analysis of Scanning Point Cloud of Structure and Research on Hole Repair Technology Considering Space-Ground Multi-Source 3D Data Acquisition.

As one of the best means of obtaining the geometry information of special shaped structures, point cloud data acquisition can be achieved by laser scanning or photogrammetry. However, there are some differences in the quantity, quality, and information type of point clouds obtained by different methods when collecting point clouds of the same structure, due to differences in sensor mechanisms and collection paths. Thus, this study aimed to combine the complementary advantages of multi-source point cloud data and provide the high-quality basic data required for structure measurement and modeling. Specifically, low-altitude photogrammetry technologies such as hand-held laser scanners (HLS), terrestrial laser scanners (TLS), and unmanned aerial systems (UAS) were adopted to collect point cloud data of the same special-shaped structure in different paths. The advantages and disadvantages of different point cloud acquisition methods of special-shaped structures were analyzed from the perspective of the point cloud acquisition mechanism of different sensors, point cloud data integrity, and single-point geometric characteristics of the point cloud. Additionally, a point cloud void repair technology based on the TLS point cloud was proposed according to the analysis results. Under the premise of unifying the spatial position relationship of the three point clouds, the M3C2 distance algorithm was performed to extract the point clouds with significant spatial position differences in the same area of the structure from the three point clouds. Meanwhile, the single-point geometric feature differences of the multi-source point cloud in the area with the same neighborhood radius was calculated. With the kernel density distribution of the feature difference, the feature points filtered from the HLS point cloud and the TLS point cloud were fused to enrich the number of feature points in the TLS point cloud. In addition, the TLS point cloud voids were located by raster projection, and the point clouds within the void range were extracted, or the closest points were retrieved from the other two heterologous point clouds, to repair the top surface and façade voids of the TLS point cloud. Finally, high-quality basic point cloud data of the special-shaped structure were generated.

Cave feature extraction and classification from rockery point clouds acquired with handheld laser scanners

Rockeries are unique elements of Chinese classical gardens with historical, cultural, artistic, and scientific value. One of the essential characteristics of garden rockery is the cave features since cave morphological features determine the degree of “Tou” (riddled through) and “Lou” (hollowed out well) features as the defined by rockery appreciation theory, which is important for rockery heritage conservation, assessment, and management. However, in heritage studies, accurately identifying and evaluating rockery caves is a difficult task because of a cave's irregular shape. This paper proposes a methodology to extract and classify cave features by point clouds obtained from data using a handheld laser scanner with a camera. Without completing surface reconstruction, the rockery point cloud is first sliced into chips, then cave chips were extracted from these approximately two-dimensional chips and next merged to obtain three-dimensional cave point clouds. Finally, the cave boundary points are extracted from the cave and fitted by an ellipse for classification. To extract and classify cave features, a methodology to improve rockery digitalization quality is also proposed. The raw point cloud data were preprocessed by pose adjustment, noise removal, and hole repair. The experimental results for the two rockeries in Tongji University and Qiuxiapu Garden indicate that the improved digitization scheme generates complete and closed rockery point clouds, all types of caves were effectively extracted and classified by our proposed method. Additionally, the extracted caves are still represented by point clouds, which suggest the possibility for other research in the future.

Forest Road Wearing Course Damage Assessment Possibilities with Different Types of Laser Scanning Methods including New iPhone LiDAR Scanning Apps

Forests make up 34.1% of the Czech Republic total area and forest roads account for nearly the same length (47,465 km) as all other roads administered by the state and its regions (55,738 km). Forest roads are not as intensively used as other roads. On the other hand, as logging trucks carry the maximum permitted load on roads and forests create a specific microclimate, forest roads are subject to rapid wear. A road wearing course is generally designed for 20 years of service and for a maximum damage level of 25% before they are supposed to be reconstructed. To ensure this life cycle is adhered to, more efficient, faster, and more flexible surface damage detection adaptable for forest environment is needed. As smartphones and their optical devices, i.e., new iPhones with LiDAR sensors, become more advanced, the option arises to perform laser scanning on road surfaces using smartphones applications. This work aimed to test this technology and its precision applicability to assessing damage to a forest wearing course and compare it with another hand-held personal laser scanner (PLShh), represented in this study by GeoSLAM ZEB Horizon scanner, and more precise terrestrial laser scanning (TLS) technology, represented in this study by Faro Focus 3D laser scanner, which have started to replace tacheometric wearing course damage surveying thanks to their greater precision. So, this study involved a comparison of three alternative laser scanning methods focused especially on these, which are implemented in new iPhones for tacheometric surveying. First, a Faro Focus 3D laser scanner was used for the TLS method. Second, the PLShh method was tested on a GeoSLAM ZEB Horizon scanner. Third, another PLShh method using an iPhone 13 Pro with applications 3D Scanner and Polycam was evaluated. If we are comparing positional height accuracy of PLShh to tacheometric surveying on reference cross position height coordinates, ZEB Horizon achieved devXY and devZ RMSE 0.108 m; 0.025 m; iPhone 13 Pro with 3D Scanner app devXY and devZ RMSE 0.185 m; 0.021 m, and with Polycam app devXY and devZ RMSE 0.31 m; 0.045. TLS achieved the best results with devXY RMSE 0.049 and devZ RMSE 0.0077. The results confirm that only the TLS scanner achieves precision values in height differences applicable for an assessment of forest road wearing course damage measurement comparable with tacheometric surveying. Surprisingly, comparing the PLShh scanners to the TLS technology, they achieved interesting results, comparing their transverse profiles and 3D objects as digital surface models (DSM) of the road to TLS in height position. In transverse profiles, ZEB Horizon achieved devZ RMSE 0.032 m; iPhone 13 Pro with 3D Scanner app devZ RMSE 0.017 m, and with Polycam app devZ RMSE 0.041 m compared to the TLS method measured using a Faro Focus 3D static laser scanner. Comparing forest road DSM to Faro Focus 3D, ZEB Horizon achieved devZ RMSE 0.028 m; iPhone 13 Pro with 3D Scanner app devZ RMSE 0.018 m and with Polycam devZ RMSE 0.041 m. These results in height differences show that the height accuracy of PLShh achieves precision, which is applicable to determining the current shape of forest road wearing course compared to the required roof shape gradient. However, further testing provided the insight that such a kind of PLShh measurement is still only possible to use for the identification of a transverse profile shape, as in length measurement the length error increases. All PLShh are able to capture the current shape of forest road cross profile, but still they cannot be used for any design or calculation of material measurement needed for wearing course repair.

Experimental study of the shear strength criterion of rock structural plane based on three-dimensional surface description

Abstract The failure of rock mass is mainly due to the failure of the structural plane, which is an important factor to reduce the mechanical properties and stability of rock mass. The shear strength of rock mass is one of the parameters for the stability calculation of large-scale rock mass engineering. The shear strength of a rock structural plane is strongly influenced by surface morphology. Considerable research has been conducted regarding the correlation between two-dimensional structural plane morphology and shear strength. However, quantitative research on three-dimensional (3D) morphology is relatively limited. In this study, 3D printing technology was used to create molds. Using cement and sand as the main materials, additives such as early strength and water-reducing agents were added, and test samples of irregular surface topography were created. The 3D roughness was quantified by formula calculation. Using a ZScanner® 800 hand-held 3D laser scanner to perform scanning on the structural surface, the parameter curve was analysed by generating 3D coordinate information and a 3D image of the fracture surface, and the quantitative parameter M p 3 D {M}_{\text{p}}^{3\text{D}} describing the 3D morphology of the structural surface was constructed. The change rule of R p 3 D {R}_{\text{p}}^{3\text{D}} and joint roughness coefficient (JRC) were analysed under different scanning resolutions, Δ(r), the scanning precision was suggested, and the functional relationship between JRC and M p 3 D {M}_{\text{p}}^{3\text{D}} was established. Finally, a formula for shear strength parameters considering the 3D characteristics of a structural plane surface was established. The model validation results show that the experimental data were within the 95% confidence band of the model curve, the average error of the shear strength was 10.4%, the errors of friction angle and cohesion, C, were 3.4 and 9.4%, and the reliability was fine.

Analysis Model of the Relationship between Public Spatial Forms in Traditional Villages and Scenic Beauty Preference Based on LiDAR Point Cloud Data

Traditional villages are historically, culturally, scientifically and aesthetically valuable, and a beautiful landscape is the primary embodiment of a traditional village environment. Urbanization and modernization have had a great impact on village landscapes. As an important aspect of traditional village landscapes, creating beautiful public spaces is an effective way to attract tourists and improve the well-being of residents. Landscape aesthetic activities are the result of the interaction between landscape objects and aesthetic subjects. Research on the relationship between the form of traditional village public spaces and subjective aesthetic preferences has long been neglected. This research examined 31 public spaces in traditional villages in the Dongshan and Xishan areas in Lake Taihu, Suzhou. An index system of public spatial forms in traditional villages was created, basic data of spatial forms were collected using a hand-held 3D laser scanner, and the value of the spatial forms index was calculated using R language. The scenic beauty estimation (SBE) method was improved, with the estimation of the beauty of the scenic environment based on VR panorama rather than traditional photo media. Parameter screening was performed using correlation analysis and full subset regression analysis, and four models were used to fit the SBE scores and grades. The results show that the majority of public spaces had lower than average SBE scores, and the four key indicators of average contour upper height, solid-space ratio, vegetation cover, and comprehensive closure predicted SBE. In addition, the linear model (R2 = 0.332, RMSE = 64.774) had the most accurate SBE level prediction and the stochastic forest model (R2 = 0.405, RMSE = 63.311) was better at predicting specific SBE scores. The model provides managers, designers, and researchers with a method for the quantitative evaluation of visual landscape preferences and quantitative landscape spatial forms and provides a reference for the protection and renewal of traditional village landscapes.