scholarly journals General External Uncertainty Models of Three-Plane Intersection Point for 3D Absolute Accuracy Assessment of Lidar Point Cloud

2019 ◽  
Vol 11 (23) ◽  
pp. 2737 ◽  
Author(s):  
Minsu Kim ◽  
Seonkyung Park ◽  
Jeffrey Danielson ◽  
Jeffrey Irwin ◽  
Gregory Stensaas ◽  
...  
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Conjugate Point ◽  
Airborne Lidar ◽  
Lidar Data ◽  
General Function ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Uncertainty Model ◽  
External Uncertainty

The traditional practice to assess accuracy in lidar data involves calculating RMSEz (root mean square error of the vertical component). Accuracy assessment of lidar point clouds in full 3D (three dimension) is not routinely performed. The main challenge in assessing accuracy in full 3D is how to identify a conjugate point of a ground-surveyed checkpoint in the lidar point cloud with the smallest possible uncertainty value. Relatively coarse point-spacing in airborne lidar data makes it challenging to determine a conjugate point accurately. As a result, a substantial unwanted error is added to the inherent positional uncertainty of the lidar data. Unless we keep this additional error small enough, the 3D accuracy assessment result will not properly represent the inherent uncertainty. We call this added error “external uncertainty,” which is associated with conjugate point identification. This research developed a general external uncertainty model using three-plane intersections and accounts for several factors (sensor precision, feature dimension, and point density). This method can be used for lidar point cloud data from a wide range of sensor qualities, point densities, and sizes of the features of interest. The external uncertainty model was derived as a semi-analytical function that takes the number of points on a plane as an input. It is a normalized general function that can be scaled by smooth surface precision (SSP) of a lidar system. This general uncertainty model provides a quantitative guideline on the required conditions for the conjugate point based on the geometric features. Applications of the external uncertainty model were demonstrated using various lidar point cloud data from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) library to determine the valid conditions for a conjugate point from three-plane modeling.

scholarly journals CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS

2016 ◽  
Vol XLI-B3 ◽  
pp. 441-445 ◽  
Author(s):  
N. Yastikli ◽  
Z. Cetin
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Classification Methods ◽  
Data Set ◽  
Cloud Data ◽  
Rule Sets

LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features) and have been tested in the study area in Zekeriyaköy, Istanbul which includes the partly open areas, forest areas and many types of the buildings. The data set used in this research obtained from Istanbul Metropolitan Municipality which was collected with ‘Riegl LSM-Q680i’ full-waveform laser scanner with the density of 16 points/m2. The proposed automatic point based Approach 1 and Approach 2 classifications successfully produced the ground, building and vegetation classes which were very similar although different features were used.

scholarly journals CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS

2016 ◽  
Vol XLI-B3 ◽  
pp. 441-445
Author(s):  
N. Yastikli ◽  
Z. Cetin
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Classification Methods ◽  
Data Set ◽  
Cloud Data ◽  
Rule Sets

LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features) and have been tested in the study area in Zekeriyaköy, Istanbul which includes the partly open areas, forest areas and many types of the buildings. The data set used in this research obtained from Istanbul Metropolitan Municipality which was collected with ‘Riegl LSM-Q680i’ full-waveform laser scanner with the density of 16 points/m2. The proposed automatic point based Approach 1 and Approach 2 classifications successfully produced the ground, building and vegetation classes which were very similar although different features were used.

scholarly journals From Point Cloud Data to Building Information Modelling: An Automatic Parametric Workflow for Heritage

2020 ◽  
Vol 12 (7) ◽  
pp. 1094 ◽  
Author(s):  
Mesrop Andriasyan ◽  
Juan Moyano ◽  
Juan Enrique Nieto-Julián ◽  
Daniel Antón
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Accuracy Assessment ◽  
Parametric Modelling ◽  
Point Cloud Data ◽  
Building Information Modelling ◽  
Historical Interpretation ◽  
Information Modelling ◽  
Cloud Data ◽  
Building Information

Building Information Modelling (BIM) is a globally adapted methodology by government organisations and builders who conceive the integration of the organisation, planning, development and the digital construction model into a single project. In the case of a heritage building, the Historic Building Information Modelling (HBIM) approach is able to cover the comprehensive restoration of the building. In contrast to BIM applied to new buildings, HBIM can address different models which represent either periods of historical interpretation, restoration phases or records of heritage assets over time. Great efforts are currently being made to automatically reconstitute the geometry of cultural heritage elements from data acquisition techniques such as Terrestrial Laser Scanning (TLS) or Structure From Motion (SfM) into BIM (Scan-to-BIM). Hence, this work advances on the parametric modelling from remote sensing point cloud data, which is carried out under the Rhino+Grasshopper-ArchiCAD combination. This workflow enables the automatic conversion of TLS and SFM point cloud data into textured 3D meshes and thus BIM objects to be included in the HBIM project. The accuracy assessment of this workflow yields a standard deviation value of 68.28 pixels, which is lower than other author’s precision but suffices for the automatic HBIM of the case study in this research.

scholarly journals AUTOMATIC EXTRACTION AND TOPOLOGY RECONSTRUCTION OF URBAN VIADUCTS FROM LIDAR DATA

2015 ◽  
Vol XL-3/W3 ◽  
pp. 131-135
Author(s):  
Y. Wang ◽  
X. Hu
Keyword(s):  
Point Cloud ◽  
Original Method ◽  
Airborne Lidar ◽  
Least Square ◽  
Automatic Extraction ◽  
Point Cloud Data ◽  
Topological Graph ◽  
Cloud Data ◽  
City Model ◽  
Region Growth

Urban viaducts are important infrastructures for the transportation system of a city. In this paper, an original method is proposed to automatically extract urban viaducts and reconstruct topology of the viaduct network just with airborne LiDAR point cloud data. It will greatly simplify the effort-taking procedure of viaducts extraction and reconstruction. In our method, the point cloud first is filtered to divide all the points into ground points and none-ground points. Region growth algorithm is adopted to find the viaduct points from the none-ground points by the features generated from its general prescriptive designation rules. Then, the viaduct points are projected into 2D images to extract the centerline of every viaduct and generate cubic functions to represent passages of viaducts by least square fitting, with which the topology of the viaduct network can be rebuilt by combining the height information. Finally, a topological graph of the viaducts network is produced. The full-automatic method can potentially benefit the application of urban navigation and city model reconstruction.

scholarly journals VOXEL-BASED EXTRACTION OF INDIVIDUAL PYLONS AND WIRES FROM LIDAR POINT CLOUD DATA

2019 ◽  
Vol IV-4/W8 ◽  
pp. 91-98 ◽  
Author(s):  
N. Munir ◽  
M. Awrangjeb ◽  
B. Stantic ◽  
G. Lu ◽  
S. Islam
Keyword(s):  
Vertical Profile ◽  
Point Cloud ◽  
Support Vector ◽  
Svm Classifier ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Objects ◽  
Voxel Grid ◽  
Power Line Corridor

<p><strong>Abstract.</strong> Extraction of individual pylons and wires is important for modelling of 3D objects in a power line corridor (PLC) map. However, the existing methods mostly classify points into distinct classes like pylons and wires, but hardly into individual pylons or wires. The proposed method extracts standalone pylons, vegetation and wires from LiDAR data. The extraction of individual objects is needed for a detailed PLC mapping. The proposed approach starts off with the separation of ground and non ground points. The non-ground points are then classified into vertical (e.g., pylons and vegetation) and non-vertical (e.g., wires) object points using the vertical profile feature (VPF) through the binary support vector machine (SVM) classifier. Individual pylons and vegetation are then separated using their shape and area properties. The locations of pylons are further used to extract the span points between two successive pylons. Finally, span points are voxelised and alignment properties of wires in the voxel grid is used to extract individual wires points. The results are evaluated on dataset which has multiple spans with bundled wires in each span. The evaluation results show that the proposed method and features are very effective for extraction of individual wires, pylons and vegetation with 99% correctness and 98% completeness.</p>

scholarly journals A Workflow to Extract the Geometry and Type of Vegetated Landscape Elements from Airborne LiDAR Point Clouds

2021 ◽  
Vol 13 (20) ◽  
pp. 4031
Author(s):  
Ine Rosier ◽  
Jan Diels ◽  
Ben Somers ◽  
Jos Van Orshoven
Keyword(s):  
River Discharge ◽  
Point Cloud ◽  
Point Clouds ◽  
Flood Frequency ◽  
Airborne Lidar ◽  
Main Function ◽  
Point Cloud Data ◽  
Landscape Elements ◽  
Cloud Data ◽  
Landscape Element

Rural European landscapes are characterized by a variety of vegetated landscape elements. Although it is often not their main function, they have the potential to affect river discharge and the frequency, extent, depth and duration of floods downstream by creating both hydrological discontinuities and connections across the landscape. Information about the extent to which individual landscape elements and their spatial location affect peak river discharge and flood frequency and severity in agricultural catchments under specific meteorological conditions is limited. This knowledge gap can partly be explained by the lack of exhaustive inventories of the presence, geometry, and hydrological traits of vegetated landscape elements (vLEs), which in turn is due to the lack of appropriate techniques and source data to produce such inventories and keep them up to date. In this paper, a multi-step methodology is proposed to delineate and classify vLEs based on LiDAR point cloud data in three study areas in Flanders, Belgium. We classified the LiDAR point cloud data into the classes ‘vegetated landscape element point’ and ‘other’ using a Random Forest model with an accuracy classification score ranging between 0.92 and 0.97. The landscape element objects were further classified into the classes ‘tree object’ and ‘shrub object’ using a Logistic Regression model with an area-based accuracy ranging between 0.34 and 0.95.

Airborne LiDAR point cloud data classification based on relevance vector machine

2016 ◽  
Vol 45 (s1) ◽  
pp. 130006
Author(s):  
刘志青 Liu Zhiqing ◽  
李鹏程 Li Pengcheng ◽  
郭海涛 Guo Haitao ◽  
张保明 Zhang Baoming ◽  
陈小卫 Chen Xiaowei ◽  
...  
Keyword(s):  
Point Cloud ◽  
Data Classification ◽  
Relevance Vector Machine ◽  
Airborne Lidar ◽  
Point Cloud Data ◽  
Cloud Data
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