LiDAR data filtering and classification by skewness and kurtosis iterative analysis of multiple point cloud data categories

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.

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

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w8-91-2019 ◽

2019 ◽

Vol IV-4/W8 ◽

pp. 91-98 ◽

Cited By ~ 1

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

Abstract. 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.

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Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize

Remote Sensing ◽

10.3390/rs13214445 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4445

Author(s):

Behrokh Nazeri ◽

Melba Crawford

Keyword(s):

Deep Learning ◽

Outlier Detection ◽

Point Cloud ◽

Lidar Data ◽

Point Cloud Data ◽

Row Crops ◽

Cloud Data ◽

Local Point ◽

Before And After ◽

The Impact

High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, plant complexity, and local point densities, which are crop-dependent. The goals of this study were first to investigate approaches to minimize the impact of outliers on LiDAR acquired over agricultural row crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform; second, to evaluate the impact of existing outliers in the datasets on leaf area index (LAI) prediction using LiDAR data. Two methods were investigated to detect and remove the outliers from the plant datasets. The first was based on surface fitting to noisy point cloud data via normal and curvature estimation in a local neighborhood. The second utilized the PointCleanNet deep learning framework. Both methods were applied to individual plants and field-based datasets. To evaluate the method, an F-score was calculated for synthetic data in the controlled conditions, and LAI, the variable being predicted, was computed both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust than the geometric approach to changes in point densities, level of noise, and shapes. The prediction of LAI was also improved for the wheel-based vehicle data based on the coefficient of determination (R2) and the root mean squared error (RMSE) of the residuals before and after the removal of outliers.

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Improved Method for LiDAR Point Cloud Data Filtering Based on Hierarchical Pseudo-Grid

Acta Optica Sinica ◽

10.3788/aos201737.0828004 ◽

2017 ◽

Vol 37 (8) ◽

pp. 0828004 ◽

Cited By ~ 1

Author(s):

黄作维 Huang Zuowei ◽

刘峰 Liu Feng ◽

胡光伟 Hu Guangwei

Keyword(s):

Point Cloud ◽

Point Cloud Data ◽

Data Filtering ◽

Improved Method ◽

Cloud Data

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Point Cloud Data Retrieval from 3D Geospatial Database for Automated Road Median Extraction

10.20944/preprints201804.0134.v1 ◽

2018 ◽

Author(s):

Pankaj Kumar ◽

Paul Lewis ◽

Conor P. McElhinney

Keyword(s):

Data Processing ◽

Point Cloud ◽

Laser Scanning ◽

Data Retrieval ◽

Lidar Data ◽

Point Cloud Data ◽

Cloud Data ◽

The Road ◽

Efficient Storage ◽

Geospatial Database

Laser scanning systems make use of Light Detection and Ranging (LiDAR) technology to acquire accurately georeferenced sets of dense 3D point cloud data. The information acquired using these systems produces better knowledge about the terrain objects which are inherently 3D in nature. The LiDAR data acquired from mobile, airborne or terrestrial platforms provides several benefit over conventional sources of data acquisition in terms of accuracy, resolution and attributes. However, the large volume and scale of LiDAR data have inhibited the development of automated feature extraction algorithms due to the extensive computational cost involved in it. Moreover, the heterogeneously distributed point cloud, which represents objects with varying size, point density, holes and complicated structures pose a great challenge for data processing. Currently, geospatial database systems do not provide a robust solution for efficient storage and accessibility of raw data in a way that data processing could be applied based on optimal spatial extent. In this paper, we present Global LiDAR and Imagery Mobile Processing Spatial Environment (GLIMPSE) system that provides a framework for storage, management and integration of 3D LiDAR data acquired from multiple platforms. The system facilitates an efficient accessibility to the raw dataset, which is hierarchically represented in a geographically meaningful way. We utilise the GLIMPSE system to automatically extract road median from Airborne Laser Scanning (ALS) point cloud. In the first part of this paper, we detail an approach to efficiently retrieve the point cloud data from the GLIMPSE system for a particular geographic area based on user requirements. In the second part, we present an algorithm to automatically extract road median from the retrieved LiDAR data. The developed road median extraction algorithm utilises the LiDAR elevation and intensity attributes to distinguish the median from the road surface. We successfully tested our algorithms on two road sections consisting of distinct road median types based on concrete and grass-hedge barriers. The use of GLIMPSE improved the efficiency of the road median extraction in terms of fast accessibility to ALS point cloud data for the required road sections. The developed system and its associated algorithms provide a comprehensive solution to the user's requirement for an efficient storage, integration, retrieval and processing of large volumes of LiDAR point cloud data. These findings and knowledge contribute to a more rapid, cost-effective and comprehensive approach to surveying road networks.

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CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-441-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 441-445 ◽

Cited By ~ 4

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.

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Point cloud data filtering and downsampling using growing neural gas

The 2013 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2013.6706719 ◽

2013 ◽

Cited By ~ 14

Author(s):

Sergio Orts-Escolano ◽

Vicente Morell ◽

Jose Garcia-Rodriguez ◽

Miguel Cazorla

Keyword(s):

Point Cloud ◽

Point Cloud Data ◽

Data Filtering ◽

Growing Neural Gas ◽

Cloud Data ◽

Neural Gas

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EFFICIENT LIDAR POINT CLOUD DATA MANAGING AND PROCESSING IN A HADOOP-BASED DISTRIBUTED FRAMEWORK

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w2-121-2017 ◽

2017 ◽

Vol IV-4/W2 ◽

pp. 121-124 ◽

Cited By ~ 2

Author(s):

C. Wang ◽

F. Hu ◽

D. Sha ◽

X. Han

Keyword(s):

Point Cloud ◽

Lidar Data ◽

City Management ◽

Point Cloud Data ◽

Cloud Data ◽

Cloud Processing ◽

Point Cloud Processing ◽

Distributed Framework ◽

3D Lidar ◽

Parallel Fashion

Light Detection and Ranging (LiDAR) is one of the most promising technologies in surveying and mapping，city management, forestry, object recognition, computer vision engineer and others. However, it is challenging to efficiently storage, query and analyze the high-resolution 3D LiDAR data due to its volume and complexity. In order to improve the productivity of Lidar data processing, this study proposes a Hadoop-based framework to efficiently manage and process LiDAR data in a distributed and parallel manner, which takes advantage of Hadoop’s storage and computing ability. At the same time, the Point Cloud Library (PCL), an open-source project for 2D/3D image and point cloud processing, is integrated with HDFS and MapReduce to conduct the Lidar data analysis algorithms provided by PCL in a parallel fashion. The experiment results show that the proposed framework can efficiently manage and process big LiDAR data.

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Classification Airbrone LiDAR Point cloud Data

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst0701008 ◽

2021 ◽

Vol 7 (01) ◽

pp. 36-39

Author(s):

Naga Madhavi lavanya Gandi

Keyword(s):

High Resolution ◽

Land Cover ◽

Point Cloud ◽

Near Infrared ◽

Lidar Data ◽

Point Cloud Data ◽

Mid Infrared ◽

Cloud Data ◽

Classification Information

Land cover classification information plays a very important role in various applications. Airborne Light detection and Ranging (LiDAR) data is widely used in remote sensing application for the classification of land cover. The present study presents a Spatial classification method using Terrasoild macros . The data used in this study are a LiDAR point cloud data with the wavelength of green:532nm, near infrared:1064nm and mid-infrared-1550nm and High Resolution RGB data. The classification is carried in TERRASCAN Module with twelve land cover classes. The classification accuracies were assessed using high resolution RGB data. From the results it is concluded that the LiDAR data classification with overall accuracy and kappa coefficient 85.2% and 0.7562.

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AUTOMATED RECONSTRUCTION OF 3D BUILDINGS IN HISTORIC CITY CENTERS FROM LIDAR DATA AND 2D BUILDING FOOTPRINTS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w6-19-2018 ◽

2018 ◽

Vol IV-4/W6 ◽

pp. 19-23

Author(s):

J. Ingensand ◽

M. Nappez ◽

T. Produit ◽

T. Chassin

Keyword(s):

Field Study ◽

Point Cloud ◽

Lidar Data ◽

Point Cloud Data ◽

Geometric Information ◽

Cloud Data ◽

Automated Generation ◽

Cadastral Maps ◽

Historic City

Abstract. This paper describes a process for the automated generation of 3D buildings using 2D building footprints derived from cadastral maps and LIDAR point cloud data. In our approach we extract relevant geometric information from 2D building footprints in order to classify point cloud data. One key concept is the fact that roofs in most cases are aligned to the angles of the walls of a building. This concept is utilized to create contiguous surfaces and to extract ridges. In a field study involving two historic city centers in Switzerland we evaluate the results of our approach.

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