scholarly journals Retraction: Li et al. Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification. ISPRS Int. J. Geo-Inf. 2019, 8, 39, doi.org/10.3390/ijgi8010039

2019 ◽  
Vol 8 (9) ◽  
pp. 402
Author(s):  
Zhiqiang Li ◽  
Chengqi Cheng ◽  
Mei-Po Kwan ◽  
Xiaochong Tong ◽  
Shaohong Tian
Keyword(s):  
Random Forest ◽  
Point Cloud ◽  
Asphalt Pavement ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Pavement Distress ◽  
Random Forest Classification ◽  
Forest Classification

All authors of the published article [...]

scholarly journals Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification

2019 ◽  
Vol 8 (1) ◽  
pp. 39 ◽  
Author(s):  
Zhiqiang Li ◽  
Chengqi Cheng ◽  
Mei-Po Kwan ◽  
Xiaochong Tong ◽  
Shaohong Tian
Keyword(s):  
Random Forest ◽  
Point Cloud ◽  
Asphalt Pavement ◽  
Validation Dataset ◽  
Support Vector ◽  
Cloud Data ◽  
Pavement Distress ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Pavement Distresses

Asphalt pavement ages and incurs various distresses due to natural and human factors. Thus, it is crucial to rapidly and accurately extract different types of pavement distress to effectively monitor road health status. In this study, we explored the feasibility of pavement distress identification using low-altitude unmanned aerial vehicle light detection and ranging (UAV LiDAR) and random forest classification (RFC) for a section of an asphalt road that is located in the suburb of Shihezi City in Xinjiang Province of China. After a spectral and spatial feature analysis of pavement distress, a total of 48 multidimensional and multiscale features were extracted based on the strength of the point cloud elevations and reflection intensities. Subsequently, we extracted the pavement distresses from the multifeature dataset by utilizing the RFC method. The overall accuracy of the distress identification was 92.3%, and the kappa coefficient was 0.902. When compared with the maximum likelihood classification (MLC) and support vector machine (SVM), the RFC had a higher accuracy, which confirms its robustness and applicability to multisample and high-dimensional data classification. Furthermore, the method achieved an overall accuracy of 95.86% with a validation dataset. This result indicates the validity and stability of our method, which highway maintenance agencies can use to evaluate road health conditions and implement maintenance.

scholarly journals Systematic Comparison of Power Line Classification Methods from ALS and MLS Point Cloud Data

2018 ◽  
Vol 10 (8) ◽  
pp. 1222 ◽  
Author(s):  
Yanjun Wang ◽  
Qi Chen ◽  
Lin Liu ◽  
Xiong Li ◽  
Arun Kumar Sangaiah ◽  
...  
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Point Cloud ◽  
Laser Scanning ◽  
Supervised Classification ◽  
Power Lines ◽  
Point Cloud Data ◽  
Classification Methods ◽  
Cloud Data ◽  
Line Classification

Power lines classification is important for electric power management and geographical objects extraction using LiDAR (light detection and ranging) point cloud data. Many supervised classification approaches have been introduced for the extraction of features such as ground, trees, and buildings, and several studies have been conducted to evaluate the framework and performance of such supervised classification methods in power lines applications. However, these studies did not systematically investigate all of the relevant factors affecting the classification results, including the segmentation scale, feature selection, classifier variety, and scene complexity. In this study, we examined these factors systematically using airborne laser scanning and mobile laser scanning point cloud data. Our results indicated that random forest and neural network were highly suitable for power lines classification in forest, suburban, and urban areas in terms of the precision, recall, and quality rates of the classification results. In contrast to some previous studies, random forest yielded the best results, while Naïve Bayes was the worst classifier in most cases. Random forest was the more robust classifier with or without feature selection for various LiDAR point cloud data. Furthermore, the classification accuracies were directly related to the selection of the local neighborhood, classifier, and feature set. Finally, it was suggested that random forest should be considered in most cases for power line classification.

A study on the classification of vegetation point cloud based on the straw checkerbo-ard barriers area of random forest

2021 ◽  
pp. 1-13
Author(s):  
Tiebo Sun ◽  
Jinhao Liu ◽  
Jiangming Kan ◽  
Tingting Sui
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Random Forest Algorithm ◽  
Cloud Data ◽  
Cloud Classification ◽  
Point Cloud Classification

Aiming at the problem of automatic classification of point cloud in the investigation of vegetation resources in the straw checkerboard barriers region, an improved random forest point cloud classification algorithm was proposed. According to the problems of decision tree redundancy and absolute majority voting in the existing random forest algorithm, first the similarity of the decision tree was calculated based on the tree edit distance, further clustered reduction based on the maximum and minimum distance algorithm, and then introduced classification accuracy of decision tree to construct weight matrix to implement weighted voting at the voting stage. Before random forest classification, based on the characteristics of point cloud data, a total of 20 point cloud single-point features and multi-point statistical features were selected to participate in point cloud classification, based on the point cloud data spatial distribution characteristics, three different scales for selecting point cloud neighborhoods were set based on the point cloud density, point cloud classification feature sets at different scales were constructed, optimizing important features of point cloud to participate in point cloud classification calculation after variable importance scored. The experimental results showed that the point cloud classification based on the optimized random forest algorithm in this paper achieved a total classification accuracy of 94.15% in dataset 1 acquired by lidar, the overall accuracy of classification on dataset 2 obtained by dense matching reaches 92.03%, both were higher than the unoptimized random forest algorithm and MRF, SVM point cloud classification method, and dimensionality reduction through feature optimization can greatly improve the efficiency of the algorithm.

scholarly journals Performance analysis of different classifiers in segmenting point cloud data

2021 ◽  
Vol 2107 (1) ◽  
pp. 012003
Author(s):  
N I Boslim ◽  
S A Abdul Shukor ◽  
S N Mohd Isa ◽  
R Wong
Keyword(s):  
Random Forest ◽  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Nearest Neighbour ◽  
Point Cloud Data ◽  
Historical Building ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Different Types

Abstract 3D point clouds are a set of point coordinates that can be obtained by using sensing device such as the Terrestrial Laser Scanner (TLS). Due to its high capability in collecting data and produce a strong density point cloud surrounding it, segmentation is needed to extract information from the massive point cloud containing different types of objects, apart from the object of interest. Bell Tower of Tawau, Sabah has been chosen as the object of interest to study the performance of different types of classifiers in segmenting the point cloud data. A state-of-the-art TLS was used to collect the data. This research’s aim is to segment the point cloud data of the historical building from its scene by using two different types of classifier and to study their performances. Two main classifiers commonly used in segmenting point cloud data of interest like building are tested here, which is Random Forest (RF) and k-Nearest Neighbour (kNN). As a result, it is found out that Random Forest classifier performs better in segmenting the existing point cloud data that represent the historic building compared to k-Nearest Neighbour classifier.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

ESTIMATION OF VEGETATION DENSITY OF TREES USING ALB POINT CLOUD DATA

2018 ◽  
Vol 74 (4) ◽  
pp. I_547-I_552
Author(s):  
Keisuke YOSHIDA ◽  
Shiro MAENO ◽  
Syuhei OGAWA ◽  
Sadayuki ISEKI ◽  
Ryosuke AKOH
Keyword(s):  
Point Cloud ◽  
Point Cloud Data ◽  
Vegetation Density ◽  
Cloud Data
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