Automated and efficient powerline extraction from laser scanning data using a voxel-based subsampling with hierarchical approach

Jung, Jae Hwan; Olsen, Michael J.; Shafer, Katherine C.

doi:10.1016/j.isprsjprs.2020.03.018

Cited by 40 publications

(39 citation statements)

References 18 publications

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“…In a very a latest work, Jung et al [34] proposed a two-step process based on extraction and refinement of candidate PL points. In the candidate PL point extraction step, input point cloud data was subsampled using a 3D voxel grid that preserved the 3D details of point cloud but significantly reduced the data set size.…”

Section: Related Workmentioning

confidence: 99%

Automatic Extraction of High-Voltage Bundle Subconductors Using Airborne LiDAR Data

2020

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Overhead high-voltage conductors are the chief components of power lines and their safety has a strong influence on social and daily life. In the recent decade, the airborne laser scanning (ALS) technique has been widely used to capture the three-dimensional (3D) information of power lines and surrounding objects. Most of the existing methods focused on extraction of single conductors or extracted all conductors as one object class by applying machine learning techniques. Nevertheless, power line corridors (PLCs) are built with multi-loop, multi-phase structures (bundle conductors) and exist in intricate environments (e.g., mountains and forests), and thus raise challenges to process ALS data for extraction of individual conductors. This paper proposes an automated method to extract individual subconductors in bundles from complex structure of PLCs using a combined image- and point-based approach. First, the input point cloud data are grouped into 3D voxel grid and PL points and separated from pylon and tree points using the fact that pylons and trees are vertical objects while power lines are non-vertical objects. These pylons are further separated from trees by employing a statistical analysis technique and used to extract span points between two consecutive pylons; then, by using the distribution properties of power lines in each individual span, the bundles located at different height levels are extracted using image-based processing; finally, subconductors in each bundle are detected and extracted by introducing a window that slides over the individual bundle. The orthogonal plane transformation and recursive clustering procedures are exploited in each window position and a point-based processing is conducted iteratively for extraction of complete individual subconductors in each bundle. The feasibility and validity of the proposed method are verified on two Australian sites having bundle conductors in high-voltage transmission lines. Our experiments show that the proposed method achieves a reliable result by extracting the real structure of bundle conductors in power lines with correctness of 100% and 90% in the two test sites, respectively.

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Section: Related Workmentioning

confidence: 99%

Automatic Extraction of High-Voltage Bundle Subconductors Using Airborne LiDAR Data

2020

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“…Presently, efforts in the inspection of transmission line corridors by using ALS point clouds include the four main following aspects: (1) point cloud classification of transmission lines [ 4 , 5 ]; (2) power line extraction [ 6 , 7 , 8 , 9 ]; (3) 3D reconstruction of power lines [ 10 , 11 ]; and (4) 3D reconstruction of power pylons [ 12 , 13 ]; whereas point cloud classification is the premise for other applications, such as 3D reconstruction of the transmission line corridors and the generation of digital surface models. Generally, point cloud classification includes four basic steps: data pre-processing, feature extraction, feature selection and classification.…”

Section: Introductionmentioning

confidence: 99%

A Classification Method of Point Clouds of Transmission Line Corridor Based on Improved Random Forest and Multi-Scale Features

Tang

Zhang

Lan

et al. 2023

Sensors

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Classification of airborne laser scanning (ALS) point clouds of power lines is of great importance to their reconstruction. However, it is still a difficult task to efficiently and accurately classify the ground, vegetation, power lines and power pylons from ALS point clouds. Therefore, in this paper, a method is proposed to improve the accuracy and efficiency of the classification of point clouds of transmission lines, which is based on improved Random Forest and multi-scale features. The point clouds are filtered by the optimized progressive TIN densification filtering algorithm, then the elevations of the filtered point cloud are normalized. The features of the point cloud at different scales are calculated according to the basic features of the point cloud and the characteristics of transmission lines. The Relief F and Sequential Backward Selection algorithm are used to select the best subset of features to estimate the parameters of the learning model, then an Improved Random Forest classification model is built to classify the point clouds. The proposed method is verified by using three different samples from the study area and the results show that, compared with the methods based on Support Vector Machines, AdaBoost or Random Forest, our method can reduce feature redundancy and has higher classification accuracy and efficiency.

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“…Several predefined discriminant rules for point cloud data extraction of power lines have been proposed based on the linear form of power lines and their typical ground clearance and sag characteristics (Jung et al, 2020; Kim & Medioni, 2011). Lam et al (2010) have suggested an algorithm for urban scene segmentation using Kalman filtering (Kalman, 1960) to eliminate the ground surface.…”

Section: Introductionmentioning

confidence: 99%

An enhanced descriptor extraction algorithm for power line detection from point clouds

Shokri

Rastiveis

Sarasua

et al. 2023

Geographical Research

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Mobile terrestrial laser scanning (MTLS) systems provide a safe and efficient means to survey roadway corridors at high speed. MTLS point clouds are rich in planimetric data. However, manual extraction of useful information from these point clouds can be time consuming and laborious and automated object extraction from MTLS point clouds has become a hot topic in the remote sensing community. This study proposes an automated method for power line extraction from MTLS point clouds based on a multilayer perceptron (MLP) neural network. The proposed method consists of three main steps: (i) point cloud preprocessing, (ii) descriptor extraction and selection, and (iii) point classification. The preprocessing step involves filtering out more than 90% of the point cloud by eliminating the vast majority of unneeded points. Next, various descriptors are extracted from the remaining points including planarity, linearity, and verticality, and the descriptor standard deviation is used to select the best‐suited descriptors for power line extraction. Finally, an MLP neural network is trained using the selected descriptors from several cable and noncable sample points. The proposed algorithm was evaluated in three MTLS point clouds in urban and nonurban environments totalling 5.5 kilometres in length. An average precision of 94% and a recall of 94% showed the algorithm’s reliability and feasibility.

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Automated and efficient powerline extraction from laser scanning data using a voxel-based subsampling with hierarchical approach

Cited by 40 publications

References 18 publications

Automatic Extraction of High-Voltage Bundle Subconductors Using Airborne LiDAR Data

Automatic Extraction of High-Voltage Bundle Subconductors Using Airborne LiDAR Data

A Classification Method of Point Clouds of Transmission Line Corridor Based on Improved Random Forest and Multi-Scale Features

An enhanced descriptor extraction algorithm for power line detection from point clouds

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