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Machine learning approach for tenaga nasional berhad (tnb) overhead powerline and electricity pole inventory using mobile laser scanning data

Decision trees; Laser applications; Learning algorithms; Machine learning; Mean square error; Scanning; Classifieds; Electricity assets; Electricity pole; Laser scanner data; Laserscanners; Machine-learning; Mobile laser scanner; Overhead powerline; Point-clouds; Power lines; Poles

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Main Authors: Mohd Rapheal M.S.A., Farhana A., Mohd Salleh M.R., Abd Rahman M.Z., Majid Z., Musliman I.A., Abdullah A.F., Abd Latif Z.
Other Authors: 57929782800
Format: Conference Paper
Published: International Society for Photogrammetry and Remote Sensing 2023
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spelling my.uniten.dspace-270002023-05-29T17:38:32Z Machine learning approach for tenaga nasional berhad (tnb) overhead powerline and electricity pole inventory using mobile laser scanning data Mohd Rapheal M.S.A. Farhana A. Mohd Salleh M.R. Abd Rahman M.Z. Majid Z. Musliman I.A. Abdullah A.F. Abd Latif Z. 57929782800 57930161900 57008087300 56757045000 9247739400 55780272000 55142706500 57216335420 Decision trees; Laser applications; Learning algorithms; Machine learning; Mean square error; Scanning; Classifieds; Electricity assets; Electricity pole; Laser scanner data; Laserscanners; Machine-learning; Mobile laser scanner; Overhead powerline; Point-clouds; Power lines; Poles Electricity assets recognition and inventory is a fundamental task in the geospatial-based electrical power distribution management. In Malaysia, Tenaga Nasional Berhad (TNB) aims to complete their assets inventory throughout the country by 2022. Previous research has shown that a method for assets detection especially for TNB is still at an early stage, which mainly relied on manual extraction of the assets from different data sources including mobile laser scanner (MLS). This research aims at evaluating a geospatial method based on machine learning to classify the TNB assets using high density MLS data. The MLS data was collected using Riegl VMQ-1 HA scanner and supported by the base station and control points for point cloud registration purpose. In the first stage the point clouds were classified into ground and non-ground objects. The non-ground points were further classified into different landcover types i.e. vegetation, building, and other classes. The points classified as other classes were used for overhead powerline and electricity poles classification using random forest-based Machine Learning (ML) approach in LiDAR 360 software. Based on the classified point clouds, detailed characteristics of electricity poles (i.e. number of poles, height, diameter and inclination from ground) and overhead powerlines (number of cable segments) were estimated. This information was validated using field collected reference data. The results show that the detection accuracy for electricity poles and overhead power line are 65% and 63% respectively. The estimation of length, diameter and height of the spun pole from point clouds has produced Root Mean Square Error (RMSE) value of 0.081cm, 0.263 cm and 0.372 cm respectively. Meanwhile for the concrete pole, the length, diameter and height has been successfully estimated with the value of RMSE of 0.034 cm, 0.029 cm and 0.331 cm respectively. The length of overhead powerline was estimated with 59.02 cm RMSE. In conclusion, the MLS data had show promising results for a semi-automatic detection and characterization of TNB overhead powerlines and poles in the sub-urban area. Such outcome can be used to support the inventory and maintenance process of the TNB assets. � 2022 International Society for Photogrammetry and Remote Sensing. All rights reserved. Final 2023-05-29T09:38:32Z 2023-05-29T09:38:32Z 2022 Conference Paper 10.5194/isprs-archives-XLVI-4-W3-2021-239-2022 2-s2.0-85139923614 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139923614&doi=10.5194%2fisprs-archives-XLVI-4-W3-2021-239-2022&partnerID=40&md5=5e7829b2b474f240d4c5a6b900bc9b6c https://irepository.uniten.edu.my/handle/123456789/27000 46 4/W3-2021 239 246 All Open Access, Gold International Society for Photogrammetry and Remote Sensing Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Decision trees; Laser applications; Learning algorithms; Machine learning; Mean square error; Scanning; Classifieds; Electricity assets; Electricity pole; Laser scanner data; Laserscanners; Machine-learning; Mobile laser scanner; Overhead powerline; Point-clouds; Power lines; Poles
author2 57929782800
author_facet 57929782800
Mohd Rapheal M.S.A.
Farhana A.
Mohd Salleh M.R.
Abd Rahman M.Z.
Majid Z.
Musliman I.A.
Abdullah A.F.
Abd Latif Z.
format Conference Paper
author Mohd Rapheal M.S.A.
Farhana A.
Mohd Salleh M.R.
Abd Rahman M.Z.
Majid Z.
Musliman I.A.
Abdullah A.F.
Abd Latif Z.
spellingShingle Mohd Rapheal M.S.A.
Farhana A.
Mohd Salleh M.R.
Abd Rahman M.Z.
Majid Z.
Musliman I.A.
Abdullah A.F.
Abd Latif Z.
Machine learning approach for tenaga nasional berhad (tnb) overhead powerline and electricity pole inventory using mobile laser scanning data
author_sort Mohd Rapheal M.S.A.
title Machine learning approach for tenaga nasional berhad (tnb) overhead powerline and electricity pole inventory using mobile laser scanning data
title_short Machine learning approach for tenaga nasional berhad (tnb) overhead powerline and electricity pole inventory using mobile laser scanning data
title_full Machine learning approach for tenaga nasional berhad (tnb) overhead powerline and electricity pole inventory using mobile laser scanning data
title_fullStr Machine learning approach for tenaga nasional berhad (tnb) overhead powerline and electricity pole inventory using mobile laser scanning data
title_full_unstemmed Machine learning approach for tenaga nasional berhad (tnb) overhead powerline and electricity pole inventory using mobile laser scanning data
title_sort machine learning approach for tenaga nasional berhad (tnb) overhead powerline and electricity pole inventory using mobile laser scanning data
publisher International Society for Photogrammetry and Remote Sensing
publishDate 2023
_version_ 1806427767208673280
score 13.2014675

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