Skip to main content
SpringerLink
Log in

A Robust and Efficient Method for Power Lines Extraction from Mobile LiDAR Point Clouds

  • Original Article
  • Published:
PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Aims and scope Submit manuscript

Abstract

Monitoring, maintaining, and organizing power lines corridors are of great importance because they are a primary means to transfer generated electricity from power stations to surrounding areas. Mobile Terrestrial Laser Scanning (MTLS) systems have significant potential for efficiently creating a power line infrastructure inventory. In this paper, a novel algorithm is presented for automatically extracting utility poles and cables from MTLS point clouds in three consecutive phases of pre-processing, poles extraction, and cables extraction. In the pre-processing step, after dividing the MTLS data into several tiles or sections along the road and using trajectory data, noisy points and low-height points are eliminated from each section. Next, search areas containing lines are detected using a Hough Transform (HT) algorithm, and utility poles are identified based on horizontal and vertical density information. The search area for cables is estimated using a two-dimensional (2D) Delaunay Triangulation (DT) of the center points of the extracted poles as vertices. In each search area, high-density points are removed as non-cable points and utility cables are eventually extracted by fitting cable points to polynomial equations. The algorithm was tested on three different MTLS point clouds from a 1371 m urban road section, and a 2800 m and a 500 m non-urban road sections. Each of these datasets has unique challenges and was used to evaluate the efficiency of the proposed algorithm under different conditions. The algorithm was able to extract poles with average correctness of 100% (no false positives) and completeness of 97%. Similarly, average correctness and completeness of 100% and 95.6% were attained for cables, respectively. These detection levels show that the proposed method for power lines extraction from an MTLS point cloud is both reliable and feasible.

Zusammenfassung

Eine robuste und effiziente Methode zur Detektion von Stromleitungen in Punktwolken aus dem mobilen Laserscanning. Überwachung, Instandhaltung und Management von frei hängenden Stromleitungen sind von großer Bedeutung, da davon die Versorgung der Endverbraucher abhängt. Mobile terrestrische Laserscanner (MTLS) - Systeme haben ein erhebliches Potenzial für die effiziente Bestandsaufnahme der Stromleitungsinfrastruktur. In diesem Beitrag wird ein neuer Algorithmus für die automatische Extraktion von Strommasten und Kabeln aus MTLS-Punktwolken vorgestellt. Der Algorithmus arbeitet in drei Phasen, nämlich Vorverarbeitung, Extraktion der Masten und Extraktion der Kabel. In der Vorverarbeitung werden auf Grundlage der Tracking-Daten des Fahrzeugs die MTLS-Daten in Kacheln entlang des Fahrweges aufgeteilt und verrauschte und bodennahe Punkte gelöscht. Danach werden Linienstrukturen mittels der Hough-Transform (HT) gesucht und Strommasten basierend auf horizontalen und vertikalen Dichteinformationen identifiziert. Die Suchbereiche für die Kabel werden mit Hilfe einer zweidimensionalen (2D) Delaunay-Triangulation (DT) zwischen den schon extrahierten Strommasten festgelegt. In jedem Suchbereich werden Verdichtungen in der Punktwolke als "Nicht-Kabelpunkte" entfernt. Danach werden die linienhaften Strukturen als Polynome modelliert und aufgrund dessen die Kabel gefunden. Der Algorithmus wurde an zwei verschiedenen MTLS-Punktwolken getestet, und zwar an einem 2800 m langen städtischen Straßenabschnitt und einer 1371 m langen Landstraße. Jeder dieser Datensätze hatte seine besonderen Herausforderungen und wurde verwendet, um die Effizienz des vorgeschlagenen Algorithmus unter unterschiedlichen Bedingungen zu bewerten. Der Algorithmus konnte Strommasten mit einer durchschnittlichen Richtigkeit von 100% (keine False Positives) und Vollständigkeit von 97% extrahieren. Für Kabel galten die Werte 100% bzw. 95,6%. Die Ergebnisse zeigen, dass die vorgeschlagene Methode zur Extraktion von Stromleitungen aus einer MTLS-Punktwolke sowohl zuverlässig als auch praktisch einsetzbar arbeitet.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  • Awrangjeb M (2019) Extraction of power line pylons and wires using airborne LiDAR data at different height levels. Remote Sens 2019:11

    Google Scholar 

  • Biasotto LD, Kindel A (2018) Power lines and impacts on biodiversity: a systematic review. Environ Impact Assess Rev 71:110–119

    Article  Google Scholar 

  • Bouhmala N (2016) How good is the Euclidean distance metric for the clustering problem, 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), pp 312–315

  • Cabo C, Ordóñez C, López-Sánchez CA, Armesto J (2018) Automatic dendrometry: tree detection, tree height and diameter estimation using terrestrial laser scanning. Int J Appl Earth Obs Geoinf 69:164–174

    Google Scholar 

  • Cai S, Zhang W, Liang X, Wan P, Qi J, Yu S, Yan G, Shao J (2019) Filtering airborne LiDAR data through complementary cloth simulation and progressive TIN densification filters. Remote Sens 2019:11

    Google Scholar 

  • Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986:679–698

    Article  Google Scholar 

  • Chandrasekaran R, Payan AP, Collins KB, Mavris DN (2020) Helicopter wire strike protection and prevention devices: review, challenges, and recommendations. Aerospace Sci Technol 98:105665

    Article  Google Scholar 

  • Che E, Jung J, Olsen MJ (2019) Object recognition, segmentation, and classification of mobile laser scanning point clouds: a state of the art review. Sensors 19:810

    Article  Google Scholar 

  • Chen B, Miao X (2020) Distribution line pole detection and counting based on YOLO using UAV inspection line video. J Electr Eng Technol 15:441–448

    Article  Google Scholar 

  • Chen C, Yang B, Song S, Peng X, Huang R (2018) Automatic clearance anomaly detection for transmission line corridors utilizing UAV-Borne LIDAR data. Remote Sens 2018:10

    Google Scholar 

  • Chen X, Wang SA, Zhang B, Luo L (2018) Multi-feature fusion tree trunk detection and orchard mobile robot localization using camera/ultrasonic sensors. Comput Electron Agric 147:91–108

    Article  Google Scholar 

  • Cheng L, Tong L, Wang Y, Li M (2014) Extraction of urban power lines from vehicle-Borne LiDAR data. Remote Sens 2014:6

    Google Scholar 

  • Davies ER (1986) Constraints on the design of template masks for edge detection. Pattern Recogn Lett 4:111–120

    Article  Google Scholar 

  • Fang H, Lafarge F (2019) Pyramid scene parsing network in 3D: improving semantic segmentation of point clouds with multi-scale contextual information. ISPRS J Photogramm Remote Sens 154:246–258

    Article  Google Scholar 

  • Fore AG, Chapman BD, Hawkins BP, Hensley S, Jones CE, Michel TR, Muellerschoen RJ (2015) UAVSAR polarimetric calibration. IEEE Trans Geosci Remote Sens 53:3481–3491

    Article  Google Scholar 

  • Fujiwara T, Hashimoto R, Usaka S, Yokoyama T, Iwasaki J-I (2020) Investigation of texture classification for power line surface by using CNN. In: Duan B, Umeda K, Hwang W (eds) Proceedings of the Seventh Asia International Symposium on Mechatronics. Springer, Singapore, pp 976–980

  • Gonzales RC, Woods RE (2002) Digital image processing. Prentice Hall, New Jersey

    Google Scholar 

  • Grubesic TH, Nelson JR (2020) UAS platforms and applications for mapping and urban analysis. In: Grubesic TH, Nelson JR (eds) UAVs and urban spatial analysis: an introduction. Springer International Publishing, Cham, pp 13–29

    Chapter  Google Scholar 

  • Guan H, Li J, Yu Y, Wang C, Chapman M, Yang B (2014) Using mobile laser scanning data for automated extraction of road markings. ISPRS J Photogramm Remote Sens 87:93–107

    Article  Google Scholar 

  • Guan H, Yu Y, Li J, Ji Z, Zhang Q (2016) Extraction of power-transmission lines from vehicle-borne lidar data. Int J Remote Sens 37:229–247

    Article  Google Scholar 

  • Hao W, Wang Y, Li Y, Shi Z, Zhao M, Liang W (2018) Hierarchical extraction of pole-like objects from scene point clouds. Opt Eng 57(1–11):11

    Google Scholar 

  • Hui Z, Li D, Jin S, Ziggah YY, Wang L, Hu Y (2019) Automatic DTM extraction from airborne LiDAR based on expectation-maximization. Opt Laser Technol 112:43–55

    Article  Google Scholar 

  • Jung J, Che E, Olsen MJ, Parrish C (2019) Efficient and robust lane marking extraction from mobile lidar point clouds. ISPRS J Photogramm Remote Sens 147:1–18

    Article  Google Scholar 

  • Jung J, Che E, Olsen MJ, Shafer KC (2020) Automated and efficient powerline extraction from laser scanning data using a voxel-based subsampling with hierarchical approach. ISPRS J Photogramm Remote Sens 163:343–361

    Article  Google Scholar 

  • Kang Z, Yang J, Zhong R, Wu Y, Shi Z, Lindenbergh R (2018) Voxel-based extraction and classification of 3-D pole-like objects from mobile LiDAR point cloud data. IEEE J Sel Top Appl Earth Observ Remote Sens 11:4287–4298

    Article  Google Scholar 

  • Lehtomäki M, Kukko A, Matikainen L, Hyyppä J, Kaartinen H, Jaakkola A (2019) Power line mapping technique using all-terrain mobile laser scanning. Autom Constr 105:102802

    Article  Google Scholar 

  • Li F, Lehtomäki M, Oude Elberink S, Vosselman G, Kukko A, Puttonen E, Chen Y, Hyyppä J (2019) Semantic segmentation of road furniture in mobile laser scanning data. ISPRS J Photogramm Remote Sens 154:98–113

    Article  Google Scholar 

  • Liang J, Zhang J, Deng K, Liu Z, Shi Q (2011) A new power-line extraction method based on Airborne LiDAR point cloud data, 2011 International Symposium on Image and Data Fusion, pp 1–4

  • Ma W, Li Q (2019) An improved Ball Pivot algorithm-based ground filtering mechanism for LiDAR data. Remote Sens 2019:11

    Google Scholar 

  • Ma L, Li Y, Li J, Zhong Z, Chapman MA (2019a) Generation of horizontally curved driving lines in HD Maps using mobile laser scanning point clouds. IEEE J Sel Top Appl Earth Observ Remote Sens 12:1572–1586

    Article  Google Scholar 

  • Ma L, Liu Y, Zhang X, Ye Y, Yin G, Johnson BA (2019b) Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J Photogramm Remote Sens 152:166–177

    Article  Google Scholar 

  • Martinez C, Sampedro C, Chauhan A, Collumeau JF, Campoy P (2018) The Power Line Inspection Software (PoLIS): a versatile system for automating power line inspection. Eng Appl Artif Intell 71:293–314

    Article  Google Scholar 

  • Mathur N, Mathur S, Mathur D (2016) A Novel approach to improve Sobel Edge Detector. Procedia Comput Sci 93:431–438

    Article  Google Scholar 

  • Matikainen L, Lehtomäki M, Ahokas E, Hyyppä J, Karjalainen M, Jaakkola A, Kukko A, Heinonen T (2016) Remote sensing methods for power line corridor surveys. ISPRS J Photogramm Remote Sens 119:10–31

    Article  Google Scholar 

  • Meng X, Currit N, Zhao K (2010) Ground filtering algorithms for Airborne LiDAR data: a review of critical issues. Remote Sens 2010:2

    Google Scholar 

  • Mlsna PA, Rodríguez JJ (2009) Chapter 19—gradient and Laplacian edge detection. In: Bovik A (ed) The essential guide to image processing. Academic Press, Boston, pp 495–524

    Chapter  Google Scholar 

  • Morita K, Hashimoto M, Takahashi K (2019) Point-cloud mapping and merging using mobile laser scanner, 2019 third IEEE international conference on robotic computing (IRC), pp 417–418

  • Munir N, Awrangjeb M, Stantic B (2020) Automatic extraction of high-voltage bundle subconductors using Airborne LiDAR data. Remote Sens 2020:12

    Google Scholar 

  • Nguyen VN, Jenssen R, Roverso D (2018) Automatic autonomous vision-based power line inspection: a review of current status and the potential role of deep learning. Int J Electr Power Energy Syst 99:107–120

    Article  Google Scholar 

  • Otsu N (1979) A threshold selection method from Gray-Level histograms. IEEE Trans Syst Man Cybern 9:62–66

    Article  Google Scholar 

  • Pidaparti R, Kalaga S (2017) A comparative study of distribution structure cross arms. Open Civ Eng J 11:757–767

    Article  Google Scholar 

  • Rastiveis H, Shams A, Sarasua WA, Li J (2020) Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference. ISPRS J Photogramm Remote Sens 160:149–166

    Article  Google Scholar 

  • Rusu RB, Marton ZC, Blodow N, Dolha M, Beetz M (2008) Towards 3D Point cloud based object maps for household environments. Robot Auton Syst 56:927–941

    Article  Google Scholar 

  • Safaie AH, Heidar, Rastiveis H, Shams A, Sarasua WA, Li J (2021) Automated street tree inventory using mobile LiDAR point clouds based on Hough transform and active contours. ISPRS J Photogramm Remote Sens 174:19–34. https://doi.org/10.1016/j.isprsjprs.2021.01.026

    Article  Google Scholar 

  • Sarabandi K, Park M (2000) Extraction of power line maps from millimeter-wave polarimetric SAR images. IEEE Trans Antennas Propag 48:1802–1809

    Article  Google Scholar 

  • Shams A, Sarasua WA, Famili A, Davis WJ, Ogle JH, Cassule L, Mammadrahimli A (2018) Highway cross-slope measurement using mobile LiDAR. Transp Res Rec 2672:88–97

    Article  Google Scholar 

  • Shi Z, Kang Z, Lin Y, Liu Y, Chen W (2018) Automatic recognition of pole-like objects from mobile laser scanning point Clouds. Remote Sens 2018:10

    Google Scholar 

  • Shi Z, Lin Y, Li H (2020) Extraction of urban power lines and potential hazard analysis from mobile laser scanning point clouds. Int J Remote Sens 41:3411–3428

    Article  Google Scholar 

  • Shokri D, Rastiveis H, Shams A, Sarasua W (2019) Utility poles extraction from mobile Lidar data in urban area based on density information. Int Arch Photogramm Remote Sens Spatial Inf Sci 2019:42

    Google Scholar 

  • Thanh Ha T, Chaisomphob T (2020) Automated localization and classification of expressway pole-like road facilities from mobile laser scanning data. Adv Civ Eng 2020:5016783

    Google Scholar 

  • Thiel C, Schmullius C (2017) Comparison of UAV photograph-based and airborne lidar-based point clouds over forest from a forestry application perspective. Int J Remote Sens 38:2411–2426

    Article  Google Scholar 

  • Wang Y, Chen Q, Liu L, Zheng D, Li C, Li K (2017) Supervised classification of power lines from Airborne LiDAR data in urban areas. Remote Sens 2017:9

    Google Scholar 

  • Wydra M, Kisala P, Harasim D, Kacejko P (2018) Overhead transmission line sag estimation using a simple optomechanical system with Chirped Fiber Bragg Gratings. Part 1: preliminary measurements. Sensors (basel, Switzerl) 18:309

    Article  Google Scholar 

  • Xia S, Chen D, Wang R, Li J, Zhang X (2020) Geometric primitives in LiDAR point clouds: a review. IEEE J Sel Top Appl Earth Observ Remote Sens 13:685–707

    Article  Google Scholar 

  • Xiao W, Vallet B, Schindler K, Paparoditis N (2016) Street-side vehicle detection, classification and change detection using mobile laser scanning data. ISPRS J Photogramm Remote Sens 114:166–178

    Article  Google Scholar 

  • Xu S, Xu S, Ye N, Zhu F (2018) Automatic extraction of street trees’ nonphotosynthetic components from MLS data. Int J Appl Earth Obs Geoinf 69:64–77

    Google Scholar 

  • Yadav M, Chousalkar CG (2017) Extraction of power lines using mobile LiDAR data of roadway environment. Remote Sens Appl Soc Env 8:258–265

    Google Scholar 

  • Yadav M, Lohani B, Singh AK, Husain A (2016) Identification of pole-like structures from mobile lidar data of complex road environment. Int J Remote Sens 37:4748–4777

    Article  Google Scholar 

  • Yang B, Dong Z, Zhao G, Dai W (2015) Hierarchical extraction of urban objects from mobile laser scanning data. ISPRS J Photogramm Remote Sens 99:45–57

    Article  Google Scholar 

  • Yi C, Zhang Y, Wu Q, Xu Y, Remil O, Wei M, Wang J (2017) Urban building reconstruction from raw LiDAR point data. Comput Aided Des 93:1–14

    Article  Google Scholar 

  • Yokoyama H, Date H, Kanai S, Takeda H (2011) Pole-like objects recognition from mobile laser scanning data using smoothing and principal component analysis. Int Arch Photogramm Remote Sens Spatial Inf Sci Arch 5:115–120

    Google Scholar 

  • Yu Y, Guan H, Li D, Jin C, Wang C, Li J (2019) Road manhole cover delineation using mobile laser scanning point cloud data. IEEE Geosci Remote Sens Lett 2019:1–5

    Google Scholar 

  • Yuan-Kai H, Wei G, Zhang Y, Wu L (2010) An adaptive threshold for the Canny Operator of edge detection, 2010 international conference on image analysis and signal processing, pp 371–374

  • Zaboli M, Rastiveis H, Shams A, Hosseiny B, Sarasua WA (2019) Classification of mobile terrestrial lidar point cloud in urban area using local descriptors. Int Arch Photogramm Remote Sens Spatial Inf Sci XLII-4/W18:1117–1122

    Article  Google Scholar 

  • Zeybek M, Şanlıoğlu İ (2019) Point cloud filtering on UAV based point cloud. Measurement 133:99–111

    Article  Google Scholar 

  • Zhu L, Hyyppä J (2014) Fully-automated power line extraction from airborne laser scanning point clouds in forest areas. Remote Sens 6:11267–11282

    Article  Google Scholar 

Download references

Acknowledgements

Authors would like to thanks Prof. Haiyan Guan for sharing their MTLS dataset (Guan et al. 2016).

Funding

This research received no external funding, however, the original US datasets were collected as part of research sponsored by the South Carolina Department of Tranportation.

Author information

Authors and Affiliations

  1. Department of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Danesh Shokri & Heidar Rastiveis

  2. Glenn Department of Civil Engineering, Clemson University, Clemson, SC, USA

    Wayne A. Sarasua

  3. Department of Environmental and Civil Engineering, Mercer University, Macon, GA, USA

    Alireza Shams

  4. Centre Eau Terre Environnement Research, Institut National de la Recherche Scientifique (INRS), Quebec, G1K 9A9, Canada

    Saeid Homayouni

Authors
  1. Danesh Shokri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Heidar Rastiveis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wayne A. Sarasua
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alireza Shams
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Saeid Homayouni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Heidar Rastiveis.

Ethics declarations

Conflicts of interest

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shokri, D., Rastiveis, H., Sarasua, W.A. et al. A Robust and Efficient Method for Power Lines Extraction from Mobile LiDAR Point Clouds. PFG 89, 209–232 (2021). https://doi.org/10.1007/s41064-021-00155-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41064-021-00155-y

Keywords

Search

Navigation