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Powerline extraction from aerial and mobile LiDAR data using deep learning

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Abstract

Accurate powerline classification from LiDAR point clouds is essential for efficient monitoring and management of power distribution networks. Currently, the classification is being done through manual labeling or some semi-automatic methods which are time-consuming and resource intensive. In this study, we explore three deep learning architectures, namely KPConv, PointCNN, and RandLA-Net, for powerline segmentation in aerial and mobile LiDAR datasets. We utilize manually labeled aerial and mobile LiDAR datasets from Surry in Canada and Kerala in India, respectively. Impressive results are obtained, demonstrating high powerline classification accuracy and precision. KPConv outperformed the other architectures, achieving over 98% accuracy for ALS data and surpassing 94% for MLS data. Additionally, we investigate the impact of ground removal on powerline extraction. The results highlight a significant improvement in powerline classification accuracy, with an approximate 3–4% enhancement observed, particularly in the test sets with a higher density of powerline points. Removing ground points effectively reduces noise and clutter in LiDAR data, leading to improved segmentation outcomes. Furthermore, ground removal proves beneficial in classifying wires in challenging scenarios, such as areas with nearby objects or branch and loose wires. KPConv demonstrates superior performance in powerline classification, capturing intricate powerline details even in scenes with varying point densities. PointCNN shows limitations, especially in wire point classification near other objects. RandLA-Net exhibits higher speed but slightly lower accuracy compared to KPConv. The findings contribute to advancing powerline classification and provide valuable insights for more efficient and automated management of power distribution networks.

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Data availability

The labeled data used in the study can be accessed at https://www.lidaverse.com/#/.

Notes

  1. https://www.geokno.com/?prophazecheck=1.

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Acknowledgements

The authors acknowledge Geokno India Pvt. Ltd. for mobile data and DALES Object for aerial data.

Funding

The authors used the support provided by the cluster project under the Data Science (DS) Research of Frontier and Futuristic Technologies (FFT) Division of the Department of Science and Technology (DST), Government of India, New Delhi. The authors also used financial support provided by the National Geospatial Programme (NGP), the Department of Science and Technology (DST), Government of India, New Delhi under grant NGP/V.Kumar/IISER-Bhopal/2023 (G).

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Authors and Affiliations

  1. GeoAI4Cities Lab, Data Science and Engineering, Indian Institute of Science Education and Research Bhopal, Bhopal, India

    Vaibhav Kumar

  2. Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, India

    Aritra Nandy, Vishal Soni & Bharat Lohani

  3. SimDaaS Autonomy Pvt. Ltd, Kanpur, India

    Bharat Lohani

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  1. Vaibhav Kumar
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  2. Aritra Nandy
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  4. Bharat Lohani
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Contributions

Vaibhav Kumar has developed the methodology, supervised the work, and written the manuscript.Aritra Nandy and Vishal Soni have done formal analysis and written the manuscript.Bharat Lohani has developed the methodology and supervised the work.

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Correspondence to Vaibhav Kumar.

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Communicated by H. Babaie.

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Appendix

Appendix

Table A1 Labels associated with MLS data. Here, XX is superclass label and Z is sub class within a labelled class XX
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Kumar, V., Nandy, A., Soni, V. et al. Powerline extraction from aerial and mobile LiDAR data using deep learning. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01310-w

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