Projects per year
Abstract
Power line mapping using remote sensing can automate the traditionally labor-intensive power line corridor inspection. Land-based mobile laser scanning (MLS) can be a good choice for the power line mapping if an aerial inspection is impossible, too costly or slow, unsafe, prohibited by regulations, or if more detailed information on the power line corridor is needed. The mapping of the power lines using MLS was studied in a rural environment outside the road network for the first time. An automatic power line extraction algorithm was developed. The algorithm first found power line candidate points based on the shape and orientation of the local neighborhood of a point using principal component analysis. Power lines were retrieved from the candidates using random sample consensus (Ransac) and a new power line labeling method, which takes into account the three-dimensional shape of the power lines. The new labeling method was able to find the power lines and remove false detections, which were found, for example, from the forest. The algorithm was tested in forested and open field (arable land) areas, outside the road environment using two different platforms of MLS, namely, personal backpack and all-terrain vehicle. The recall and precision of the power line extraction were 93.3% and 93.6%, respectively, using 10 cm as a distance criterion for a successful detection. Drifting of the positioning solution of the scanner was the largest error source, being the (contributory) cause for 60–70% of the errors. The platform did not have a significant effect on the power line extraction accuracy. The accuracy was higher in the open field compared to the forest, because the one-dimensional point density along the power line was inhomogeneous and GNSS (global navigation satellite system) signal was weak in the forest. The results suggest that the power lines can be mapped accurately enough for inspection purposes using MLS in a rural environment outside the road network.
Original language | English |
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Article number | 102802 |
Number of pages | 16 |
Journal | Automation in Construction |
Volume | 105 |
DOIs | |
Publication status | Published - 1 Sep 2019 |
MoE publication type | A1 Journal article-refereed |
Keywords
- All-terrain
- Feature extraction
- Forest
- Inspection
- Lidar
- Mobile laser scanning
- Power line corridor
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Projects
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Competence-Based Growth Through Integrated Disruptive Technologies of 3D Digitalization, Robotics, Geospatial Information and Image Processing/Computing Point Cloud Ecosystem
Maksimainen, M., Gullmets, H., Hyyppä, H., Nuikka, M., Jaalama, K., Luhtala, L., Vaaja, M. T., Julin, A., El-Mahgary, S., Siirala, H., Sarlin, M., Handolin, H. & Aho, S.
01/01/2018 → 30/04/2021
Project: Academy of Finland: Strategic research funding
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Virtual and Augmented Reality Content Production and Use
Julin, A., Handolin, H., Vaaja, M. T., Ahlavuo, M., Virtanen, J., Hyyppä, H., Keitaanniemi, A., Torkkel, A., Rantanen, T., Ingman, M. & Kauhanen, H.
25/01/2017 → 31/12/2018
Project: Business Finland: Other research funding
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COMBAT: Competence-Based Growth Through Integrated Disruptive Technologies of 3D Digitalization, Robotics, Geospatial Information and Image Processing/Computing - Point Cloud Ecosystem
Gullmets, H., Nieminen, J., Ahlavuo, M., Vaaja, M. T., Laitala, A., Julin, A., Hyyppä, H., Maksimainen, M., Ståhle, P., Jaalama, K., Virtanen, J., Luhtala, L., Haggren, H., Lehtola, V., Rantanen, T., Ingman, M. & Kauhanen, H.
01/05/2015 → 31/12/2017
Project: Academy of Finland: Strategic research funding