Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 119-126, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-119-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 119-126, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-119-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  04 Jun 2019

04 Jun 2019

CLOUD-BASED SOLUTION FOR NATIONWIDE POWER LINE MAPPING

I. Toschi1, D. Morabito1,4, E. Grilli1, F. Remondino1, C. Carlevaro2, A. Cappellotto2, G. Tamagni3, and M. Maffeis3 I. Toschi et al.
  • 13D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy
  • 2Spindox Labs srl, Trento, Italy
  • 3Enel Group – Europa e Latino America, Milano, Italy
  • 4Laboratory of Photogrammetry, National Technical University of Athens (NTUA), Athens, Greece

Keywords: Power Line Mapping, Photogrammetry, Airborne, Machine Learning, Cloud Computing

Abstract. Automatic tools for power line mapping and monitoring are increasingly required by modern societies. Since traditional methods, like ground-based onsite inspections, are very labour- and time-intensive, the use of Geomatics techniques is becoming the most promising solution. However, there is a need for an all-in-one solution that allows the entire 3D mapping pipeline in a nationwide data context. The aim of this paper is to introduce a novel cloud-based solution for nationwide power line mapping. The innovative aspects of the system are threefold. First, to exploit image-based 3D reconstruction algorithms to derive dense point clouds over power line corridors, thus demonstrating the potential of photogrammetry as a promising alternative to costly LiDAR surveys. Second, to supply an all-in-one web-based pipeline that automatically manages all steps of power line mapping, from 3D data generation to clearance anomaly detection. Finally, to exploit cloud-computing technology, to handle massive input data. First tests show promising results for (i) 3D image-based reconstruction, (ii) point cloud classification and (iii) anomaly detection.