Volume XLII-2/W11
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W11, 461-468, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W11-461-2019
© 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/W11, 461-468, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W11-461-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  04 May 2019

04 May 2019

REMOTE SENSING TECHNOLOGIES FOR LINEAR INFRASTRUCTURE MONITORING

P. D’Aranno1, A. Di Benedetto2, M. Fiani2, and M. Marsella3 P. D’Aranno et al.
  • 1Survey Lab S.r.l., Rome, Italy
  • 2Dipartimento di Ingegneria Civile, Università di Salerno, Fisciano (SA), Italy
  • 3Dipartimento di Ingegneria Civile Edile e Ambientale, Università La Sapienza, Rome, Italy

Keywords: DinSAR, LiDAR, Infrastructures, Monitoring, Data processing, Correlation

Abstract. The need for a continuous evaluation of the state of preservation of civil infrastructures during their lifetime is increasingly requiring advanced monitoring technologies. The improvement of spatial and temporal resolution of the measurements is now one of the most significant achievement, especially for large infrastructures. Monitoring actions are necessary to maintain safety conditions by controlling the evolution of deformation patterns or detecting significant instabilities. Remote sensing technique such as Differential Interferometry by Synthetic Aperture Radar (DInSAR) allows identifying environmental vulnerability and potential damages on large road infrastructures thus contributing to plan and optimize maintenance actions. DInSAR data allow to highlight instability processes and to quantify mean deformation velocities and displacement time series. This information can be analysed considering geotechnical and structural characteristics and adopted to evaluate possible safety condition improvement and damage mitigation. Using proximal remote sensing techniques, such as Light Detection And Ranging (LiDAR), it is possible to analyse the pavement conditions on 3D models derived from a dense point cloud acquired by Mobile Laser Scanner (MLS). By combining the DInSAR and LiDAR datasets a great improvement is expected in the capability to promptly identifying critical situations and understanding potential risks affecting extended road infrastructures. The principal aim of this paper is to provide a general overview of the most innovative remote sensing techniques for infrastructure safety condition assessments. Furthermore, a methodological approach to define a reliable procedure for data processing and integration is applied on a test area located in the municipality of Rome.