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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 12 | Copyright
Nat. Hazards Earth Syst. Sci., 17, 2093-2107, 2017
https://doi.org/10.5194/nhess-17-2093-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Dec 2017

Research article | 01 Dec 2017

Using street view imagery for 3-D survey of rock slope failures

Jérémie Voumard1, Antonio Abellán1,2, Pierrick Nicolet1,3, Ivanna Penna1,3, Marie-Aurélie Chanut4, Marc-Henri Derron1, and Michel Jaboyedoff1 Jérémie Voumard et al.
  • 1Risk analysis group, Institute of Earth Sciences, FGSE, University of Lausanne, Switzerland
  • 2Scott Polar Research Institute, Department of Geography, University of Cambridge, UK
  • 3Geohazard and Earth Observation team, Geological Survey of Norway (NGU), Norway
  • 4Groupe Risque Rocheux et Mouvements de Sols (RRMS), Cerema Centre-Est, France

Abstract. We discuss here different challenges and limitations of surveying rock slope failures using 3-D reconstruction from image sets acquired from street view imagery (SVI). We show how rock slope surveying can be performed using two or more image sets using online imagery with photographs from the same site but acquired at different instances. Three sites in the French alps were selected as pilot study areas: (1) a cliff beside a road where a protective wall collapsed, consisting of two image sets (60 and 50 images in each set) captured within a 6-year time frame; (2) a large-scale active landslide located on a slope at 250m from the road, using seven image sets (50 to 80 images per set) from five different time periods with three image sets for one period; (3) a cliff over a tunnel which has collapsed, using two image sets captured in a 4-year time frame. The analysis include the use of different structure from motion (SfM) programs and a comparison between the extracted photogrammetric point clouds and a lidar-derived mesh that was used as a ground truth. Results show that both landslide deformation and estimation of fallen volumes were clearly identified in the different point clouds. Results are site- and software-dependent, as a function of the image set and number of images, with model accuracies ranging between 0.2 and 3.8m in the best and worst scenario, respectively. Although some limitations derived from the generation of 3-D models from SVI were observed, this approach allowed us to obtain preliminary 3-D models of an area without on-field images, allowing extraction of the pre-failure topography that would not be available otherwise.

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We discuss the challenges and limitations of surveying rock slope failures using 3-D reconstruction from images acquired from street view imagery (SVI) and processed with modern photogrammetric workflows. Despite some clear limitations and challenges, we demonstrate that this original approach could help obtain preliminary 3-D models of an area without on-field images. Furthermore, the pre-failure topography can be obtained for sites where it would not be available otherwise.
We discuss the challenges and limitations of surveying rock slope failures using 3-D...
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