Abstract
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 250 m 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 3.8 m 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.
Highlights
Three-dimensional remote sensing techniques are becoming widely used for geohazard investigations due to their ability to represent the geometry of natural hazards and its evolution over time by comparing 3-D point clouds acquired at different time steps
VisualSFM gave results with print screens from Google Street View (GSV) in Google Maps while Agisoft PhotoScan could not align those print screens despite adding a series of control points measured with Google Earth Pro
The 2008 point cloud is composed of 150 000 points with an average density of 290 points m2, and the 2014 point cloud is composed of 182 000 points with an average density of 640 points m2 (Table 1)
Summary
Three-dimensional remote sensing techniques are becoming widely used for geohazard investigations due to their ability to represent the geometry of natural hazards (mass movements, lava flows, debris flows, etc.) and its evolution over time by comparing 3-D point clouds acquired at different time steps. 3-D remote sensing techniques are helping to better quantify key aspects of rock slope evolution, including the accurate quantification of rockfall rates and the deformation of rock slopes before failure using both lidar (Rosser et al, 2005; Oppikofer et al, 2009; Royan et al, 2014; Kromer et al, 2015; Fey and Wichmann., 2017) and photogrammetrically derived point clouds (Walstra et al, 2007; Lucieer et al, 2013, Stumpf et al, 2015; Fernandes et al, 2016; Guerin et al, 2017; Ruggles et al, 2016). Airborne and terrestrial laser scanners are commonly used techniques to obtain 3-D digital terrain models (Abellán et al, 2014). Despite their very high accuracy and resolution, these technologies are costly and often demanding from a logistical point of view. Some of the largest sources of pictures online are street view imagery (SVI) services, Published by Copernicus Publications on behalf of the European Geosciences Union
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