ALBERT

Description: We discuss the different challenges, pros and cons of the fairly new Structure for Motion (SfM) embarked on a vehicle (SfM-EV) technique for slope surveys along transportation network tracks using action cameras embarked on standard moving vehicles. This low-cost technique generates georeferenced and coloured 3D point clouds without using any ground control points. Four action cameras, two of which had an integrated GNSS chip, were used to collect a series of pictures of tracksides at a rate of two images per second each. The SfM-EV results were compared with the results of seven other 3D survey techniques to evaluate the precision and accuracy of this technique, demonstrating the ability of this simple setting to generate 3D scenes. Different platforms for the cameras were tested, such as a bike, car, train, funicular, helicopter and so on. The SfM-EV technique was also tested on several study sites to highlight its strengths and weaknesses and obtain data, such as the density of points, equations of errors, overlap ratios and image resolution. The precision of the SfM-EV results was sufficient for detecting topographical changes close to the track for a volume of ~1 dm3 and the absolute positioning obtained with a low sky obstruction was approximately 5 m. The precision of SfM-EV was of a similar order to the other techniques, with an order of magnitude of a few centimetres. This approach possesses a low price-result quality ratio and is very simple to use. The possibility of using any type of vehicle for surveying is an advantage, especially for transportation track embankments.

Description: 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.

Description: Switzerland is threatened by many natural hazards. Many events occur in built-up environments, affecting infrastructure, buildings, and transportation networks, occasionally producing expensive damages. This expense is why large landslides are generally well studied and monitored in Switzerland to reduce the financial and human risks. However, there is a lack of data on small events, which have recently affected roads and railways. Therefore, in this study, all of the reported natural hazard events that have affected Swiss transportation networks since 2012 were collected in a database. More than 800 events affecting roads and railways were recorded within in a 5-year period from 2012 to 2016. These events are classified into six classes: earth flow, debris flow, rockfall, flood, snow avalanche, and others. Data from Swiss online press articles were sorted by Google Alerts. The search was based on more than 30 keywords in three languages (Italian, French, and German). After verification that the article was related to an actual event that affected a road or a railway track, it was studied in detail. We collected information on more than 170 attributes of events, such as the event date, event type, event localization, meteorological conditions, impacts and damages on the track, and human damages. From this database, a variety of trends over the 5-year period can be observed in the event attributes, particularly the spatial and temporal distributions of the events, and their consequences on traffic (closure duration, deviation, costs of direct damage). The database is imperfect due to the short period of data collection, but it highlights the non-negligible impact of small natural hazard events on roads and railways in Switzerland at a national level. This database contributes to understanding and quantification of these types of events and better integration in risk assessment.

Description: We discuss here the challenges and limitations on surveying rock slope failures using 3D reconstruction from images acquired from Street View Imagery (SVI) and processed with modern photogrammetric workflows. We show how the back in time function can be used for a 3D reconstruction of two or more image sets from the same site but at different instants of time, allowing for rock slope surveying. Three sites in the French alps were selected: (a) a cliff beside a road where a protective wall collapsed consisting on two images sets (60 and 50 images on each set) captured on a six years timeframe; (b) a large-scale active landslide located on a slope at 250 m from the road, using seven images sets (50 to 80 images per set) from five different time periods with three images sets for one period; (c) a cliff over a tunnel which has collapsed, using three images sets on a six years time-frame. The analysis includes the use of different commercially available Structure for Motion (SfM) programs and comparison between the so-extracted photogrammetric point clouds and a LiDAR derived mesh used as a ground truth. As a result, both landslide deformation together with estimation of fallen volumes were clearly identified in the point clouds. Results are site and software-dependent, as a function of the image set and number of images, with model accuracies ranging between 0.1 and 3.1 m in the best and worst scenario, respectively. Despite some clear limitations and challenges, this manuscript demonstrates that this original approach might allow obtaining preliminary 3D 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.

Description: Switzerland is a country threatened by a lot of natural hazards. Many events occur in built environment, affecting infrastructures, buildings or transportation networks and producing occasionally expensive damages. This is the reason why large landslides are generally well studied and monitored in Switzerland to reduce the financial and human risks. However, we have noticed a lack of data on small events which have impacted roads and railways these last years. Therefore, we have collected all the reported natural hazard events which have affected the Swiss transportation networks since 2012 in a database. More than 800 events affecting roads and railways have been recorded in five years from 2012 to 2016. These events are classified into six classes: earth flow, debris flow, rockfall, flood, snow avalanche and others. Data come from Swiss online press articles sorted by Google Alerts. The search is based on more than thirty keywords, in three languages (Italian, French, German). After verifying that the article relates indeed an event which has affected a road or a railways track, it is studied in detail. We get finally information on about sixty attributes by event about event date, event type, event localisation, meteorological conditions as well as impacts and damages on the track and human damages. From this database, many trends over the five years of data collection can be outlined: in particular, the spatial and temporal distributions of the events, as well as their consequences in term of traffic (closure duration, deviation, etc.). Even if the database is imperfect because of it was built and because of the short time period considered, it highlights the not negligible impact of small natural hazard events on roads and railways in Switzerland at a national level. This database helps to better understand and quantify this type of events and to better integrate them in risk assessment.