Abstract:

A variety of remote sensing tools have been extensively used in the past years for landslide detection and mapping purposes. In addition, detection and mapping of landslide and rockfall events using remote sensing products has been proved to be an effective approach to provide landslide inventories (Scaioni et al., 2014). However, most of the studies are lacking valuable semantic information about landslide elements and how they react with the surrounding environment; natural and man-made primitives. In addition, post classification object-based approaches have been proved to result in better accuracies compared with the pixel-based (Martha et al., 2011). Lately, innovative close-range remote sensing technology such as Unmanned Aerial Vehicle (UAV) photogrammetry and Terrestrial Laser Scanning (TLS) are widely applied in the field of geoscience due to their efficiency in collecting data about terrain morphology rapidly. Their main advantage stands on the fact that conventional methods are mainly collecting point measurements such as compass measurements of bedding and fracture orientation only from areas that were accessible. Aerial platforms are capable to overcome technical issues such as potential occlusions and unfavorable incidence angles due to their ability to capture imagery from multiple positions and with different angles. Nowadays, UAVs tend to be more flexible and powerful tools for landslide and rockfall investigations compared to TLS, due to their low-cost and ease of transportability in harsh environments but also with technology advances such as maintaining of Real Time Kinematic (RTK) positioning. An important factor of their usefulness is their capability to offer unprecedented spatial resolution over wide inaccessible areas, maintain a variability of different sensors (optical, laser, thermal, multispectral) and great ability to reach remote areas and acquire data as close as the user defines. UAVs applications are widely used in post-disaster situations for emergency support, in infrastructure monitoring, in natural resources management, in geohazard monitoring etc. (Corominas et al., 2016; Vassilakis et al., 2019). The latter proves that UAV market has been rapidly growing over the last decade and in future more applications will be introduced in the public. Thus, rapidness and efficiency of Structure-from-Motion (SfM) technology in landslide management provides numerous advantages such as creating landslide inventory maps providing 3D information of large areas.

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