Abstract:

Late years, innovative close-range Remote Sensing (RS) 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 surface data. Detection and mapping of landslide and rockfall events using RS products has been proved to be an effective approach to provide landslide inventories. However, most of the studies are lacking valuable semantic information about landslide elements and how they react with the surrounding environment. The proposed methodology was divided in five main working phases. The first phase includes designing and execution of an optimal UAV flight planning to collect accurate 3D data. During the second phase, the pre-processing and raw data preparation such as point cloud filtering and elimination of ambiguities is taking place, while at the next phase an image segmentation using the 3D point cloud RGB information is created. The main task was focused on identifying the specific landslide elements by using an object-based approach. A sequence of image-based processes was applied, including multi-scale object segmentation, spectral, morphometric and contextual information extraction aiming to detect the landslide among other features. The next phase was set up for object classification in meaningful and homogeneous landslide classes (e.g. scarp, depletion zone, accumulation zone) which are spatially connected by introducing contextual information. The proposed methodology presents the effectiveness and efficiency of UAV platforms to acquire accurate data from intense relief environments and complex surface topographies.

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