Abstract
The increased development of computer vision technology combined with the increased availability of innovative platforms with ultra-high-resolution sensors, has generated new opportunities and fields for investigation in the engineering geology domain in general and landslide identification and characterization in particular. During the last decade, the so-called Unmanned Aerial Vehicles (UAVs) have been evaluated for diverse applications such as 3D terrain analysis, slope stability, mass movement hazard and risk management. Their advantages of detailed data acquisition at a low cost and effective performance identifies them as leading platforms for site-specific 3D modelling. In this study, the proposed methodology has been developed based on Object-Based Image Analysis (OBIA) and fusion of multivariate data resulted from UAV photogrammetry processing in order to take full advantage of the produced data. Two landslide case studies within the territory of Greece, with different geological and geomorphological characteristics, have been investigated in order to assess the developed landslide detection and characterization algorithm performance in distinct scenarios. The methodology outputs demonstrate the potential for an accurate characterization of individual landslide objects within this natural process based on ultra high-resolution data from close range photogrammetry and OBIA techniques for landslide conceptualization. This proposed study shows that UAV-based landslide modelling on the specific case sites provides a detailed characterization of local scale events in an automated sense with high adaptability on the specific case site.
Highlights
Natural hazards pose a major threat in multiple regions of the world and usually cause crucial economic dislocation, environmental impacts and fatal injuries
The initial phase encompasses the determination of appropriate input layers for segmentation, determination of the optimum parameters for the multiresolution segmentation (MLS)
The second phase entails the appropriate extraction of “landslide hazardous regions” and “non-landslide” information with the optimal image object metrics in order to be used for further landslide risk management procedures
Summary
Natural hazards pose a major threat in multiple regions of the world and usually cause crucial economic dislocation, environmental impacts and fatal injuries. In most of the cases, those failures are linked as indirect post-events after heavy rainfall [2] or seismic events [3] and the majority cause considerable losses. These mass movements are favored along pre-existing instabilities and their timely identification is of primary importance. The tremendous effects of landslides have serious impacts on the anthropogenic environment [4] and most of the times the event is abrupt with no warning sign of failure and inability to collect post-event information due to site inaccessibility [5]. There has been increasing interest in developing automatic and accurate procedures for landslide and rockfall segmentation and characterization into meaningful hazard entities, aiming at replacing subjective conventional, expensive manual procedures for delineating and assessing those catastrophic events in site specific scales
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call