Object-based Landslide Assessment in Site-Specific Scale Date: Friday 26th April 2024 Time: 11:39am Date: Tuesday 23 February Time: 3pm GMT The increased development of computer vision technology combined with the increased availability of innovative platforms with ultra-high-resolution sensors mounted, has generated new opportunities and fields for landslide identification and characterization. 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 operational cost and efficient performance pose them as leading platforms for site-specific 3D modelling. In this research, methodology has been developed based on Object-Based Image Analysis (OBIA) and fusion of multivariate data derived from UAV photogrammetry processing in order to take full advantage of the ultra-high-resolution datasets. Analysis demonstrates the potential for accurate and reliable characterization of individual landslide zones within this natural process based on close range photogrammetry for landslide conceptualization.
Date: Tuesday 23 February Time: 3pm GMT The increased development of computer vision technology combined with the increased availability of innovative platforms with ultra-high-resolution sensors mounted, has generated new opportunities and fields for landslide identification and characterization. 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 operational cost and efficient performance pose them as leading platforms for site-specific 3D modelling. In this research, methodology has been developed based on Object-Based Image Analysis (OBIA) and fusion of multivariate data derived from UAV photogrammetry processing in order to take full advantage of the ultra-high-resolution datasets. Analysis demonstrates the potential for accurate and reliable characterization of individual landslide zones within this natural process based on close range photogrammetry for landslide conceptualization.