Search

Topographic change detection is increasingly being used to identify and monitor landslides and other geohazards in support of risk-informed decision-making. Expanding change detection from site specific to regional scales enables increasingly proactive asset management and contributes to improving the resilience of infrastructure to extreme events. It is widely known that change detection precision can be improved by applying three-dimensional algorithms, such as iterative closest point (ICP) and M3C2, directly to raw point clouds. However, this also increases the computational requirements compared to alternatives such as digital elevation model differencing. This study presents a novel graphics processing unit (GPU)-based implementation of the ICP-M3C2 workflow to address this limitation. In the proposed algorithm, point cloud data segments are automatically queued and served to the working GPU, which efficiently performs point cloud processing operations, while the central processing unit (CPU) performs data management operations in parallel. The developed method is estimated to be up to 54 times faster than CPU-based versions of the same algorithm. In this study, we present how the workflow has been applied to six regional-scale landslide identification and monitoring case studies in which landslides are causing the disruption of pipelines, highways, and rail corridors. Overall, in 2021 and 2022, over 17 500 linear km of change detection were processed using the demonstrated method. [ABSTRACT FROM AUTHOR]

Remote sensing techniques can be used to gain a more detailed understanding of hazardous rock slopes along railway corridors that would otherwise be inaccessible. Multiple datasets can be used to identify changes over time and create an inventory of rockfall events including frequency-magnitude relationships for rockfalls sourced on the slope. This study presents a method for using the remotely sensed data to develop inputs to rockfall simulations, which can be used to determine the likelihood of a rockfall impacting the railway tracks given it's source zone location and volume. The results of the simulations can be related to the rockfall inventory to develop modified frequency-magnitude curves presenting a more realistic estimate of the hazard. These methods were developed using the RockyFor3D software and LiDAR and photogrammetry data collected over several years at the White Canyon, British Columbia, Canada, where the CN Rail main line runs along the base of the slope. Rockfalls sourced closer to the tracks were more likely to be deposited on the track or in the ditch, and of these, rockfalls between 0.1 and 10 m3 were the most likely to be deposited. Smaller blocks did not travel far enough to reach the bottom of the slope and larger blocks were deposited past the tracks. Applying the results of the simulations to a database of over 2000 rockfall events, a modified frequency-magnitude can be created, allowing the frequency of rock falls deposited on the railway tracks or in ditches to be determined. Suggestions are made for future development of the methods including refinement of input parameters and extension to other modelling packages. [ABSTRACT FROM AUTHOR]