Abstract
While terrestrial laser scanning and photogrammetry provide high quality point cloud data that can be used for rock slope monitoring, their increased use has overwhelmed current data analysis methodologies. Accordingly, point cloud processing workflows have previously been developed to automate many processes, including point cloud alignment, generation of change maps and clustering. However, for more specialized rock slope analyses (e.g., generating a rockfall database), the creation of more specialized processing routines and algorithms is necessary. More specialized algorithms include the reconstruction of rockfall volumes from clusters and points and automatic classification of those volumes are both processing steps required to automate the generation of a rockfall database. We propose a workflow that can automate all steps of the point cloud processing workflow. In this study, we detail adaptions to commonly used algorithms for rockfall monitoring use cases, such as Multiscale Model to Model Cloud Comparison (M3C2). This workflow details the entire processing pipeline for rockfall database generation using terrestrial laser scanning.
Highlights
Creation of Rockfall Databases.Ease of use and reduction of cost has led to an increased application of terrestrial laser scanning (TLS) and photogrammetry in a wide range of earth science applications
“modified M3C2”, there are some manual, one-time processes required to setup the workflow the automated the 4D filtering algorithm proposed by Kromer et al [23]
12a).ofTwo planes with noisy an automated compared process thatto canM3C2 be runusing in the synthetic background, including repetitions the inrandomly generated around them were populated with varying dividual points alignment types
Summary
Ease of use and reduction of cost has led to an increased application of terrestrial laser scanning (TLS) and photogrammetry in a wide range of earth science applications. A review of earth science research using photogrammetry and terrestrial laser scanning by Telling et al [1] notes that the number of publications using and citing methods including. TLS has increased at a near exponential rate since 1995. While these methods result in the same type of raw data (e.g., using a lidar scanner to acquire point clouds or using structure from motion to construct point clouds from images), the applications in earth science are incredibly diverse. TLS and photogrammetry have been used to better understand erosional and depositional processes [5,6,7,8]. TLS and photogrammetry have been implemented to perform rock slope characterization and monitoring for rockfall and landslide hazards [1,9]
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