Schovanec, H, Walton, G, Kromer, R orcid.org/0000-0002-6036-0919 et al. (1 more author) (2021) Development of Improved Semi-Automated Processing Algorithms for the Creation of Rockfall Databases. Remote Sensing, 13 (8). ISSN 2072-4292
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | rockfall; lidar; terrestrial laser scanning; TLS; point clouds; processing |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Institute for Applied Geosciences (IAG) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Aug 2021 11:53 |
Last Modified: | 13 Aug 2021 11:53 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/rs13081479 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177063 |