Version 1
: Received: 10 July 2024 / Approved: 11 July 2024 / Online: 11 July 2024 (04:52:12 CEST)
How to cite:
Zhipeng, L.; Gabrieli, F.; Pol, A.; Brezzi, L. Automated Photogrammetric Tool for Landslide Recognition and Volume Calculation Using Time-Lapse Imagery. Preprints2024, 2024070918. https://doi.org/10.20944/preprints202407.0918.v1
Zhipeng, L.; Gabrieli, F.; Pol, A.; Brezzi, L. Automated Photogrammetric Tool for Landslide Recognition and Volume Calculation Using Time-Lapse Imagery. Preprints 2024, 2024070918. https://doi.org/10.20944/preprints202407.0918.v1
Zhipeng, L.; Gabrieli, F.; Pol, A.; Brezzi, L. Automated Photogrammetric Tool for Landslide Recognition and Volume Calculation Using Time-Lapse Imagery. Preprints2024, 2024070918. https://doi.org/10.20944/preprints202407.0918.v1
APA Style
Zhipeng, L., Gabrieli, F., Pol, A., & Brezzi, L. (2024). Automated Photogrammetric Tool for Landslide Recognition and Volume Calculation Using Time-Lapse Imagery. Preprints. https://doi.org/10.20944/preprints202407.0918.v1
Chicago/Turabian Style
Zhipeng, L., Antonio Pol and Lorenzo Brezzi. 2024 "Automated Photogrammetric Tool for Landslide Recognition and Volume Calculation Using Time-Lapse Imagery" Preprints. https://doi.org/10.20944/preprints202407.0918.v1
Abstract
Digital photogrammetry has attracted widespread attention in the field of geotechnical and geological survey due to its low-cost, ease of use and contactless mode. In this work, with the purpose of studying the progressive block surficial detachments of a landslide we developed a monitoring system based on fixed multi-view time-lapse cameras. Thanks to a new-developed photogrammetric algorithm based on the comparison of photo sequences through a structural similarity metric and the computation of the disparity map of two convergent views we can quickly detect the occurrence of collapse events, determine their location and calculate the collapse volume. With the field data obtained at the Perarolo landslide site (Belluno Province, Italy), we have preliminary tested the effectiveness of the algorithm and its accuracy in the volume calculation. The method of quickly and automatically obtaining collapse information proposed in this paper can extend the potentials of landslide monitoring system based on videos or photo sequence and it will be of great significance for further research on the link between the frequency of collapse events and the driving factors.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
