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This repository contains the dataset used in the associated paper and a jupyter notebook of the automatic workflow.

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3D landslide detection

This repository contains the code used in Bernard et al. (2021) and a jupyter notebook of the 3D point cloud differencing workflow. It also include the Matlab code developped by Philippe Steer using TopoToolBox (Schwangharth and Scherler, 2014) to compute the Closest Deposit distance (CDD_Matlab/).

Requierements

Software : Cloudcompare (V2.11 or later) : http://cloudcompare.org/

Data

(All LiDAR data are under license: https://creativecommons.org/licenses/by/3.0/nz/)

Getting started

The 3D landslide detection workflow can be executed from the jupyter notebook.

References

Lague, D., Brodu, N., & Leroux, J. (2013). Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (NZ). ISPRS journal of photogrammetry and remote sensing, 82, 10-26.

Bernard, T. G., Lague, D., and Steer, P.: Beyond 2D inventories : synoptic 3D landslide volume calculation from repeat LiDAR data, Earth Surf. Dynam. Discuss. [preprint], https://doi.org/10.5194/esurf-2020-73, in review, 2020.

Schwanghart, W. and Scherler, D.: Short Communication: Topo-Toolbox 2 – MATLAB-based software for topographic analysis and modeling in Earth surface sciences, Earth Surf. Dynam., 2, 1–7, https://doi.org/10.5194/esurf-2-1-2014, 2014

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This repository contains the dataset used in the associated paper and a jupyter notebook of the automatic workflow.

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