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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-2-2024
https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-401-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-401-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
11 Jun 2024
| 11 Jun 2024
Three-dimensional modelling of artificial caves for geomechanical analysis
Alessandra Spadaro
Dept. of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Turin, Italy
Marco Piras
Dept. of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Turin, Italy
Nives Grasso
Dept. of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Turin, Italy
Piernicola Lollino
Department of Earth and Geoenvironmental Sciences, University of Bari, Bari, Italy
Alessandro Parisi
Association “Gravina Sotterranea” – Geophysical Applications Processing - GAP s.r.l., Bari, Italy
Daniele Giordan
Research Institute for Geo-hydrological Protection (IRPI), Italian National Research Council, Turin, Italy
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Revised manuscript not accepted
Short summary
Short summary
We compare three different instruments used to collect snow depth, i.e., photogrammetric surveys using Unmanned Aerial Systems (UAS), a 3D laser scanning, and manual probing. The relatively high density of manual data (135 pt over 6700 m2, i.e., 2 pt/100 m2) enables to assess the performance of UAS in capturing the marked spatial variability of snow. Results suggest that UAS represent a competitive choice among existing techniques for high-precision, high-resolution remote sensing of snow.
I. Aicardi, F. Chiabrando, N. Grasso, A. M. Lingua, F. Noardo, and A. Spanò
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 835–842, https://doi.org/10.5194/isprs-archives-XLI-B1-835-2016, https://doi.org/10.5194/isprs-archives-XLI-B1-835-2016, 2016
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