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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLII-2/W15
https://doi.org/10.5194/isprs-archives-XLII-2-W15-381-2019
© Author(s) 2019. 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-XLII-2-W15-381-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
22 Aug 2019
| 22 Aug 2019
HANDHELD VOLUMETRIC SCANNER FOR 3D PRINTED INTEGRATIONS OF HISTORICAL ELEMENTS: COMPARISON AND RESULTS
D. De Luca
Department of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
M. Del Giudice
Department of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
N. Grasso
Department of Environment Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
F. Matrone
Department of Environment Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
A. Osello
Department of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Department of Environment Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, 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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