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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-W15-215-2019
© 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-W15-215-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
20 Aug 2019
LASER-VISUAL-INERTIAL ODOMETRY BASED SOLUTION FOR 3D HERITAGE MODELING: THE SANCTUARY OF THE BLESSED VIRGIN OF TROMPONE
G. P. C. Bronzino
Scuola di Specializzazione in Beni Architettonici e del Paesaggio – 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 Engineerin (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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