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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIII-B4-2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-741-2020
© Author(s) 2020. 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-XLIII-B4-2020-741-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
25 Aug 2020
| 25 Aug 2020
TACK PROJECT: TUNNEL AND BRIDGE AUTOMATIC CRACK MONITORING USING DEEP LEARNING AND PHOTOGRAMMETRY
V. Belloni
Geodesy and Geomatics Division (DICEA), Sapienza University of Rome, Rome, Italy
A. Sjölander
Division of Concrete Structures, KTH - Royal Institute of Technology, Stockholm, Sweden
R. Ravanelli
Geodesy and Geomatics Division (DICEA), Sapienza University of Rome, Rome, Italy
M. Crespi
Geodesy and Geomatics Division (DICEA), Sapienza University of Rome, Rome, Italy
A. Nascetti
Geoinformatics Division, KTH - Royal Institute of Technology, Stockholm, Sweden
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Short summary
Short summary
The estimation of precipitable water vapour (W) content is of great interest in both meteorological and climatological studies. Sun photometers allowed the development of W automatic estimations with high temporal resolution. A new methodology, based on the hypothesis that the calibration parameters characterizing the atmospheric transmittance are dependent on vertical profiles of temperature, air pressure and moisture typical of each measurement site, has been presented providing good results.
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