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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIII-B3-2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1483-2020
© 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-B3-2020-1483-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
22 Aug 2020
| 22 Aug 2020
UAV IMAGES AND DEEP-LEARNING ALGORITHMS FOR DETECTING FLAVESCENCE DOREE DISEASE IN GRAPEVINE ORCHARDS
M. A. Musci
DIATI, Politecnico di Torino, 10124 Torino, Italy
PIC4SeR, Politecnico di Torino Interdepartmental Centre for Service Robotics, Torino, Italy
C. Persello
Department of Earth Observation Science, Faculty ITC, University of Twente, 7514AE Enschede, The Netherlands
A. M. Lingua
DIATI, Politecnico di Torino, 10124 Torino, Italy
PIC4SeR, Politecnico di Torino Interdepartmental Centre for Service Robotics, Torino, Italy
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Short summary
Short summary
Glaciers are melting worldwide, but direct measurements are spatially sparse. We developed a new model that combines mechanistic physical rules with machine learning to better estimate yearly gains and losses of ice mass across Europe. By learning local corrections to a physical melt model, the machine learning component improves accuracy and works well on glaciers not used for training. This will help produce more reliable glacier assessments and support future climate studies.
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