Articles | Volume 19, issue 12
https://doi.org/10.5194/amt-19-4255-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-4255-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jun 2026
Research article |
| 30 Jun 2026
| 30 Jun 2026
Leveraging machine learning techniques and SEVIRI data to detect volcanic clouds composed of ash, ice, and SO2
INGV, Istituto Nazionale di Geofisica e Vulcanologia, ONT, 00143 Rome, Italy
Lorenzo Guerrieri
INGV, Istituto Nazionale di Geofisica e Vulcanologia, ONT, 00143 Rome, Italy
Stefano Corradini
INGV, Istituto Nazionale di Geofisica e Vulcanologia, ONT, 00143 Rome, Italy
Matteo Picchiani
ASI, Italian Space Agency, 00133 Rome, Italy
Luca Merucci
INGV, Istituto Nazionale di Geofisica e Vulcanologia, ONT, 00143 Rome, Italy
Dario Stelitano
INGV, Istituto Nazionale di Geofisica e Vulcanologia, ONT, 00143 Rome, Italy
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Short summary
This work presents the development of a neural network model for detecting volcanic clouds under challenging conditions, where the cloud contains not only ash but also sulfur dioxide and ice. The presence of ice complicates detection and often leads to failures in traditional methods. Our results show that the neural network improves detection performance and supports the automatic volcanic cloud monitoring, which is crucial for aviation safety.
This work presents the development of a neural network model for detecting volcanic clouds under...
