Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 24
Articles | Volume 15, issue 24
https://doi.org/10.5194/amt-15-7195-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-15-7195-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 24
Research article
 | 
14 Dec 2022
Research article |  | 14 Dec 2022

Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case

Ilaria Petracca, Davide De Santis, Matteo Picchiani, Stefano Corradini, Lorenzo Guerrieri, Fred Prata, Luca Merucci, Dario Stelitano, Fabio Del Frate, Giorgia Salvucci, and Giovanni Schiavon

Viewed

Total article views: 2,229 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,560 594 75 2,229 67 61
  • HTML: 1,560
  • PDF: 594
  • XML: 75
  • Total: 2,229
  • BibTeX: 67
  • EndNote: 61
Views and downloads (calculated since 10 Jun 2022)
Cumulative views and downloads (calculated since 10 Jun 2022)

Viewed (geographical distribution)

Total article views: 2,229 (including HTML, PDF, and XML) Thereof 2,283 with geography defined and -54 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Nov 2024
Download
Short summary
The authors propose a near-real-time procedure for the detection of volcanic clouds by means of Sentinel-3 satellite data and neural networks. The algorithm results in an automatic image classification where ashy pixels are distinguished from other surfaces with remarkable accuracy. The model is considerably faster if compared to other approaches which are time consuming, case specific, and not automatic. The algorithm can be significantly helpful for emergency management during eruption events.
Read more
Special issue
Satellite observations, in situ measurements and model simulations...