Machine learning and best fit approach to map lava flows from space

Amato, E. (2022) Machine learning and best fit approach to map lava flows from space. Il nuovo cimento C, 45 (4). pp. 1-12. ISSN 1826-9885

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Abstract

Estimating the areal coverage of newly erupted lava is both a crucial component of volcano monitoring and a powerful tool for characterizing lava flow emplacement behavior. Here, it is presented a methodology based on machine learning, developed in the Google Earth Engine platform, and best fitting approach, to map the extent of lava flows solely using freely available and open-source data from space-borne instruments. Radar and optical satellite data are used as input to different machine learning techniques and best fitting models, so that the methodology is able to operate in all weather conditions. The satellite-driven approach has been successfully used for mapping lava flows automatically during the long sequence of summit eruptions occurred at Mt. Etna between December 2020 and October 2021.

Item Type:	Article
Subjects:	500 Scienze naturali e Matematica > 530 Fisica
Depositing User:	Marina Spanti
Date Deposited:	06 Sep 2022 11:36
Last Modified:	06 Sep 2022 11:36
URI:	http://eprints.bice.rm.cnr.it/id/eprint/21989

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