A Fully Automatic, Interpretable and Adaptive Machine Learning Approach to Map Burned Area from Remote Sensing

A Fully Automatic, Interpretable and Adaptive Machine Learning Approach to Map Burned Area from Remote Sensing

The paper proposes a fully automatic algorithm approach to map burned areas from remote sensing characterized by human interpretable mapping criteria and explainable results. This approach is partially knowledge-driven and partially data-driven. It exploits active fire points to train the fusion fun...

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Bibliographic Details
Main Authors: Daniela Stroppiana, Gloria Bordogna, Matteo Sali, Mirco Boschetti, Giovanna Sona, Pietro Alessandro Brivio
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:ISPRS International Journal of Geo-Information
Subjects:
interpretable machine learning
OWA operators
wildfires
mapping burned areas
explainable fusion
Online Access:https://www.mdpi.com/2220-9964/10/8/546

Internet

https://www.mdpi.com/2220-9964/10/8/546

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