Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction

Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus,...

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Autores principales:	Simone Monaco, Salvatore Greco, Alessandro Farasin, Luca Colomba, Daniele Apiletti, Paolo Garza, Tania Cerquitelli, Elena Baralis
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	wildfire severity prediction deep neural networks multi-channel attention-based analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/ab07aa6e9ce145faab961e7300fb4986
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spelling	oai:doaj.org-article:ab07aa6e9ce145faab961e7300fb49862021-11-25T16:43:30ZAttention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction10.3390/app1122110602076-3417https://doaj.org/article/ab07aa6e9ce145faab961e7300fb49862021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/11060https://doaj.org/toc/2076-3417Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact and plan the restoration process, a rapid intervention by authorities is needed, which can be enhanced by the use of satellite imagery and automatic burned area delineation methodologies, accelerating the response and the decision-making processes. In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework. Experimental results compare different model architectures and loss functions on a very large real-world Sentinel2 satellite dataset. Furthermore, a novel multi-channel attention-based analysis is presented to uncover the prediction behaviour and provide model interpretability. A perturbation mechanism is applied to an attention-based DS-UNet to evaluate the contribution of different domain-driven groups of channels to the severity estimation problem.Simone MonacoSalvatore GrecoAlessandro FarasinLuca ColombaDaniele ApilettiPaolo GarzaTania CerquitelliElena BaralisMDPI AGarticlewildfire severity predictiondeep neural networksmulti-channel attention-based analysisTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 11060, p 11060 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wildfire severity prediction deep neural networks multi-channel attention-based analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	wildfire severity prediction deep neural networks multi-channel attention-based analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Simone Monaco Salvatore Greco Alessandro Farasin Luca Colomba Daniele Apiletti Paolo Garza Tania Cerquitelli Elena Baralis Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
description	Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact and plan the restoration process, a rapid intervention by authorities is needed, which can be enhanced by the use of satellite imagery and automatic burned area delineation methodologies, accelerating the response and the decision-making processes. In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework. Experimental results compare different model architectures and loss functions on a very large real-world Sentinel2 satellite dataset. Furthermore, a novel multi-channel attention-based analysis is presented to uncover the prediction behaviour and provide model interpretability. A perturbation mechanism is applied to an attention-based DS-UNet to evaluate the contribution of different domain-driven groups of channels to the severity estimation problem.
format	article
author	Simone Monaco Salvatore Greco Alessandro Farasin Luca Colomba Daniele Apiletti Paolo Garza Tania Cerquitelli Elena Baralis
author_facet	Simone Monaco Salvatore Greco Alessandro Farasin Luca Colomba Daniele Apiletti Paolo Garza Tania Cerquitelli Elena Baralis
author_sort	Simone Monaco
title	Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
title_short	Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
title_full	Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
title_fullStr	Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
title_full_unstemmed	Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
title_sort	attention to fires: multi-channel deep learning models for wildfire severity prediction
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ab07aa6e9ce145faab961e7300fb4986
work_keys_str_mv	AT simonemonaco attentiontofiresmultichanneldeeplearningmodelsforwildfireseverityprediction AT salvatoregreco attentiontofiresmultichanneldeeplearningmodelsforwildfireseverityprediction AT alessandrofarasin attentiontofiresmultichanneldeeplearningmodelsforwildfireseverityprediction AT lucacolomba attentiontofiresmultichanneldeeplearningmodelsforwildfireseverityprediction AT danieleapiletti attentiontofiresmultichanneldeeplearningmodelsforwildfireseverityprediction AT paologarza attentiontofiresmultichanneldeeplearningmodelsforwildfireseverityprediction AT taniacerquitelli attentiontofiresmultichanneldeeplearningmodelsforwildfireseverityprediction AT elenabaralis attentiontofiresmultichanneldeeplearningmodelsforwildfireseverityprediction
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Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction

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