Wildfire events are increasingly threatening our lands, cities, and lives. To contrast this phenomenon and to limit its damages, governments around the globe are trying to find proper counter-measures, identifying prevention and monitoring as two key factors to reduce wildfires impact worldwide. In this work, we propose two deep convolutional neural networks to automatically detect and delineate burned areas from satellite acquisitions, assessing their performances at scale using validated maps of burned areas of historical wildfires. We demonstrate that the proposed networks substantially improve the burned area delineation accuracy over conventional methods.

Supervised Burned Areas delineation by means of Sentinel-2 imagery and Convolutional Neural Networks / Farasin, Alessandro; Colomba, Luca; Palomba, Giulio; Nini, Giovanni; Rossi, Claudio. - ELETTRONICO. - (2020), pp. 1060-1071. ((Intervento presentato al convegno 17th International Conference on Information Systems for Crisis Response And Management (ISCRAM 2020) tenutosi a Blacksburg, Virginia (USA) nel May 24-27, 2020.

Supervised Burned Areas delineation by means of Sentinel-2 imagery and Convolutional Neural Networks

Farasin, Alessandro;Colomba, Luca;Rossi, Claudio
2020

Abstract

Wildfire events are increasingly threatening our lands, cities, and lives. To contrast this phenomenon and to limit its damages, governments around the globe are trying to find proper counter-measures, identifying prevention and monitoring as two key factors to reduce wildfires impact worldwide. In this work, we propose two deep convolutional neural networks to automatically detect and delineate burned areas from satellite acquisitions, assessing their performances at scale using validated maps of burned areas of historical wildfires. We demonstrate that the proposed networks substantially improve the burned area delineation accuracy over conventional methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2838032