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Supervised Burned Areas Delineation by Means of Sentinel-2 Imagery and Convolutional Neural Networks
Alessandro Farasin
author
Luca Colomba
author
Giulio Palomba
author
Giovanni Nini
author
2020
Virginia Tech
Blacksburg, VA (USA)
English
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.
Burned Area Delineation
Sentinel-2
U-Net
CuMedVision1
Convolutional Neural Network
Deep Learning
Supervised Learning
Pixel-wise Segmentation.
alessandro.farasin@polito.it
exported from refbase (http://idl.iscram.org/show.php?record=2297), last updated on Mon, 29 Jun 2020 08:02:59 +0200
text
http://idl.iscram.org/files/alessandrofarasin/2020/2297_AlessandroFarasin_etal2020.pdf
AlessandroFarasin_etal2020
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management
Iscram 2020
Amanda Hughes
editor
Fiona McNeill
editor
Christopher W. Zobel
editor
17th International Conference on Information Systems for Crisis Response and Management
2020
Virginia Tech
Blacksburg, VA (USA)
conference publication
1060
1071
2411-3482
978-1-949373-27-96
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