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Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery
Rapid response to natural hazards, such as floods, is essential to mitigate loss of life and the reduction of suffering. For emergency response teams, access to timely and accurate data is essential. Satellite imagery offers a rich source of information which can be analysed to help determine region...
Autores principales: | , , , |
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Lenguaje: | eng |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.3390/rs12162532 http://cds.cern.ch/record/2799352 |
_version_ | 1780972537921929216 |
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author | Nemni, Edoardo Bullock, Joseph Belabbes, Samir Bromley, Lars |
author_facet | Nemni, Edoardo Bullock, Joseph Belabbes, Samir Bromley, Lars |
author_sort | Nemni, Edoardo |
collection | CERN |
description | Rapid response to natural hazards, such as floods, is essential to mitigate loss of life and
the reduction of suffering. For emergency response teams, access to timely and accurate data is
essential. Satellite imagery offers a rich source of information which can be analysed to help determine
regions affected by a disaster. Much remote sensing flood analysis is semi-automated, with time
consuming manual components requiring hours to complete. In this study, we present a fully
automated approach to the rapid flood mapping currently carried out by many non-governmental,
national and international organisations. We design a Convolutional Neural Network (CNN) based
method which isolates the flooded pixels in freely available Copernicus Sentinel-1 Synthetic Aperture
Radar (SAR) imagery, requiring no optical bands and minimal pre-processing. We test a variety of
CNN architectures and train our models on flood masks generated using a combination of classical
semi-automated techniques and extensive manual cleaning and visual inspection. Our methodology
reduces the time required to develop a flood map by 80%, while achieving strong performance over a
wide range of locations and environmental conditions. Given the open-source data and the minimal
image cleaning required, this methodology can also be integrated into end-to-end pipelines for more
timely and continuous flood monitoring. |
id | cern-2799352 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2020 |
record_format | invenio |
spelling | cern-27993522022-01-25T11:03:45Zdoi:10.3390/rs12162532http://cds.cern.ch/record/2799352engNemni, EdoardoBullock, JosephBelabbes, SamirBromley, LarsFully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar ImageryEngineeringRapid response to natural hazards, such as floods, is essential to mitigate loss of life and the reduction of suffering. For emergency response teams, access to timely and accurate data is essential. Satellite imagery offers a rich source of information which can be analysed to help determine regions affected by a disaster. Much remote sensing flood analysis is semi-automated, with time consuming manual components requiring hours to complete. In this study, we present a fully automated approach to the rapid flood mapping currently carried out by many non-governmental, national and international organisations. We design a Convolutional Neural Network (CNN) based method which isolates the flooded pixels in freely available Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) imagery, requiring no optical bands and minimal pre-processing. We test a variety of CNN architectures and train our models on flood masks generated using a combination of classical semi-automated techniques and extensive manual cleaning and visual inspection. Our methodology reduces the time required to develop a flood map by 80%, while achieving strong performance over a wide range of locations and environmental conditions. Given the open-source data and the minimal image cleaning required, this methodology can also be integrated into end-to-end pipelines for more timely and continuous flood monitoring.oai:cds.cern.ch:27993522020 |
spellingShingle | Engineering Nemni, Edoardo Bullock, Joseph Belabbes, Samir Bromley, Lars Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery |
title | Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery |
title_full | Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery |
title_fullStr | Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery |
title_full_unstemmed | Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery |
title_short | Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery |
title_sort | fully convolutional neural network for rapid flood segmentation in synthetic aperture radar imagery |
topic | Engineering |
url | https://dx.doi.org/10.3390/rs12162532 http://cds.cern.ch/record/2799352 |
work_keys_str_mv | AT nemniedoardo fullyconvolutionalneuralnetworkforrapidfloodsegmentationinsyntheticapertureradarimagery AT bullockjoseph fullyconvolutionalneuralnetworkforrapidfloodsegmentationinsyntheticapertureradarimagery AT belabbessamir fullyconvolutionalneuralnetworkforrapidfloodsegmentationinsyntheticapertureradarimagery AT bromleylars fullyconvolutionalneuralnetworkforrapidfloodsegmentationinsyntheticapertureradarimagery |