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Author Giulio Palomba; Alessandro Farasin; Claudio Rossi pdf  isbn
openurl 
  Title Sentinel-1 Flood Delineation with Supervised Machine Learning Type Conference Article
  Year 2020 Publication ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages (down) 1072-1083  
  Keywords Floods, Mapping, Deep Learning, Copernicus EMS, Sentinel-1, SAR.  
  Abstract Floods are one of the major natural hazards in terms of affected people and economic damages. The increasing and often uncontrolled urban sprawl together with climate change effects will make future floods more frequent and impacting. An accurate flood mapping is of paramount importance in order to update hazard and risk maps and to plan prevention measures. In this paper, we propose the use of a supervised machine learning approach for flood delineation from satellite data. We train and evaluate the proposed algorithm using Sentinel-1 acquisition and certified flood delineation maps produced by the Copernicus Emergency Management Service across different geographical regions in Europe, achieving increased performances against previously proposed supervised machine learning approaches for flood mapping.  
  Address LINKS Foundation – DSISA dept.; Politecnico di Torino – DAUIN dept. and LINKS Foundation – DSISA dept.; LINKS Foundation – DSISA dept.  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-97 ISBN 2411-3483 Medium  
  Track Using Artificial Intelligence to exploit Satellite Data in Risk and Crisis Management Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes giulio.palomba@linksfoundation.com Approved no  
  Call Number Serial 2298  
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Author Alessandro Farasin; Luca Colomba; Giulio Palomba; Giovanni Nini pdf  isbn
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  Title Supervised Burned Areas Delineation by Means of Sentinel-2 Imagery and Convolutional Neural Networks Type Conference Article
  Year 2020 Publication ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages (down) 1060-1071  
  Keywords Burned Area Delineation, Sentinel-2, U-Net, CuMedVision1, Convolutional Neural Network, Deep Learning, Supervised Learning, Pixel-wise Segmentation.  
  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.  
  Address Politecnico di Torino – DAUIN dept., and LINKS Foundation – DSISA dept.; Politecnico di Torino – DAUIN dept.; LINKS Foundation – DSISA dept.; LINKS Foundation – DSISA dept.  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-96 ISBN 2411-3482 Medium  
  Track Using Artificial Intelligence to exploit Satellite Data in Risk and Crisis Management Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes alessandro.farasin@polito.it Approved no  
  Call Number Serial 2297  
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