|   | 
Details
   web
Records
Author Alessandro Farasin; Paolo Garza
Title (up) PERCEIVE: Precipitation Data Characterization by means on Frequent Spatio-Temporal Sequences Type Conference Article
Year 2018 Publication ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2018
Volume Issue Pages 1081-1088
Keywords Spatio-temporal sequence mining, Data characterization
Abstract Nowadays large amounts of climatology data, including daily precipitation data, are collected by means of sensors located in different locations of the world. The data driven analysis of these large data sets by means of scalable machine learning and data mining techniques allows extracting interesting knowledge from data, inferring interesting patterns and correlations among sets of spatio-temporal events and characterizing them. In this paper, we describe the PERCEIVE framework. PERCEIVE is a data-driven framework based on frequent spatio-temporal sequences and aims at extracting frequent correlations among spatio-temporal precipitation events. It is implemented by using R and Apache Spark, for scalability reasons, and provides also a visualization module that can be used to intuitively show the extracted patterns. A preliminary set of experiments show the efficiency and the effectiveness of PERCEIVE.
Address
Corporate Author Thesis
Publisher Rochester Institute of Technology Place of Publication Rochester, NY (USA) Editor Kees Boersma; Brian Tomaszeski
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-0-692-12760-5 Medium
Track 1st International Workshop on Intelligent Crisis Management Technologies for Climate Events (ICMT) Expedition Conference ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 2180
Share this record to Facebook
 

 
Author Giulio Palomba; Alessandro Farasin; Claudio Rossi
Title (up) 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 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
Share this record to Facebook
 

 
Author Alessandro Farasin; Luca Colomba; Giulio Palomba; Giovanni Nini
Title (up) 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 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
Share this record to Facebook