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Author  |
Alessandro Farasin; Luca Colomba; Giulio Palomba; Giovanni Nini |
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Title |
Supervised Burned Areas Delineation by Means of Sentinel-2 Imagery and Convolutional Neural Networks |
Type |
Conference Article |
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Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
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Volume |
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Issue |
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Pages |
1060-1071 |
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Keywords |
Burned Area Delineation, Sentinel-2, U-Net, CuMedVision1, Convolutional Neural Network, Deep Learning, Supervised Learning, Pixel-wise Segmentation. |
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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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Address |
Politecnico di Torino – DAUIN dept., and LINKS Foundation – DSISA dept.; Politecnico di Torino – DAUIN dept.; LINKS Foundation – DSISA dept.; LINKS Foundation – DSISA dept. |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
978-1-949373-27-96 |
ISBN |
2411-3482 |
Medium |
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Track |
Using Artificial Intelligence to exploit Satellite Data in Risk and Crisis Management |
Expedition |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
alessandro.farasin@polito.it |
Approved |
no |
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Call Number |
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Serial |
2297 |
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Author |
Alessandro Farasin; Paolo Garza |
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Title |
PERCEIVE: Precipitation Data Characterization by means on Frequent Spatio-Temporal Sequences |
Type |
Conference Article |
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Year |
2018 |
Publication |
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2018 |
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Volume |
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Issue |
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Pages |
1081-1088 |
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Keywords |
Spatio-temporal sequence mining, Data characterization |
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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. |
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Publisher |
Rochester Institute of Technology |
Place of Publication |
Rochester, NY (USA) |
Editor |
Kees Boersma; Brian Tomaszeski |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-0-692-12760-5 |
Medium |
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Track |
1st International Workshop on Intelligent Crisis Management Technologies for Climate Events (ICMT) |
Expedition |
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Conference |
ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
2180 |
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Author |
Giulio Palomba; Alessandro Farasin; Claudio Rossi |
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Title |
Sentinel-1 Flood Delineation with Supervised Machine Learning |
Type |
Conference Article |
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Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
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Volume |
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Issue |
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Pages |
1072-1083 |
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Keywords |
Floods, Mapping, Deep Learning, Copernicus EMS, Sentinel-1, SAR. |
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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. |
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Address |
LINKS Foundation – DSISA dept.; Politecnico di Torino – DAUIN dept. and LINKS Foundation – DSISA dept.; LINKS Foundation – DSISA dept. |
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Corporate Author |
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Thesis |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
978-1-949373-27-97 |
ISBN |
2411-3483 |
Medium |
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Track |
Using Artificial Intelligence to exploit Satellite Data in Risk and Crisis Management |
Expedition |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
giulio.palomba@linksfoundation.com |
Approved |
no |
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Call Number |
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Serial |
2298 |
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