Data-Driven flood detection using neural networks

Keiller Nogueira; Samuel G. Fadel; Icaro C. Dourado; Rafael O. De Werneck; Javier A. Munoz; Otavio A.B. Penatti; Rodrigo T. Calumby; Lin Tzy Li; Jefersson A.Dos Santos; Ricardo Da S. Torres

Data-Driven flood detection using neural networks

Keiller Nogueira
, Samuel G. Fadel
, Icaro C. Dourado
, Rafael O. De Werneck
, Javier A. Munoz
, Otavio A.B. Penatti
, Rodrigo T. Calumby
, Lin Tzy Li
, Jefersson A.Dos Santos
, Ricardo Da S. Torres

Research output: Journal contributions › Conference article in journal › Research › peer-review

4 Citations (Scopus)

Abstract

This paper describes the approaches used by our team (MultiBrasil) for the Multimedia Satellite Task at MediaEval 2017. For both disaster image retrieval and flood-detection in satellite images, we employ neural networks for end-to-end learning. Specifically, for the first subtask, we exploit Convolutional Networks and Relation Networks while, for the latter, dilated Convolutional Networks were employed.

Original language	English
Journal	CEUR Workshop Proceedings
Volume	1984
Number of pages	3
ISSN	1613-0073
Publication status	Published - 2017
Externally published	Yes
Event	Multimedia Benchmark Workshop - MediaEval 2017 - Trinity College, Dublin, Ireland Duration: 13.09.2017 → 15.09.2017 http://www.multimediaeval.org/mediaeval2017/

Research areas and keywords

Business informatics

ASJC Scopus Subject Areas

Computer Science(all)

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http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_39.pdfLicence: In Copyright

Cite this

@article{595672114b1442c8acb79bfbd743e977,

title = "Data-Driven flood detection using neural networks",

abstract = "This paper describes the approaches used by our team (MultiBrasil) for the Multimedia Satellite Task at MediaEval 2017. For both disaster image retrieval and flood-detection in satellite images, we employ neural networks for end-to-end learning. Specifically, for the first subtask, we exploit Convolutional Networks and Relation Networks while, for the latter, dilated Convolutional Networks were employed.",

keywords = "Business informatics",

author = "Keiller Nogueira and Fadel, \{Samuel G.\} and Dourado, \{Icaro C.\} and \{De Werneck\}, \{Rafael O.\} and Munoz, \{Javier A.\} and Penatti, \{Otavio A.B.\} and Calumby, \{Rodrigo T.\} and Li, \{Lin Tzy\} and Santos, \{Jefersson A.Dos\} and Torres, \{Ricardo Da S.\}",

year = "2017",

language = "English",

volume = "1984",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "CEUR-WS",

note = "Multimedia Benchmark Workshop - MediaEval 2017 ; Conference date: 13-09-2017 Through 15-09-2017",

url = "http://www.multimediaeval.org/mediaeval2017/",

}

TY - JOUR

T1 - Data-Driven flood detection using neural networks

AU - Nogueira, Keiller

AU - Fadel, Samuel G.

AU - Dourado, Icaro C.

AU - De Werneck, Rafael O.

AU - Munoz, Javier A.

AU - Penatti, Otavio A.B.

AU - Calumby, Rodrigo T.

AU - Li, Lin Tzy

AU - Santos, Jefersson A.Dos

AU - Torres, Ricardo Da S.

PY - 2017

Y1 - 2017

N2 - This paper describes the approaches used by our team (MultiBrasil) for the Multimedia Satellite Task at MediaEval 2017. For both disaster image retrieval and flood-detection in satellite images, we employ neural networks for end-to-end learning. Specifically, for the first subtask, we exploit Convolutional Networks and Relation Networks while, for the latter, dilated Convolutional Networks were employed.

AB - This paper describes the approaches used by our team (MultiBrasil) for the Multimedia Satellite Task at MediaEval 2017. For both disaster image retrieval and flood-detection in satellite images, we employ neural networks for end-to-end learning. Specifically, for the first subtask, we exploit Convolutional Networks and Relation Networks while, for the latter, dilated Convolutional Networks were employed.

KW - Business informatics

UR - https://www.scopus.com/pages/publications/85034961113

M3 - Conference article in journal

AN - SCOPUS:85034961113

SN - 1613-0073

VL - 1984

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - Multimedia Benchmark Workshop - MediaEval 2017

Y2 - 13 September 2017 through 15 September 2017

ER -

Data-Driven flood detection using neural networks

Abstract

Research areas and keywords

ASJC Scopus Subject Areas

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