Data-Driven flood detection using neural networks
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In: CEUR Workshop Proceedings, Vol. 1984, 2017.
Research output: Journal contributions › Conference article in journal › Research › peer-review
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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 - http://www.scopus.com/inward/record.url?scp=85034961113&partnerID=8YFLogxK
M3 - Conference article in journal
AN - SCOPUS:85034961113
VL - 1984
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
SN - 1613-0073
T2 - Multimedia Benchmark Workshop - MediaEval 2017
Y2 - 13 September 2017 through 15 September 2017
ER -