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 -