Data-Driven flood detection using neural networks

Publikation: Beiträge in ZeitschriftenKonferenzaufsätze in FachzeitschriftenForschungbegutachtet

Standard

Data-Driven flood detection using neural networks. / Nogueira, Keiller; Fadel, Samuel G.; Dourado, Icaro C. et al.
in: CEUR Workshop Proceedings, Jahrgang 1984, 2017.

Publikation: Beiträge in ZeitschriftenKonferenzaufsätze in FachzeitschriftenForschungbegutachtet

Harvard

Nogueira, K, Fadel, SG, Dourado, IC, De Werneck, RO, Munoz, JA, Penatti, OAB, Calumby, RT, Li, LT, Santos, JAD & Torres, RDS 2017, 'Data-Driven flood detection using neural networks', CEUR Workshop Proceedings, Jg. 1984. <http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_39.pdf>

APA

Nogueira, K., Fadel, S. G., Dourado, I. C., De Werneck, R. O., Munoz, J. A., Penatti, O. A. B., Calumby, R. T., Li, L. T., Santos, J. A. D., & Torres, R. D. S. (2017). Data-Driven flood detection using neural networks. CEUR Workshop Proceedings, 1984. http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_39.pdf

Vancouver

Nogueira K, Fadel SG, Dourado IC, De Werneck RO, Munoz JA, Penatti OAB et al. Data-Driven flood detection using neural networks. CEUR Workshop Proceedings. 2017;1984.

Bibtex

@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 = "Rheinisch-Westfaelische Technische Hochschule Aachen",
note = "Multimedia Benchmark Workshop - MediaEval 2017 ; Conference date: 13-09-2017 Through 15-09-2017",
url = "http://www.multimediaeval.org/mediaeval2017/",

}

RIS

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 -

Links