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Deep Learning models for passability detection of flooded roads

Authors: Laura Lopez-Fuentes; Alessandro Farasin; Harald Skinnemoen; Paolo Garza;

Deep Learning models for passability detection of flooded roads

Abstract

In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter.We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task.

Country
Italy
Keywords

passability detection, multimedia, flood detection, Multimedia benchmarking, Twitter, Deep learning; flood detection; passability detection; Twitter; multimedia; Multimedia benchmarking, Deep learning

16 references, page 1 of 2

[1] 2018. MediaEval 2018 Multimedia Satellite Task. http://www. multimediaeval.org/mediaeval2018/multimediasatellite/. (2018). Data released: 31 May 2018.

[2] Flavia Sofia Acerbo and Claudio Rossi. 2017. Filtering informative tweets during emergencies: a machine learning approach. In Proceedings of the First CoNEXT Workshop on ICT Tools for Emergency Networks and DisastEr Relief. ACM, 1-6.

[3] Federico Angaramo and Claudio Rossi. 2017. Online clustering and classification for real-time event detection in Twitter. (2017).

[4] Benjamin Bischke, Patrick Helber, Zhengyu Zhao, Jens de Bruijn, and Damian Borth. The Multimedia Satellite Task at MediaEval 2018: Emergency Response for Flooding Events. In Proc. of the MediaEval 2018 Workshop (Oct. 29-31, 2018). Sophia-Antipolis, France.

[5] Tom Brouwer, Dirk Eilander, Arnejan Van Loenen, Martijn J Booij, Kathelijne M Wijnberg, Jan S Verkade, and Jurjen Wagemaker. 2017. Probabilistic flood extent estimates from social media flood observations. Natural Hazards & Earth System Sciences 17, 5 (2017).

[6] Yinghua He, Hong Wang, and Bo Zhang. 2004. Color-based road detection in urban trafic scenes. IEEE Transactions on intelligent transportation systems 5, 4 (2004), 309-318.

[7] Christopher D. Manning Jefrey Pennington, Richard Socher. 2018. GloVe: Global Vectors for Word Representation. (2018). https://nlp. stanford.edu/projects/glove/

[8] Victor Klemas. 2014. Remote sensing of floods and flood-prone areas: an overview. Journal of Coastal Research 31, 4 (2014), 1005-1013.

[9] Hui Kong, Jean-Yves Audibert, and Jean Ponce. 2010. General road detection from a single image. IEEE Transactions on Image Processing 19, 8 (2010), 2211-2220.

[10] Laura Lopez-Fuentes, Joost van de Weijer, Marc Bolanos, and Harald Skinnemoen. 2017. Multi-modal deep learning approach for flood detection. In Proc. of the MediaEval 2017 Workshop (Sept. 13-15, 2017). Dublin, Ireland.

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