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.

Deep Learning Models for Passability Detection of Flooded Roads / Lopez-Fuentes, Laura; Farasin, Alessandro; Skinnemoen, Harald; Garza, Paolo. - ELETTRONICO. - Vol-2283:(2018). ((Intervento presentato al convegno Working Notes Proceedings of the MediaEval 2018 Workshop, Sophia Antipolis, France, 29-31 October 2018 tenutosi a Sophia Antipolis, France. nel 29/10/2018 - 31/10/2018.

Deep Learning Models for Passability Detection of Flooded Roads

Alessandro Farasin;Paolo Garza
2018

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2719409
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