10.5281/zenodo.2553163
https://zenodo.org/records/2553163
oai:zenodo.org:2553163
Lopez-Fuentes, Laura
Laura
Lopez-Fuentes
AnsuR Technologies
Farasin, Alessandro
Alessandro
Farasin
Istituto Superiore Mario Boella and Politecnico di Torino
Skinnemoen, Harald
Harald
Skinnemoen
AnsuR Technologies
Garza, Paolo
Paolo
Garza
Politecnico di Torino
Deep Learning models for passability detection of flooded roads
Zenodo
2018
2018-10-29
eng
10.5281/zenodo.2553162
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
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.
European Commission
10.13039/501100000780
700256
Improving Resilience to Emergencies through Advanced Cyber Technologies