2553163
doi
10.5281/zenodo.2553163
oai:zenodo.org:2553163
user-eu
Farasin, Alessandro
Istituto Superiore Mario Boella and Politecnico di Torino
Skinnemoen, Harald
AnsuR Technologies
Garza, Paolo
Politecnico di Torino
Deep Learning models for passability detection of flooded roads
Lopez-Fuentes, Laura
AnsuR Technologies
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>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.</p>
Zenodo
2018-10-29
info:eu-repo/semantics/conferencePaper
2553162
user-eu
award_title=Improving Resilience to Emergencies through Advanced Cyber Technologies; award_number=700256; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/700256; funder_id=00k4n6c32; funder_name=European Commission;
1579540593.524924
814174
md5:90c02802e744a948d5e78423a283ad03
https://zenodo.org/records/2553163/files/MediaEval_18_paper_48.pdf
public
10.5281/zenodo.2553162
isVersionOf
doi