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Proceedings of the ACM on Human-Computer Interaction
Article . 2022 . Peer-reviewed
License: CC BY NC SA
Data sources: Crossref
DBLP
Article . 2022
Data sources: DBLP
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Venice Was Flooding ... One Tweet at a Time

Authors: Valerio Lorini; Paola Rufolo; Carlos Castillo 0001;

Venice Was Flooding ... One Tweet at a Time

Abstract

Before urban flooding actually happens, weather forecasts with varying degrees of precision are available to emergency managers. In the aftermath of the event, authoritative information including Earth Observation (EO) data can be used to estimate precisely the flood extent, possibly after several hours. This study aims to determine how social media information can reduce the inherent uncertainty of the information in the immediate aftermath of an urban flood event. Specifically, the study investigates how to collect relevant social media images and to interpolate such data in order to create a map. The premise of the study is that social media platforms, when combined with digital surface models, can provide control points for creating a reliable near real-time estimate of the flood extent. In the study, we compared a flood extent map derived from social media with that derived from authoritative altimetry data during one of the worst floods to hit Venice, which occurred in November 2019. The results of the experiments show a good overall accuracy using several digital surface models. Given the global coverage of such models and the low resources required, we think the methodology proposed could be beneficial for emergency managers. Specifically, we describe how a flood extent map can be made available within 24 h, or even less, after urban flooding strikes a densely inhabited area, where data generated by the public are available.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Average
Average
hybrid