research-article

Venice Was Flooding ... One Tweet at a Time

Authors:

Valerio Lorini,

Carlos CastilloAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 6, Issue CSCW2

Article No.: 382, Pages 1 - 16

https://doi.org/10.1145/3555107

Published: 11 November 2022 Publication History

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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Cited By

Firmansyah HLorini VMulayim MGomes JFernandez-Marquez J(2024)Improving Social Media Geolocation for Disaster Response by Using Text From Images and ChatGPTProceedings of the 2024 11th Multidisciplinary International Social Networks Conference10.1145/3675669.3675696(67-72)Online publication date: 21-Aug-2024
https://dl.acm.org/doi/10.1145/3675669.3675696
Firmansyah HFernandez-Marquez JMulayim MGomes JLorini VRokne JWang D(2023)Accelerating Crisis Response: Automated Image Classification for Geolocating Social Media ContentProceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3625007.3627831(77-81)Online publication date: 6-Nov-2023
https://dl.acm.org/doi/10.1145/3625007.3627831

Index Terms

Venice Was Flooding ... One Tweet at a Time
1. Information systems
  1. Information retrieval

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 6, Issue CSCW2

CSCW

November 2022

8205 pages

EISSN:2573-0142

DOI:10.1145/3571154

Editor:
Jeff Nichols
Google

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Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 November 2022

Published in PACMHCI Volume 6, Issue CSCW2

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Cited By

Firmansyah HLorini VMulayim MGomes JFernandez-Marquez J(2024)Improving Social Media Geolocation for Disaster Response by Using Text From Images and ChatGPTProceedings of the 2024 11th Multidisciplinary International Social Networks Conference10.1145/3675669.3675696(67-72)Online publication date: 21-Aug-2024
https://dl.acm.org/doi/10.1145/3675669.3675696
Firmansyah HFernandez-Marquez JMulayim MGomes JLorini VRokne JWang D(2023)Accelerating Crisis Response: Automated Image Classification for Geolocating Social Media ContentProceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3625007.3627831(77-81)Online publication date: 6-Nov-2023
https://dl.acm.org/doi/10.1145/3625007.3627831

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