Skip to main content

Research Repository

Advanced Search

Rapid flood inundation mapping using social media, remote sensing and topographic data

Rosser, Julian F.; Leibovici, Didier; Jackson, M.J.

Authors

Julian F. Rosser

Didier Leibovici

M.J. Jackson



Abstract

Flood events cause substantial damage to urban and rural areas. Monitoring water extent during large-scale flooding is crucial in order to identify the area affected and to evaluate damage. During such events, spatial assessments of floodwater may be derived from satellite or airborne sensing platforms. Meanwhile, an increasing availability of smartphones is leading to documentation of flood events directly by individuals, with information shared in real-time using social media. Topographic data, which can be used to determine where floodwater can accumulate, are now often available from national mapping or governmental repositories. In this work, we present and evaluate a method for rapidly estimating flood inundation extent based on a model that fuses remote sensing, social media and topographic data sources. Using geotagged photographs sourced from social media, optical remote sensing and high-resolution terrain mapping, we develop a Bayesian statistical model to estimate the probability of flood inundation through weights-of-evidence analysis. Our experiments were conducted using data collected during the 2014 UK flood event and focus on the Oxford city and surrounding areas. Using the proposed technique, predictions of inundation were evaluated against ground-truth flood extent. The results report on the quantitative accuracy of the multisource mapping process, which obtained area under receiver operating curve values of 0.95 and 0.93 for model fitting and testing, respectively.

Citation

Rosser, J. F., Leibovici, D., & Jackson, M. (in press). Rapid flood inundation mapping using social media, remote sensing and topographic data. Natural Hazards, https://doi.org/10.1007/s11069-017-2755-0

Journal Article Type Article
Acceptance Date Jan 11, 2017
Online Publication Date Jan 25, 2017
Deposit Date Jan 24, 2017
Publicly Available Date Jan 25, 2017
Journal Natural Hazards
Print ISSN 0921-030X
Electronic ISSN 1573-0840
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1007/s11069-017-2755-0
Keywords Flood mapping, Data fusion, Data conflation, Data integration
Public URL https://nottingham-repository.worktribe.com/output/839054
Publisher URL https://link.springer.com/article/10.1007/s11069-017-2755-0

Files

art%3A10.1007%2Fs11069-017-2755-0.pdf (2.2 Mb)
PDF

Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0





You might also like



Downloadable Citations