Improving remote sensing flood assessment using volunteered geographical data

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dc.contributor.authorSchnebele, E.
dc.contributor.authorCervone, G.
dc.date.accessioned2014-09-15T20:14:57Z
dc.date.available2014-09-15T20:14:57Z
dc.date.issued2013-03-19
dc.description.abstractA new methodology for the generation of flood hazard maps is presented fusing remote sensing and volunteered geographical data. Water pixels are identified utilizing a machine learning classification of two Landsat remote sensing scenes, acquired before and during the flooding event as well as a digital elevation model paired with river gage data. A statistical model computes the probability of flooded areas as a function of the number of adjacent pixels classified as water. Volunteered data obtained through Google news, videos and photos are added to modify the contour regions. It is shown that even a small amount of volunteered ground data can dramatically improve results.
dc.description.sponsorshipWork performed under this project has been partially supported by the US Department of Transportation award 202717 (RITARS-12-H-GMU, CFDA). The publication of this article was funded in part by the George Mason University Libraries Open Access Publishing Fund.
dc.identifier.citationSchnebele, E. and Cervone, G.: Improving remote sensing flood assessment using volunteered geographical data, Nat. Hazards Earth Syst. Sci., 13, 669-677, doi:10.5194/nhess-13-669-2013, 2013.
dc.identifier.otherdoi:10.5194/nhess-13-669-2013
dc.identifier.urihttps://hdl.handle.net/1920/8824
dc.language.isoen_US
dc.publisherCopernicus Publications
dc.rightsAttribution 3.0 United States
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/us/
dc.subjectRemote sensing
dc.subjectVolunteered ground data
dc.subjectStatistical flood model
dc.subjectFlood hazard maps
dc.titleImproving remote sensing flood assessment using volunteered geographical data
dc.typeArticle

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