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Improving remote sensing flood assessment using volunteered geographical data

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dc.contributor.author Schnebele, E.
dc.contributor.author Cervone, G.
dc.date.accessioned 2014-09-15T20:14:57Z
dc.date.available 2014-09-15T20:14:57Z
dc.date.issued 2013-03-19
dc.identifier.citation Schnebele, 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. en_US
dc.identifier.other doi:10.5194/nhess-13-669-2013
dc.identifier.uri https://hdl.handle.net/1920/8824
dc.description.abstract A 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.sponsorship Work 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. en_US
dc.language.iso en_US en_US
dc.publisher Copernicus Publications en_US
dc.rights Attribution 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by/3.0/us/ *
dc.subject remote sensing en_US
dc.subject volunteered ground data en_US
dc.subject statistical flood model en_US
dc.subject flood hazard maps en_US
dc.title Improving remote sensing flood assessment using volunteered geographical data en_US
dc.type Article en_US


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