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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 13, issue 3
Nat. Hazards Earth Syst. Sci., 13, 669–677, 2013
https://doi.org/10.5194/nhess-13-669-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nat. Hazards Earth Syst. Sci., 13, 669–677, 2013
https://doi.org/10.5194/nhess-13-669-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 19 Mar 2013

Research article | 19 Mar 2013

Improving remote sensing flood assessment using volunteered geographical data

E. Schnebele and G. Cervone E. Schnebele and G. Cervone
  • Dept. of Geography and Geoinformation Science, George Mason University, 4400 University Drive, Fairfax, VA, USA

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.

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