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.