The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical
Committee of the IEEE Geoscience and Remote Sensing Society was focused
on the detection of flooded areas using multi-temporal and multi-modal
images. Both high spatial resolution optical and synthetic aperture
radar data were provided. The goal was not only to identify the best
algorithms (in terms of accuracy), but also to investigate the further
improvement derived from decision fusion. This paper presents the
four awarded algorithms and the conclusions of the contest, investigating
both supervised and unsupervised methods and the use of multi-modal
data for flood detection. Interestingly, a simple unsupervised change
detection method provided similar accuracy as supervised approaches,
and a digital elevation model-based predictive method yielded a comparable
projected change detection map without using post-event data.