  Due to climate and land-use change, natural disasters such as flooding have
been increasing in recent years. Timely and reliable flood detection and
mapping can help emergency response and disaster management. In this work, we
propose a flood detection network using bi-temporal SAR acquisitions. The
proposed segmentation network has an encoder-decoder architecture with two
Siamese encoders for pre and post-flood images. The network's feature maps are
fused and enhanced using attention blocks to achieve more accurate detection of
the flooded areas. Our proposed network is evaluated on publicly available
Sen1Flood11 benchmark dataset. The network outperformed the existing
state-of-the-art (uni-temporal) flood detection method by 6\% IOU. The
experiments highlight that the combination of bi-temporal SAR data with an
effective network architecture achieves more accurate flood detection than
uni-temporal methods.
