  We propose a framework that estimates inundation depth (maximum water level)
and debris-flow-induced topographic deformation from remote sensing imagery by
integrating deep learning and numerical simulation. A water and debris flow
simulator generates training data for various artificial disaster scenarios. We
show that regression models based on Attention U-Net and LinkNet architectures
trained on such synthetic data can predict the maximum water level and
topographic deformation from a remote sensing-derived change detection map and
a digital elevation model. The proposed framework has an inpainting capability,
thus mitigating the false negatives that are inevitable in remote sensing image
analysis. Our framework breaks the limits of remote sensing and enables rapid
estimation of inundation depth and topographic deformation, essential
information for emergency response, including rescue and relief activities. We
conduct experiments with both synthetic and real data for two disaster events
that caused simultaneous flooding and debris flows and demonstrate the
effectiveness of our approach quantitatively and qualitatively.
