  Timely and accurate assessment of hurricane-induced building damage is
crucial for effective post-hurricane response and recovery efforts. Recently,
remote sensing technologies provide large-scale optical or Interferometric
Synthetic Aperture Radar (InSAR) imagery data immediately after a disastrous
event, which can be readily used to conduct rapid building damage assessment.
Compared to optical satellite imageries, the Synthetic Aperture Radar can
penetrate cloud cover and provide more complete spatial coverage of damaged
zones in various weather conditions. However, these InSAR imageries often
contain highly noisy and mixed signals induced by co-occurring or co-located
building damage, flood, flood/wind-induced vegetation changes, as well as
anthropogenic activities, making it challenging to extract accurate building
damage information. In this paper, we introduced an approach for rapid
post-hurricane building damage detection from InSAR imagery. This approach
encoded complex causal dependencies among wind, flood, building damage, and
InSAR imagery using a holistic causal Bayesian network. Based on the causal
Bayesian network, we further jointly inferred the large-scale unobserved
building damage by fusing the information from InSAR imagery with prior
physical models of flood and wind, without the need for ground truth labels.
Furthermore, we validated our estimation results in a real-world devastating
hurricane -- the 2022 Hurricane Ian. We gathered and annotated building damage
ground truth data in Lee County, Florida, and compared the introduced method's
estimation results with the ground truth and benchmarked it against
state-of-the-art models to assess the effectiveness of our proposed method.
Results show that our method achieves rapid and accurate detection of building
damage, with significantly reduced processing time compared to traditional
manual inspection methods.
