Scalable and rapid building damage detection after hurricane Ian using causal Bayesian networks and InSAR imagery

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 zone 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 a causality-informed Bayesian network inference 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 also benchmarked it against state-of-the-art models to assess the effectiveness of our proposed method. The results show that our method advances building damage assessment after hurricanes by accurately reflecting the complex dynamics between wind and flood impacts. Notably, it achieves this without the need for a ground truth label, which is a substantial step forward from traditional methods. Our model registers a 22.6% increase in the Area Under the Curve (AUC) and a 46.29% enhancement in the True Positive Rate (TPR). Moreover, it expedites the detection of building damage, cutting down processing times by up to 83.8%. These improvements mark a considerable leap in efficiency, demonstrating our method’s ability to streamline the assessment process markedly over conventional methods.