Optimizing Rapid Seismic Building Damage Assessment: Integrating Enhanced Radar Change Detection Maps with Variational Bayesian Networks

Accurate damage estimation after earthquakes is crucial for effective post-disaster response and recovery. However, earthquakes often trigger various additional hazards, such as landslides and liquefaction, making accurate building damage estimation even more challenging. To date, despite significant research efforts, automated, accurate building-specific damage estimation has not been achieved. Our study tackles this challenge. We integrate multi-sourced global building footprints and InSAR coherence-based Change Detection Maps (CDMs) generated by the U.S. Geological Survey (USGS) within a variational causal Bayesian network, providing intricate maps of landslides, liquefaction, and building damage. Our key innovations include: 1) a novel masking strategy for the CDMs, derived from low pre-event mean coherence value and high pre-event coherence standard deviation to eliminate noisy signals in InSAR products induced by irrelevant noise sources (steep slopes, soil moisture and vegetation change, open water, etc.), and 2) variational inference to differentiate potential causes of the changes in InSAR coherence signals, specifically landslides, liquefaction, building damage, and non-hazard changes. Our strategy is critical for enhancing the accuracy of building damage and ground failure assessments, as noise from environmental or human-induced changes can obscure true damage signals. We provide reliable damage identification with attribution to specific causes by focusing on accurate building footprints and improving regional ground failure predictions using the 2023 M6.8 Morocco earthquake to validate our methodology. Our approach enables thorough damage analysis across numerous buildings, with the potential for significantly aiding disaster management and marking a substantial advancement of post-earthquake building damage assessment methods.