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Rapid Building Damage Assessment Following the M7.7 Myanmar Earthquake: A Causal Bayesian Approach Using InSAR Imagery

Description: 

On March 28, 2025, a devastating 7.7 magnitude earthquake struck Myanmar's Sagaing Region near Mandalay, killing approximately 5,350 people, injuring 7,860, and leaving hundreds missing. Our research presents comprehensive city-scale mapping of earthquake effects using a novel variational causal Bayesian network that integrates Interferometric Synthetic Aperture Radar (InSAR) derived change detection with empirical ground failure models and building footprints. This innovative approach enables four critical capabilities: (1) rapid large-scale estimation of building damage and ground failures from remote sensing, (2) causal attribution of building damage to specific failure mechanisms, (3) enhanced prediction of regional infrastructure impacting landslides and liquefaction, and (4) fine-grained damage assessment across thousands of buildings simultaneously. By utilizing 30-meter resolution satellite imagery and building footprints, we achieved building-by-building damage classification within days of the earthquake. Our five-category damage classification system (no damage, slight, moderate, partial collapse, and collapse) was validated against high-resolution Maxar optical imagery, demonstrating exceptional accuracy in identifying damage patterns across the affected region. This work represents a significant advancement in rapid post-disaster damage assessment, providing critical information for emergency response and recovery planning following major seismic events.

Session: Late-breaking on Recent and Future Large Earthquakes [Poster]

Type: Poster

Date: 4/15/2025

Presentation Time: 08:00 AM (local time)

Presenting Author: Xuechun

Student Presenter: Yes

Invited Presentation: 

Poster Number: 151

Authors