Multi-class Seismic Building Damage Assessment from InSAR Imagery using Quadratic Variational Causal Bayesian Inference

TL;DR

This paper introduces a novel variational causal Bayesian inference framework that enhances multi-class seismic building damage assessment from InSAR imagery, achieving high accuracy and efficiency across multiple earthquake datasets.

Contribution

The paper presents a new quadratic variational causal Bayesian inference method that improves damage classification accuracy and computational efficiency in seismic building damage assessment.

Findings

Achieved damage classification accuracy with AUC of 0.94-0.96.

Reduced computational overhead by over 40%.

Maintained high accuracy (>0.93 AUC) across all damage categories.

Abstract

Interferometric Synthetic Aperture Radar (InSAR) technology uses satellite radar to detect surface deformation patterns and monitor earthquake impacts on buildings. While vital for emergency response planning, extracting multi-class building damage classifications from InSAR data faces challenges: overlapping damage signatures with environmental noise, computational complexity in multi-class scenarios, and the need for rapid regional-scale processing. Our novel multi-class variational causal Bayesian inference framework with quadratic variational bounds provides rigorous approximations while ensuring efficiency. By integrating InSAR observations with USGS ground failure models and building fragility functions, our approach separates building damage signals while maintaining computational efficiency through strategic pruning. Evaluation across five major earthquakes (Haiti 2021, Puerto Rico 2020, Zagreb 2020, Italy 2016, Ridgecrest 2019) shows improved damage classification accuracy (AUC: 0.94-0.96), achieving up to 35.7% improvement over existing methods. Our approach maintains high accuracy (AUC > 0.93) across all damage categories while reducing computational overhead by over 40% without requiring extensive ground truth data.