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

arXiv: https://arxiv.org/abs/2502.18546 · HTML: https://mlsanity.com/paper/2502.18546/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 55 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 2 of 2)

- [Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery](https://mlsanity.com/paper/2310.01565/) (2023)
- [Spatial-variant causal Bayesian inference for rapid seismic ground failures and impacts estimation](https://mlsanity.com/paper/2412.00026/) (2024)

## Built on by (most cited first, 1 of 1)

- [Multi-resolution Score-Based Variational Graphical Diffusion for Causal Disaster System Modeling and Inference](https://mlsanity.com/paper/2504.04015/) (2025)

## Abstract (arXiv)

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.