# Multimodal Building Damage Assessment Method Fusing Adaptive Attention Mechanism and State-Space Modeling

## TL;DR

This paper introduces a new method for assessing building damage after disasters using advanced attention and modeling techniques to improve accuracy and reliability.

## Contribution

The paper proposes ADSFNet, a novel network combining adaptive attention and state-space modeling for multimodal building damage assessment.

## Key findings

- ADSFNet achieves F1 scores of 71.36% and 73.98% on BRIGHT and xBD datasets, outperforming existing methods.
- The model integrates CNNs and Mamba to capture both local and global damage features effectively.
- The method constructs a disaster-centric knowledge graph and supports intelligent emergency decision-making.

## Abstract

Rapid and reliable building damage assessment (BDA) is crucial for post-disaster emergency response. However, existing methods face challenges such as complex background interference, the difficulty in jointly modeling local geometric details and global spatial dependencies, and adverse weather conditions. To address these issues, this paper proposes the Adaptive Difference State-Space Fusion Network (ADSFNet), capable of processing both optical and Synthetic Aperture Radar (SAR) data to alleviate weather-induced limitations. To achieve this, ADSFNet innovatively introduces the Adaptive Difference Attention Fusion (ADAF) module and the Hybrid Selective State-Space Convolution (HSSC) module. Specifically, ADAF integrates pre- and post-disaster features to guide the network to focus on building regions while suppressing background interference. Meanwhile, HSSC synergizes the local texture extraction of CNNs with the global modeling strength of Mamba, enabling the simultaneous capture of cross-building spatial relationships and fine-grained damage details. Experimental results on sub-meter high-resolution MultiModal (BRIGHT) and optical (xBD) datasets demonstrate that ADSFNet attains F1 scores of 71.36% and 73.98%, which are 1.29% and 0.6% higher than the state-of-the-art mainstream methods, respectively. Finally, we leverage the model outputs to construct a disaster-centric knowledge graph and integrate it with Large Language Models to develop an intelligent management system, providing a novel technical pathway for emergency decision-making.