Post-disaster building damage assessment based on gated adaptive multi-scale spatial-frequency fusion network

Post-disaster building damage assessment based on gated adaptive multi-scale spatial-frequency fusion network

Accurate building damage assessment is crucial for post-disaster response, yet existing methods struggle to capture complex spatial relationships and contextual features needed for distinguishing damage levels. To address this, we propose the Gated Adaptive Multi-scale Spatial-frequency Fusion Netwo...

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Bibliographic Details
Main Authors: Bo Yu, Yao Sun, Jiansong Hu, Fang Chen, Lei Wang
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Building damage assessment
Adaptive attention
Convolutional neural network
Satellite imagery
Multi-scale feature extraction
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843225002766
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Summary:Accurate building damage assessment is crucial for post-disaster response, yet existing methods struggle to capture complex spatial relationships and contextual features needed for distinguishing damage levels. To address this, we propose the Gated Adaptive Multi-scale Spatial-frequency Fusion Network (GAMSF), a two-phase framework for building localization and damage classification. GAMSF integrates three key innovations: (1) Adaptive Attention (AA) to dynamically prioritize critical regions, (2) Gated Multi-scale Feed-Forward Network (GMFFN) to enhance robustness by emphasizing prominent damage features, and (3) Multi-Scale Wavelet Fusion (MWF) to extract fine-grained structural details using wavelet transforms. Rigorous evaluations on the datasets, including xBD and xFBD, demonstrates that GAMSF achieves the state-of-the-art performance, with a 1.7% improvement in F1-score, a 2.1% gain in Kappa, and a 3.7% increase in minor damage identification accuracy compared to existing approaches. Furthermore, transferability experiments on the high-resolution Ida-BD dataset validate GAMSF’s superior generalization capabilities, outperforming four advanced models. These results highlight the practical value of GAMSF in enhancing disaster management, emergency response, and resource allocation strategies.
ISSN:1569-8432

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