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Accurate mapping of earthquake-damaged buildings is essential for rapid post-disaster assessment and urban recovery planning. Very high-resolution (VHR) satellite imagery enables large-scale geospatial damage detection; however, conventional convolutional neural networks (CNNs) often struggle to model complex spatial dependencies and nonlinear damage patterns efficiently. To address these limitati
