Geographic Bias Analysis and Cross-Domain Generalization in Deep Learning-Based Building Damage Assessment

Automated building damage assessment from satellite imagery has become increasingly critical for rapid disaster response and humanitarian relief operations. However, current state-of-the-art deep learning models exhibit significant generalization challenges when deployed to geographically and environmentally diverse regions. This study investigates the nature and extent of geographic bias in building damage detection systems, revealing that model performance degradation occurs primarily from geographic and structural characteristics rather than insufficient training data representation. Through a systematic evaluation of top-performing xView2 competition solutions across 17 disaster locations across multiple climate zones, we found that even state-of-the-art models struggle with generalization under geographic shift, particularly for the minor and major damage classes, and exhibit strong geographic biases toward certain regions. In this work, we explore a six-channel Fusion Augmentation strategy that enriches RGB imagery with auxiliary structural enhancement channels, together with supervised fine-tuning and unsupervised CORAL-based domain adaptation for three unseen regions. The experimental results demonstrate a substantial improvement of 7.1% overall F1 score, with notable gains for intermediate damage categories such as minor and major damage. Domain adaptation experiments on three unseen locations show that combining Fusion Augmentation with supervised fine-tuning yields 40.8% and 60.0% improvements over the minor and major classes, while unsupervised CORAL achieves 24.2% and 39.5% improvements over the minor and major damage classes compared to benchmarks. These findings highlight persistent geographic bias and demonstrate that structural feature enhancement combined with domain adaptation is essential for globally deployable damage assessment systems.