SwinEADFormer: An Edge-Aware Dynamic Swin Transformer for Building Change Detection in High-Resolution Remote Sensing Images

Binary building change detection in high-resolution optical remote sensing images requires accurate temporal comparison and precise boundary localization. Existing methods often apply cross-temporal interaction uniformly to all spatial tokens at a selected feature level. When true changes are sparse, appearance differences at unchanged locations may still produce nonzero interaction responses. To address this issue, we propose SwinEADFormer, a difference-conditioned and discrepancy-aware cross-temporal interaction framework. A shared Swin-Tiny encoder extracts hierarchical bitemporal features. An image-pair-specific router generated from S3 difference cues and S4 semantic context modulates cross-temporal attention outputs before residual addition. The model explicitly computes the discrepancy between the two temporal interaction directions and fuses it with learned and direct S3 difference evidence. This design conditions interaction on estimated change evidence while preserving direct temporal differences. A multi-scale decoder produces the final change map. Experiments on LEVIR-CD, WHU-CD, and SYSU-CD show competitive region-level performance against representative baselines. On LEVIR-CD, SwinEADFormer achieves the highest mean B-recall and B-F1. Five-seed ablations show that Φ fusion and bidirectional interaction provide the clearest improvements, while the router yields smaller mean gains.