Source-Only Cross-Dataset Building Change Detection with Frozen DINOv3 and Hierarchical Evidence Fusion

Remote sensing building change detection is important for monitoring urban expansion, rural settlement dynamics, post-disaster reconstruction, and human-induced land transformation. However, most existing change detection models are optimized under in-domain protocols, while practical deployment often requires direct transfer from one labeled source dataset to unseen target domains without target images, labels, validation data, adaptation, or threshold calibration. This source-only cross-dataset setting is challenging because changes in sensor characteristics, spatial resolution, viewing geometry, scene composition, and background appearance can cause missed detections and pseudo-change false alarms. To address this problem, we propose DLV-CD, a frozen-DINOv3-based framework that trains only task-specific adapters, a multi-level difference fusion decoder, and a hierarchical evidence fusion module for source-only cross-dataset building change detection. Transfer evaluation on four datasets, namely LEVIR-CD, WHU-CD, S2Looking, and DSIFN-CD, shows that DLV-CD achieves the best F1-score compared with seven reproduced baselines, including classic supervised CD models, recent supervised CD models, and the SAM-based foundation-model baseline SAM-CD. Specifically, DLV-CD improves the average F1-score from 32.99% to 65.38%, outperforming the strongest reproduced baseline by 32.39 percentage points. Precision–recall analysis and qualitative comparisons further show that DLV-CD reduces both recall collapse and pseudo-change over-detection. These results demonstrate that frozen visual foundation representations provide a strong basis for target-free cross-dataset building change detection.