Revisiting the Necessity of Full Accuracy: Weakly Supervised Object-Level Offset Correction for Misaligned Building Labels

Google Earth imagery, combined with building footprint databases, offers an efficient way to construct localized building datasets. However, the lack of orthorectification in these images leads to spatial misalignments between annotations and their corresponding roof locations. Adopting such misaligned data directly for model training can severely degrade segmentation performance. To address the challenge, we propose an Object-based Multi-stage Alignment Framework (OMAF) that generates high-quality corrected labels with minimal manual intervention. OMAF first employs a prior-regularized self-alignment method to produce high-confidence, object-level offset pseudo-labels, which are then used to train an instance-level offset regression model for label refinement. Experimental results on the challenging Islahiye and Antakya datasets demonstrate that OMAF effectively corrects misalignments and consistently boosts the mIoU of all baseline models by up to $40.6\%$. The ablation experiments also demonstrated that each module in OMAF effectively improves the final alignment performance. Among them, the self-alignment algorithm contributed $9.22\%$ to the mIoU metric, demonstrating the strong effectiveness of this unsupervised alignment method.This work provides a practical and cost-effective solution for large-scale dataset construction and domain adaptation.