Building registry updates are essential for urban planning but remain a labor-intensive process. This thesis introduces PolyChange, an adaptation of the PolyBuilding model, to automate mutation delineation by integrating aerial imagery with reference maps to produce precise, vectorized building mutations. PolyChange combines raster-based and direct polygonal representation methods, augmented with angle loss, double-thresholding, and label smoothing to enhance delineation accuracy. Experiments on our synthetic dataset demonstrate that raster-based methods achieve superior delineation, with an AR of 89.3\% and AP of 85.2\%. However, real-world datasets reveal challenges in generalization due to overfitting, semantic vagueness, and annotation inconsistencies, resulting in reduced detection and delineation capabilities, indicated by an AR of 30\% and AP of 13.7\% on the test set. Future work should address these limitations by improving dataset quality, refining model architectures, and leveraging reference maps more effectively to achieve fully automated and accurate building registry updates.