Accurate cadastral maps are essential for effective land administration, supporting tenure security, land management, and socioeconomic planning. Automating cadastral boundary extraction can accelerate mapping in regions with incomplete or absent cadastral information, but deploying pretrained models in data-scarce areas is challenging due to limited reference data and heterogeneous landscapes. In this study, we investigate cross-region transfer learning for delineating cadastral boundaries using high-resolution aerial imagery. We employ <em>CadNet</em>, a U-shaped deep learning model with a Swin Transformer backbone pretrained on the Dutch <em>CadastreVision</em> dataset, and fine-tune it using Polish cadastral reference data selected for landscape similarity to a data-scarce region in northern Moldova. Evaluation on Moldovan test tiles demonstrates substantial quantitative improvements: recall for visually discernible boundaries increases from 0.310 to 0.624, total vector-based discrepancy via Normalized Discrepant Area decreases from 7.898 to 7.051. Qualitatively, fine-tuning produces more continuous and coherent boundaries, recovers interior parcel divisions, and better aligns predicted parcel structures with ground truth, compared to the pretrained model, which generates fragmented and incomplete boundaries. These results highlight the importance of landscape similarity and reference data quality for transfer learning and demonstrate a scalable framework for automated cadastral mapping in regions with similar landscape characteristics.