Super-Resolution AI-Based Approach for Extracting Agricultural Cadastral Maps: Form and Content Validation

Updating and digitizing cadastral maps remains a major challenge in land administration, demanding significant financial and human resources. This study presents a fully automated AI-based system to address this issue, focusing on the extraction and digitization of agricultural cadastral maps using photogrammetric images. The proposed method leverages the segment anything model for high-accuracy segmentation, achieving a notable intersection over union score of 92%, significantly outperforming traditional approaches. In addition, the system reduces processing time by 40% and eliminates the need for manual intervention, enabling scalable, efficient digitization. These improvements are critical for better…