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

Abstract

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 land-use planning, resource allocation, and sustainable land management practices. The model, implemented using open-source Python libraries, integrates three stages: image preprocessing, AI-based segmentation, and postprocessing. By automating these processes, the system not only accelerates map production but also reduces environmental impacts associated with traditional mapping techniques. The approach also enhances the accuracy of agricultural boundary delineation, offering benefits for land dispute resolution and optimized agricultural practices. This research contributes to the modernization of land administration systems by providing an accessible, scalable solution for surveyors and policymakers. It bridges the gap between cutting-edge artificial intelligence advancements and practical applications, addressing technical and operational challenges in geospatial data management. The findings underscore the importance of automating cadastral mapping for both economic efficiency and environmental sustainability.