Leveraging Transformers for Boundary Detection and Encroachment Identification in Land Surveys

Abstract

Land management, urban planning, and legal compliance all depend on the prompt and accurate detection of encroachments and the establishment of land boundaries. Especially in vast or complicated land regions, traditional surveying methods are labor-intensive and prone to inaccuracy since they frequently rely on manual processes or GPS-based approaches. In this study, we offer a unique method to automate land boundary recognition and monitor encroachment in satellite and aerial photos using transformer-based deep learning models, specifically Vision Transformers (ViT). By utilizing transformers' capacity to identify local and global patterns in high-resolution images, the suggested approach offers scalability and accuracy improvements over current techniques.