LandParser: Intelligent Land Boundary Detection and Encroachment Analysis Using Deep Learning with Satellite Imagery

Land management is a critical concern in modern governance, particularly with the increase in urbanisation and

disputes over land ownership. This initiative introduces a comprehensive intelligent system aimed at achieving three main goals:

detecting land encroachment, parsing land and recognising boundaries, and predicting land prices. The system utilises satellite

images and user-submitted photos, employing deep learning methods, especially U-Net-based semantic segmentation, to precisely

identify and mark encroached areas. For land parsing, it uses geospatial data and image processing techniques to accurately

define land boundaries and improve the precision of digital land records. Furthermore, machine learning models like XGBoost

are used to forecast land prices by considering various factors such as location, nearby infrastructure, land use category, and

historical trends. The system is designed with an interactive modular architecture that integrates GIS tools, OpenCV, and realtime analytics to facilitate efficient visualisation and decision-making. By combining computer vision, geospatial intelligence,

and predictive modelling, the platform provides a scalable solution for government officials, urban planners, and real estate

stakeholders.