AI LAND VERIFICATION AND RECOMMENDATION PLATFORM

Author

Janani J M, Kanishka V, Netra S D, Mrs. Dr. Deepa M


Abstract

 Buying land is a high-value decision, yet buyers often find it difficult to confirm that a listing is genuine, that the seller holds the title, and that the plot suits their budget, size, and purpose. Manual document checking is slow and error-prone, and listing portals usually leave the verification burden on the buyer. This paper presents the AI Land Verification and Recommendation Platform (LandVision AI), a web-based application in which owners list land and submit supporting legal documents, an AI model extracts the visible details from those documents, administrators review them against a structured checklist, and buyers receive ranked land recommendations. The proposed system aims to combine role-based authentication, land listing management, secure document upload, AI-based field extraction, mismatch warnings, administrator approval with an audit trail, natural-language search, and transparent match scoring. The application uses a React.js frontend, a Node.js and Express.js backend, Firebase Firestore for data storage, Cloudinary for images and documents, and the Gemini API for document analysis and request parsing. AI extraction is treated as preliminary assistance and never as proof of ownership; only an administrator decision can mark a listing as verified. The system is designed with consideration for usability, privacy, security, and reliable handling of submitted documents. Functional and integration testing can be used to evaluate the individual modules and their interaction. The proposed system provides a foundation for combining document-assisted verification and preference-based recommendation in a single application, with further development required to evaluate extraction accuracy on real-world documents.


Keywords

Land Verification, Land Recommendation, Document Analysis, Gemini AI, Role-Based Access Control, Match Scoring, React.js, Firebase Firestore.



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