MUMBAI, India, Feb. 13 -- Intellectual Property India has published a patent application (202541124558 A) filed by Jerusalem Engineering College, Chennai, Tamil Nadu, on Dec. 10, 2025, for 'ai powered safe path prediction to prevent human wild life conflict using geospatial data.'
Inventor(s) include K. Pushpa Valli; K. P. Gopal; S. Jeevitha; K. Shanmugapriya; and Dr. D. Sudhagar.
The application for the patent was published on Feb. 13, under issue no. 07/2026.
According to the abstract released by the Intellectual Property India: "Human-wildlife conflict has become a growing environmental and safety concern, particularly in areas where human settlements and transport routes intersect with forest regions. Such encounters often result in loss of life, property damage, and ecological imbalance. Conventional wildlife monitoring systems rely on loT sensors, manual observations, or fixed cameras, which are costly, limited in coverage, and require constant maintenance. To overcome these challenges, this study proposes anAl-powered predictive system that uses open-source biodiversity data to forecast wildlife movement and recommend safe travel routes near forest zones. The system integrates data from the Global Biodiversity Information Facility (GBIF) API, which provides verified animal occurrence records. By analyzing data on species such as tiger, elephant, leopard, bison, rhino, and sloth bear, the model identifies animal distribution and activity patterns in South Indian Forest regions. The collected data are processed into time-series sequences and used to train a Long Short-Term Memory (LSTM) neural network that predicts future wildlife presence. The Prediction Module highlights risk-prone zones, while the Safe Route Navigation Module, using the Valhalla Routing Engine API, suggests alterimtive paths to avoid potential wildlife areas. The results are visualized on an interactive map interface built with React and OpenStreetMap APis, enabling users to view real-time animal movement and safe routes. Operating entirely on open data and software frameworks, the proposed system eliminates the need for physical sensors, offering a cost-effective, scalable, and ecofriendly solution for wildlife conflict management. By combining biodiversity data; deep learning, and geospatial visualization, the system enhances human safety, promotes conservation awareness, and supports sustainable coexistence between humans and wildlife."
Disclaimer: Curated by HT Syndication.
