MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085387 A) filed by Sri Eshwar College Of Engineering on July 11, 2026, for A Geoai-Enabled Digital Twin Framework For Smart Forest Management Using Edge Intelligence, Gis, And Multi-Source Remote Sensing.
Inventors include Dr. R. Babitha Lincy; Ms. Minu Balakrishnan; Dr. H. Anandakumar; and Ms. J. Yashwandra.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: Forests are vital natural resources that support biodiversity, regulate climate, conserve water resources, and provide livelihoods for millions of people. However, forests are increasingly threatened by wildfires, illegal logging, habitat degradation, and climate change. Conventional forest management practices primarily rely on periodic field inspections, manual patrols, and isolated monitoring systems, which often fail to provide timely detection of critical events. Moreover, the absence of integrated spatial intelligence and real-time decision support limits the ability of forest authorities to respond effectively to emerging threats. Recent advancements in the Internet of Things (IoT), Geographic Information Systems (GIS), remote sensing, artificial intelligence (AI), and edge computing have created new opportunities for intelligent forest management. IoT sensors can continuously monitor environmental parameters such as temperature, humidity, smoke, and acoustic signals, while satellite and drone-based remote sensing provide large-scale spatial information on forest conditions. GIS enables the integration and analysis of geospatial data, allowing visualization of forest boundaries, terrain, road networks, and fire-prone areas. Edge Intelligence further enables local processing of sensor data, reducing communication latency and ensuring continuous operation even in remote forest regions with limited network connectivity. A Digital Twin offers an advanced virtual representation of the physical forest environment by continuously synchronizing data from multiple sources. When integrated with GeoAI, GIS, IoT sensors, and multi-source remote sensing, the Digital Twin can provide real-time monitoring, predictive analytics, and intelligent decision support. Such a framework enables early forest fire prediction, wildlife movement monitoring, and illegal logging detection while offering interactive visualization of forest assets and environmental conditions. The proposed framework integrates distributed IoT sensor networks, satellite and drone imagery, GIS-based spatial analysis, GeoAI models, and an Edge AI Gateway into a unified Digital Twin platform. The system performs local data processing, predictive event detection, and geospatial analysis to provide timely alerts and support proactive forest management. By combining real-time sensing with intelligent analytics and spatial visualization, the proposed framework aims to improve forest conservation, enhance disaster preparedness, optimize resource management, and strengthen decision-making for sustainable forest management.
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