MUMBAI, India, Jan. 9 -- Intellectual Property India has published a patent application (202541110621 A) filed by New Prince Shri Bhavani College Of Engineering And Technology; Aswin Kumar C; Chandru D; Tamilselvan G; and Monisha Jothi R, Chennai, Tamil Nadu, on Nov. 13, 2025, for 'predicting land transformation in a climate-sensitive basin using svm-markov modeling.'
Inventor(s) include Aswin Kumar C; Chandru D; Tamilselvan G; and Monisha Jothi R.
The application for the patent was published on Jan. 9, under issue no. 02/2026.
According to the abstract released by the Intellectual Property India: "Land transformation is one of the most critical indicators of environmental and socio-economic change, directly influencing climate regulation, water availability, soil fertility, and food security. In climate-sensitive basins, unchecked deforestation, agricultural expansion, and urbanization pose severe risks to ecological stability and community livelihoods. This region faces disruptions in water flow patterns, soil stability, and biodiversity conservation. These changes reduce ecosystem services such as flood control, carbon sequestration, and water purification, while increasing the vulnerability of local communities. 0) o CQ 0. _2 Traditional land use studies rely heavily on manual interpretation of satellite images, which is both time-consuming and error-prone. Moreover, many monitoring systems use outdated statistical methods, lacking advanced machine learning-based predictive capabilities. This limits the ability to plan proactive interventions for sustainable land management. CM E o u. CM (0 O This project leverages remote sensing data, Support Vector Machine (SVM), and Markov Chain modeling to improve the accuracy of Land Use and Land Cover (LULC) prediction. By analyzing multitemporal satellite imagery (2001, 2014, 2021) using hybrid pixel-based and segment-based classification, the system generates transition probability matrices and simulates future changes for the year 2040. m CM o C! 5 K- co m The outcomes include accurate LULC maps, transition potential models, and predictive insights for land-use planning. These results aid policymakers, municipalities, and environmental agencies in combating deforestation, guiding urban expansion, and protecting fi-agile ecosystems. By aligning with SDG 13 (Climate Action) and SDG 15 (Life on Land), this project provides a decision-support framework for sustainable development and ecological resilience."
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