GEOAWARE: AI-DRIVEN LAND COVER CHANGE CLASSIFICATION FOR PROACTIVE NATURAL DISASTER MITIGATION

Authors

  • Dr. Angadi Swarupa Rani , Y. Bhargavi , S.Soumya, T.Renuka

DOI:

https://doi.org/10.47750/

Keywords:

AI-driven systems, Satellite imagery, Convolutional Neural Networks

Abstract

India faces frequent natural disasters such as floods, droughts, cyclones, and landslides, which significantly impact lives, infrastructure, and the environment. Between 1990 and 2020, India reported over 400 natural disasters, affecting more than 1 billion people and causing damages worth billions. With 68% of its land prone to drought, 12% to floods, and 8% to cyclones, the need for effective disaster mitigation strategies is paramount. Land cover change, driven by deforestation, urbanization, and agricultural expansion, exacerbates disaster risks, leading to soil erosion, reduced water retention, and biodiversity loss. 

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