Authors : S. S. Banne; Shreyas Waral; Tuba Khan; Krish Kava; Pranjal Tathe

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/3k24secv

Scribd : https://tinyurl.com/zvkuphwv

DOI : https://doi.org/10.38124/ijisrt/26jun969

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This project introduces an AI-enabled regional environmental monitoring framework that leverages the capabilities of Google Earth Engine to analyze longitudinal satellite data for detailed ecological evaluations. The system assesses temporal changes in vegetation cover, water bodies, urban growth, land surface temperature, carbon sequestration, air quality, precipitation, and land use at the district level. By integrating various satellite-based indices such as NDVI, NDWI, and NDBI alongside climate metrics, the platform provides a comprehensive overview of environmental trends and fluctuations over selected time periods. Building on this analytical foundation, the platform incorporates generative AI techniques to generate expert, context-specific policy recommendations. These recommendations focus on promoting environmentally sustainable practices, such as strategic tree planting to improve air quality, enhance carbon sequestration, and strengthen ecosystem resilience. Delivered through a dynamic web interface, the system empowers decision-makers and stakeholders with data-driven insights essential for climate adaptation, ecosystem preservation, and sustainable regional development planning.

Keywords : AI, Air Quality, Carbon Sequestration, Climate Resilience, Decision-Support, Generative Artificial Intelligence, Land Surface Temperature, Multi-Temporal Analysis, Urban Green Cover, Tree Detection, Remote Sensing, Decision-Support, Satellite Data, Sustainable Development, Urban Green Cover, Water Body Detection, Tree Detection.

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