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@article{187103,
        author = {Shreyas Waral and Tuba Khan and Krish Kava and Pranjal Tathe},
        title = {A Review Of AI-Powered Urban Green Cover Monitoring System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {3103-3110},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187103},
        abstract = {This project introduces an AI-enabled regional en- vironmental monitoring framework that utilizes the capabilities of Google Earth Engine to analyze longitudinal satellite data for detailed ecological evaluations [4], [5]. The system assesses tem- poral 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 like NDVI, NDWI, and NDBI alongside climate metrics, the platform offers a comprehensive overview of environmental trends and fluctuations over selected periods [2], [3].
Expanding on this analytical foundation, the platform employs generative AI techniques to develop expert, context-specific policy recommendations [6]. These recommendations focus on promot- ing environmentally sustainable practices such as strategic tree planting to improve air quality, enhance carbon capture, and strengthen ecosystem resilience [6]. Delivered via a dynamic web interface, this initiative empowers decision-makers and stake- holders with data-driven insights essential for climate adaptation, ecosystem preservation, and sustainable regional development planning [5].},
        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},
        month = {November},
        }