AI-Powered Urban Green Space Detection and Analysis Using Deep Learning and GIS

In rapidly expanding cities, urban green areas are essential to maintaining environmental sustainability, public health, and general well-being. However, urban planners' capacity to make prompt and efficient decisions is limited by the manual, antiquated, and time-consuming nature of traditional ways of monitoring and administering these spaces. This research proposes an AI-driven system that combines sophisticated deep learning algorithms with high-resolution satellite and drone imagery and Geographic Information System (GIS) capabilities to automate the detection, mapping, and analysis of urban vegetation. The system uses Convolutional Neural Networks (CNNs) for semantic segmentation of green spaces, including parks, forests, and grasslands, ensuring high accuracy and adaptability. The distribution of green space in metropolitan areas is dynamically visualized by superimposing these identified areas on interactive GIS maps. Furthermore, a novel Green Space Accessibility Index is presented to assess how accessible and close green spaces are to the community, supporting equity-focused urban planning. Initial tests show that the system works well in various urban environments, with a detection accuracy of over 90%. The platform is designed to be scalable, affordable, and deployable in both large and small city settings, providing a potent instrument for real-time urban planning and environmental management. Key Words: deep learning, urban green areas, accessibility index, GIS visualization, semantic segmentation, sustainable urban planning.