Intelligent Urban Greening Assessment Using Machine Learning and Aerial Imaging

Urban greening is no longer a mere embellishment but is the critical backbone sustaining ecological balance amid relentless urban sprawl, technological divides, and social inequities. In an era where megacities suffer from crippling heat island effects and experience a seventeen percent loss of parkland in cities such as Mumbai, our study harnesses an array of avant-garde computer vision and deep learning techniques to quantify and reinvent urban greenery. By fusing multispectral unmanned aerial vehicle data, Internet of Things sensor outputs, and advanced semantic segmentation techniques such as the models YOLO Eleven, U-Net, and TreeFormer, our methodology investigates green zone distribution with astonishing precision, achieving mean absolute error values as low as one point five percent and processing speeds exceeding sixty frames per second on edge devices. Short yet potent, the approach transforms raw satellite and aerial imagery into actionable insights that reveal not only spatial deficits but also forecast microclimatic shifts. The resulting framework, underpinned by hybrid convolutional neural network transformer architectures and real-time video tracking using DeepSORT and StrongSORT, offers a dynamic and scalable design for urban planners, environmental scientists, and policymakers. Ultimately, this research illuminates a pathway to reclaim urban ecosystems through data-driven, high-resolution green space analytics, ensuring that nature's vital benefits including thermal regulation, air filtering, and public health improvements are resiliently renewed.