AI-Powered Waste Management Optimization for Smart Cities

As cities grow, waste management becomes increasingly challenging, leading to inefficiencies and environmental issues. Our project focuses on developing an AI-driven solution that utilizes the power of predictive analytics, computer vision, and NLP to optimize waste management systems in municipal settings. By implementing computer vision models, such as YOLO, we aim to accurately identify and categorize waste types from live camera feeds, enabling automated sorting and improved recycling processes. Additionally, leveraging predictive analytics and LLMs, we will forecast waste generation patterns to design efficient collection routes and schedules, reducing fuel consumption and operational costs. Utilizing technologies like TensorFlow and PyTorch, our solution will integrate seamlessly with existing municipal infrastructures. The deployment of the system will occur both centrally and at the edge, ensuring real-time decision-making capabilities. This project not only addresses the urgent need for sustainable waste management practices but also aligns with regulatory pressures for cleaner urban environments. Our AI solution promises significant cost savings and environmental impact reductions, setting a new standard for smart city operations.