Predictive Urban Growth Analysis Using AI & Machine Learning

The project seeks to develop an AI & machine learning-based solution that can forecast urban growth trends, enabling cities to respond proactively to developmental changes. Urban planning departments often struggle with anticipating the pace and direction of city expansion, leading to inefficient infrastructure development and resource allocation. Our initiative will integrate Large Language Models (LLMs) and computer vision to analyze historical data, satellite imagery, and socio-economic indicators. By employing tools such as Langchain and Pinecone, we will create a platform that processes vast datasets, offering planners a comprehensive view of potential growth areas. The solution will be capable of running predictive analyses using TensorFlow and PyTorch frameworks, ensuring robust and scalable forecasts. Our goal is to facilitate sustainable urban development, ensuring cities can support their growing populations while maintaining quality of life. The project timeline is set for 16-24 weeks, with a budget between $50,000 - $150,000, reflecting the complex nature of urban data integration and analysis.