Sustainable and Cost-Effective School Networks

This project integrates geographical, meteorological, and population data to recommend sustainable network infrastructure for schools, ensuring cost efficiency and functionality. It uses OpenCage Geocoder to convert pincodes into latitude and longitude, Earth Engine for retrieving administrative area data, and datasets like MODIS, ERA5, and WorldPop to analyze solar and wind power potential, cloud cover, and population density. A custom function processes geographical metrics like NDVI, LST, precipitation, and elevation using datasets such as Landsat 8, CHIRPS, and SRTM. The LangChain framework, paired with OpenAI's GPT-4 model, generates detailed recommendations for required devices, school size estimation (based on population density), cost breakdown, and suitable approaches. The infrastructure is tailored considering environmental factors, emphasizing cost-saving measures like energy-efficient devices and refurbished equipment. Results are exported as a comprehensive PDF, offering a dynamic decision-support system for sustainable school infrastructure planning.