EcoPolis - AI-Driven Urban Biodiversity Planner

An interactive web application that helps urban planners make biodiversity-conscious development decisions using machine learning, GIS data analysis, and sustainability recommendations.

🌿 Project Overview

EcoPolis is a tool that addresses the challenge of preserving urban biodiversity in the face of expanding urbanization. It combines GIS data, machine learning, and interactive visualization to help urban planners:

• Identify biodiversity hotspots
• Predict the impact of urban development on local ecosystems
• Generate actionable recommendations for sustainable urban planning

🚀 Features

GIS-based Biodiversity Visualization

• Interactive mapping of urban areas, vegetation, and biodiversity hotspots
• Integration with QGIS and Geopandas for spatial data handling
• Zoom and click functionality for detailed biodiversity metrics

Impact Prediction Model

• Machine learning model to predict development impact on local biodiversity
• Considers factors like climate data, land use changes, and vegetation cover
• Provides biodiversity risk scores for different development scenarios

Sustainability Recommendation System

• Suggests optimal green corridors for wildlife movement
• Identifies areas for native vegetation preservation
• Recommends biodiversity-friendly infrastructure modifications

User Interface

• Web-based interface for data upload and visualization
• Support for GIS files and climate data input
• Interactive scenario planning tools

🛠️ Technical Stack

• Frontend: React + Vite
• Backend: Python
• ML Framework: Jupyter notebooks for model development
• Data Processing: Geopandas, QGIS
• Deployment: Heroku/GitHub Pages compatible

📁 Project Structure

├── ML/                     # Machine learning models and notebooks
├── src/                    # Source code
├── .env                    # Environment variables
├── .gitignore             # Git ignore rules
├── README.md              # Project documentation
├── eslint.config.js       # ESLint configuration
├── index.html             # Main HTML file
├── models2.pkl            # Trained ML models
├── package.json           # Project dependencies
├── package-lock.json      # Dependency lock file
├── postcss.config.js      # PostCSS configuration
├── tailwind.config.js     # Tailwind CSS configuration
└── vite.config.js         # Vite configuration

🔍 Machine Learning Components

The ML directory contains:

• All.ipynb: Complete model training pipeline
• Inference.ipynb: Model inference and testing
• LLM-baseline.ipynb: Baseline model implementation
• Reasoning_prompt.ipynb: Prompt engineering for recommendations
• ensemble_weights2.pkl: Trained ensemble model weights
• preprocessors2.pkl: Data preprocessing pipelines

🚀 Getting Started

1. Clone the repository:

git clone https://github.com/your-username/ecopolis.git
cd ecopolis

2. Install dependencies:

npm install          # Frontend dependencies
pip install -r requirements.txt  # Python dependencies

3. Set up environment variables:

• Create a .env file based on .env.example
• Add your API keys for weather and biodiversity data services

4. Run the development server:

npm run dev

📊 Data Sources

• Urban and Biodiversity GIS Data: OpenStreetMap, USGS Earth Explorer
• Climate Data: NOAA, WorldClim
• Land Use Data: Various classification datasets

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• OpenStreetMap contributors for GIS data
• NOAA and WorldClim for climate data
• The open-source community for various tools and libraries used in this project