Satellite AI 🛰️

A modern web application that uses AI to classify different types of areas from satellite images. The application can identify various terrains like deserts, plains, and other landscapes from satellite imagery.

🌟 Features

• 🔍 Accurate Detection: High-precision satellite image classification
• 🌍 Global Coverage: Analyze satellite imagery from anywhere
• ⚡ Real-time Processing: Fast and efficient image analysis
• 🌤️ Weather Independent: Reliable analysis in any conditions

🚀 Tech Stack

Frontend

• Next.js 13+ with App Router
• TypeScript
• Tailwind CSS
• Shadcn UI Components
• Framer Motion for animations

Backend

• Python
• FastAPI
• TensorFlow/Keras
• Docker

🛠️ Installation

Prerequisites

• Docker and Docker Compose
• Node.js 16+ (for local development)
• Python 3.8+ (for local development)

Using Docker (Recommended)

1. Clone the repository:

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

2. Start the application using Docker Compose:

docker-compose up --build

The application will be available at:

• Frontend: http://localhost:3000
• Backend API: http://localhost:8000

Manual Setup

Frontend Setup

cd frontend
npm install
npm run dev

Backend Setup

cd backend
python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate
pip install -r requirements.txt
python app.py

🌐 Environment Variables

Frontend

• NEXT_PUBLIC_API_URL: Backend API URL
• NODE_ENV: Production/Development environment

Backend

• PYTHONPATH: Python path configuration
• ALLOWED_URL: Frontend URL for CORS

📦 Project Structure

satellite-ai/
├── frontend/              # Next.js frontend application
│   ├── src/              # Source code
│   ├── public/           # Static files
│   └── ...
├── backend/              # Python FastAPI backend
│   ├── app.py           # Main application file
│   ├── model/           # AI model files
│   └── ...
└── docker-compose.yml    # Docker compose configuration

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

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

🙏 Acknowledgments

• Dataset sourced from Kaggle - Satellite Image Classification
• Inspired by various satellite image classification projects