GeoInsight Pro: Satellite AI Analysis Platform

Overview

GeoInsight Pro is an advanced geospatial AI application designed to revolutionize satellite imagery analysis using cutting-edge technologies like Streamlit and Groq API.

🚀 Features

Key Capabilities

• Satellite Image Classification
• Climate Change Visualization
• Environmental Impact Assessment
• AI-Powered Geospatial Analysis

🛠 Technology Stack

• Frontend: Streamlit
• AI Processing: Groq API
• Data Visualization: Plotly
• Image Processing: OpenCV, PIL
• Machine Learning: scikit-learn, TensorFlow

Prerequisites

System Requirements

• Python 3.8+
• pip
• Virtual Environment (recommended)

API Keys Required

• Groq API Key
• (Optional) Additional geospatial data service keys

Installation Steps

1. Clone Repository

git clone https://github.com/tushar2704/geoinsight.git
cd geoinsight-pro

2. Create Virtual Environment

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file in project root:

GROQ_API_KEY=your_groq_api_key

Configuration

Configuration File: config.yaml

app:
  name: GeoInsight Pro
  version: 0.1.0

modules:
  image_classification: true
  climate_analysis: true
  environmental_impact: true

api:
  groq:
    model: llama2-70b-4096
    max_tokens: 1024

logging:
  level: INFO
  file: logs/app.log

Running the Application

Development Mode

streamlit run app.py

Production Deployment

# Recommended: Use Streamlit sharing or cloud platforms
streamlit run app.py --server.port 8501

Docker Deployment

Dockerfile

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

EXPOSE 8501

CMD ["streamlit", "run", "Home.py"]

Build and Run Docker

docker build -t geoinsight-pro .
docker run -p 8501:8501 geoinsight-pro

Project Structure

geoinsight-pro/
│
├── app.py               # Main Streamlit application
├── requirements.txt     # Project dependencies
├── config.yaml          # Configuration management
│
├── modules/
│   ├── image_processing.py
│   ├── ai_analysis.py
│   └── visualization.py
│
├── data/                # Sample datasets
│   └── satellite_images/
│
├── models/              # ML model artifacts
│   └── trained_models/
│
└── tests/               # Unit and integration tests
    ├── test_image_processing.py
    └── test_ai_analysis.py

Usage Examples

Basic Image Classification

# Example usage in Streamlit app
uploaded_file = st.file_uploader("Upload Satellite Image")
if uploaded_file:
    result = analyze_image(uploaded_file)
    st.write(result)

Contributing

Contribution Guidelines

1. Fork the repository
2. Create feature branch
3. Commit changes
4. Push to branch
5. Create pull request

Code Style

• Follow PEP 8 guidelines
• Use type hints
• Write comprehensive docstrings

Ethical Considerations

• Respect geographical data privacy
• Obtain proper image rights
• Ensure transparent AI decision-making

Limitations

• Dependent on satellite image quality
• AI model accuracy varies
• Computational resource intensive

Future Roadmap

• Multi-language support
• Enhanced ML models
• Real-time data streaming
• Advanced visualization techniques

License

MIT License

Contact

• Project Maintainer: Tushar Aggarwal

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Disclaimer: This is a demonstration project for educational purposes.