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https://github.com/tushar2704/geoinsight
GeoInsight Pro is an advanced geospatial AI application designed to revolutionize satellite imagery analysis using cutting-edge technologies like Streamlit and Groq API.
https://github.com/tushar2704/geoinsight
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GeoInsight Pro is an advanced geospatial AI application designed to revolutionize satellite imagery analysis using cutting-edge technologies like Streamlit and Groq API.
- Host: GitHub
- URL: https://github.com/tushar2704/geoinsight
- Owner: tushar2704
- License: mit
- Created: 2024-11-27T11:36:01.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-27T12:01:50.000Z (about 2 months ago)
- Last Synced: 2024-11-27T12:33:01.037Z (about 2 months ago)
- Topics: gis, tushar2704
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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
```bash
git clone https://github.com/tushar2704/geoinsight.git
cd geoinsight-pro
```
### 2. Create Virtual Environment
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
### 3. Install Dependencies
```bash
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`
```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
```bash
streamlit run app.py
```
### Production Deployment
```bash
# Recommended: Use Streamlit sharing or cloud platforms
streamlit run app.py --server.port 8501
```
## Docker Deployment
### Dockerfile
```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
```bash
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
```python
# 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](https://www.linkedin.com/in/tusharaggarwalinseec/)
---
**Disclaimer:** This is a demonstration project for educational purposes.