# EOML - Earth Observation Machine Learning

A Python library for managing GIS operations and machine learning workflows for remote sensing applications.

## Overview

EOML provides a comprehensive toolkit for processing Earth observation data and building machine learning models for 
satellite imagery analysis. The library integrates rasterio, PyTorch, and Google Earth Engine to streamline geospatial
machine learning workflows.

## Features

- **PyTorch Integration**: Pre-built CNN architectures and training utilities for remote sensing


## Installation 

### PyPI
pip install eoml

### Developement mode
Installation in development mode:
```bash
pip install -e .
```



### Running Tests

```bash
pytest tests/
```

## Contributing

Contributions are welcome! Please ensure code follows the project style and includes appropriate docstrings.

## License
MIT License

## Author

**Thibaud Vantalon**
Email: t.vantalon@cgiar.org
Organization: CGIAR

## Citation

If you use this library in your research, please cite:

```bibtex
@software{eoml,
  author = {Vantalon, Thibaud},
  title = {EOML: Earth Observation Machine Learning},
  year = {2024},
  url = {https://ciatgit.ciat.cgiar.org/Data_driven_sustainability_public/terra-i/eoml#}
}
```
