Metadata-Version: 2.4
Name: eoml
Version: 0.9.1
Summary: library to manage GIS operation
Project-URL: Homepage, https://ciatgit.ciat.cgiar.org/Data_driven_sustainability_public/terra-i/eoml#
Project-URL: Documentation, https://ciatgit.ciat.cgiar.org/Data_driven_sustainability_public/terra-i/eoml#
Project-URL: Repository, https://ciatgit.ciat.cgiar.org/Data_driven_sustainability_public/terra-i/eoml#
Project-URL: Bug Tracker, https://ciatgit.ciat.cgiar.org/Data_driven_sustainability_public/terra-i/eoml/-/issues
Author-email: Thibaud Vantalon <t.vantalon@cgiar.org>
Maintainer-email: Thibaud Vantalon <t.vantalon@cgiar.org>
License: MIT License
Keywords: GIS,Rasterio
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Requires-Python: >=3.12
Requires-Dist: fiona
Requires-Dist: geopandas
Requires-Dist: lmdb
Requires-Dist: msgpack
Requires-Dist: numpy
Requires-Dist: pydantic>=2.6
Requires-Dist: pyproj
Requires-Dist: rasterio
Requires-Dist: rasterop
Requires-Dist: rasterstats
Requires-Dist: scikit-learn
Requires-Dist: shapely
Requires-Dist: tensorboard
Requires-Dist: toml
Requires-Dist: tomli
Requires-Dist: torchmetrics
Requires-Dist: torchvision
Requires-Dist: tqdm
Requires-Dist: typer
Description-Content-Type: text/markdown

# 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#}
}
```
