.. pyeogpr documentation master file, created by sphinx-quickstart on Mon Jul 23 2024. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. .. image:: logo.png :scale: 50% Welcome to pyeogpr's documentation! ==================================== .. image:: https://img.shields.io/pypi/v/pyeogpr.svg :target: https://pypi.org/project/pyeogpr/ .. image:: https://img.shields.io/badge/GitHub-pyeogpr-purple.svg :target: https://github.com/daviddkovacs/pyeogpr .. image:: https://img.shields.io/badge/IPL-Flexinel-yellow :target: https://leoipl.uv.es/flexinel/ :alt: Flexinel .. image:: https://img.shields.io/badge/DOI-10.5281%2Fzenodo.13373838-blue :target: https://doi.org/10.5281/zenodo.13373838 :alt: Zenodo DOI Python based library to use Earth Observation data to retrieve biophysical maps using Gaussian Process Regression Features -------- - Access to `openEO `_ or `Google Earth Engine `_ is required. - Advantages of **openEO**: Download locally netcdf or tiff format. no uncertainty estimates, smaller areas - Advantages of **Google Earth Engine**: Export to ee.Assets with Uncertainty estimates, and up to global scale processing. - The package uses satellite observations and machine learning to create vegetation trait maps - Get your maps in a few lines of code: no Machine Learning, coding or remote sensing knowledge needed! - Built in gap filling to avoid cloud cover - Runs "in the cloud" with the GEE/openEO API. No local processing needed. Installation ------------ You can install pyeogpr using pip via the command line: .. code-block:: shell pip install pyeogpr Usage ----- Please refer to the documentation to use either GEE or openEO based processing. .. toctree:: :maxdepth: 2 :caption: Documentation pyeogpr .. toctree:: :maxdepth: 2 :caption: Satellites and variables sensors Contact ======= Dávid D.Kovács - `daviddkovacs@gmail.com `_ .. image:: flexinel.png