Metadata-Version: 2.1
Name: eeharvest
Version: 1.0.0
Summary: A simple harvester for Google Earth Engine
Home-page: https://github.com/sydney-informatics-hub/eeharvest
Author: Januar Harianto
Author-email: januar.harianto@sydney.edu.au
License: LGPLv3
Project-URL: Documentation, https://github.com/sydney-informatics-hub/eeharvest
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Requires-Python: >=3.9
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
License-File: LICENSE.txt
Requires-Dist: alive-progress
Requires-Dist: earthengine-api
Requires-Dist: eemont
Requires-Dist: geedim
Requires-Dist: geemap
Requires-Dist: pyyaml
Requires-Dist: termcolor
Requires-Dist: yamale
Requires-Dist: importlib-resources ; python_version < "3.10"
Requires-Dist: importlib-metadata ; python_version < "3.8"
Provides-Extra: testing
Requires-Dist: setuptools ; extra == 'testing'
Requires-Dist: pytest ; extra == 'testing'
Requires-Dist: pytest-cov ; extra == 'testing'
Requires-Dist: pytest-sugar ; extra == 'testing'

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# eeharvest

Simplify the download and processing of Google Earth Engine data by leveraging
on available python packages into three (3) intuitive functions:

- `preprocess()`: server-side processing, cloud and shadow masking, image
  reduction and calculation of spectral indices
- `map()`: preview data rasters on an interactive map before downloading
- `download()`: download data collection, ready for client-side processing
<!-- - `aggregate()`: perform additional temporal and/or spatial aggregaton on data -->

## Installation

On PyPi:

```sh
pip install eeharvest
```

Or use `conda`:

```sh
# conda install eeharvest - WORK IN PROGRESS
```

You may need to install the following packages manually:

- [GDAL](https://gdal.org/download.html): to manipulate raster and vector
  geospatial data.
- [gcloud
  CLI](https://cloud.google.com/sdk/docs/install): needed to authenticate
  to Google servers.

In most cases, these can be installed through conda-forge:

```sh
conda install -c conda-forge gdal google-cloud-sdk
```

<!-- pyscaffold-notes -->

## Attribution and Acknowledgments

This software was developed by the Sydney Informatics Hub, a core research
facility of the University of Sydney, as part of the Data Harvesting project for
the Agricultural Research Federation (AgReFed).
AgReFed is supported by the Australian Research Data Commons (ARDC) and the
Australian Government through the National Collaborative Research Infrastructure
Strategy (NCRIS).

Acknowledgments are an important way for us to demonstrate the value we bring to
your research. Your research outcomes are vital for ongoing funding of the
Sydney Informatics Hub. If you make use of this software for your research
project, please include the following acknowledgment:

> This research was supported by the Sydney Informatics Hub, a Core Research
> Facility of the University of Sydney, and the Agricultural Research Federation
> (AgReFed).

## Note

This project has been set up using [PyScaffold] 4.3.1 and the [dsproject
extension] 0.7.2.

[conda]: https://docs.conda.io/
[pre-commit]: https://pre-commit.com/
[jupyter]: https://jupyter.org/
[nbstripout]: https://github.com/kynan/nbstripout
[google style]: http://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings
[pyscaffold]: https://pyscaffold.org/
[dsproject extension]: https://github.com/pyscaffold/pyscaffoldext-dsproject
