Metadata-Version: 2.4
Name: planetary-imaging
Version: 0.0.1
Summary: Building pipelines and models for planetary imaging data
Author-email: Jacob Prince-Bieker <jacob@bieker.tech>
License: MIT License
Project-URL: repository, https://github.com/jacobbieker/planetary-imaging
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.12.0
Description-Content-Type: text/markdown
Requires-Dist: loguru>=0.7.3
Requires-Dist: numpy>=1.23.2

# Planetary Imaging
 
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This project is focused on models and workflows to process primarily geostationary satellite imagery for a few starting uses:

1. Combined global imagery -> an open reproduction of [Zeus AI combined global imagery](https://myzeus.ai/zeus-ais-globally-harmonized-geostationary-infrared-analysis/)
2. Dense Atmospheric motion vectors -> an open reproduction of [Zeus AI dense atmospheric motion vectors](https://myzeus.ai/90-million-wind-vectors-every-10-minutes-dense-stereo-wind-retrieval-from-geostationary-satellites/)

These are starting points, as the project aims to extend those ideas to all imaging channels, and potentially other satellite imagery sources.
The main sources used initially would be easily the publicly available satellites, GOES, Himawari, GK-2A, and Meteosat satellites, nicely in Icechunk and available from [Source Cooperative](https://source.coop/bkr/geo)


## Installation

## Example usage

## Documentation

## FAQ

## Development


### Running the test suite
