Metadata-Version: 2.4
Name: planetsca
Version: 0.1.0
Summary: Mapping snow covered areas (SCA) from high-resolution PlanetScope images using Random Forest model
Project-URL: Homepage, https://test.pypi.org/project/PyPlanetSCA/#description
Project-URL: Repository, https://github.com/DSHydro/PyPlanetSCA-Python-Library
Author-email: Ian Chiu <ianchiu333@gmail.com>, Nicoleta Cristea <cristn@uw.edu>, Steven Pestana <spestana@uw.edu>, Kehan Yang <kyang33@uw.edu>
License: MIT License
        
        Copyright (c) [2024] [Ian Chiu]
        
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License-File: LICENSE
Requires-Python: >=3.8
Requires-Dist: dvc>=2.10.0
Requires-Dist: fiona
Requires-Dist: geopandas
Requires-Dist: huggingface-hub>=0.23.4
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Description-Content-Type: text/markdown

# PlanetSCA

**PlanetSCA** is an open-source Python library for mapping snow-covered areas
(SCA) from high-resolution PlanetScope images using a Random Forest model.
PlanetScope images can be accessed at [Planet.com](planet.com).

This package was developed from
[original work by Kehan Yang and others](#citations).

This library also includes access to a
[pre-trained model](https://huggingface.co/geo-smart/planetsca_models) for
mapping SCA in PlanetScope imagery, and
[sample data](https://huggingface.co/datasets/geo-smart/planetsca_datasets) to
demonstrate the library's functions.

The search and download functions require you to have an account with Planet and
an [API key](https://developers.planet.com/quickstart/apis/#find-your-api-key).

![planetsca_flowchart](https://github.com/user-attachments/assets/be1569be-bf4a-4522-aa38-443411e8c026)

## Documentation

Please see the **Getting Started** pages of the
[website](https://dshydro.github.io/planetsca/) for installation and basic usage
examples. See the **API Reference** pages for detailed documentation.

## Citations

When using this package, please **cite both the package and the original study
describing the model**:

Citing PlanetSCA:

1. the name of the author (Chiu et al.),
2. the title of the software or code (planetsca),
3. the version number, the publication date and the unique identifier (PID)

Citing the original study:

- Yang K., John A., Shean D., Lundquist J.D., Sun Z., Yao F., Todoran S., and
  Cristea N. (2023) High-resolution mapping of snow cover in montane meadows and
  forests using Planet imagery and machine learning. Front. Water 5:1128758.
  doi: [10.3389/frwa.2023.1128758](https://doi.org/10.3389/frwa.2023.1128758)

Other material of interest:

- Code from the original study which was adapted into this library can be found
  [here](https://github.com/KehanGit/High_resolution_snow_cover_mapping).
- A tutorial describing the random forest model in the original study is
  published as a GeoScience Machine Learning Resources and Training (GeoSMART)
  [here](https://geo-smart.github.io/scm_geosmart_use_case/chapters/one.html).
