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"info": {
"author": "Sinergise EO research team",
"author_email": "anze.zupanc@sinergise.com",
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"Development Status :: 5 - Production/Stable",
"Intended Audience :: Education",
"Intended Audience :: Science/Research",
"Operating System :: MacOS",
"Operating System :: Microsoft :: Windows",
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"Programming Language :: Python",
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"description": "[](https://pypi.org/project/s2cloudless/)\n[](https://pypi.org/project/s2cloudless/)\n[](https://travis-ci.org/sentinel-hub/sentinel2-cloud-detector)\n[](https://pepy.tech/project/s2cloudless)\n[](https://pepy.tech/project/s2cloudless)\n\n# Sentinel Hub's cloud detector for Sentinel-2 imagery\n\nThe **s2cloudless** Python package provides automated cloud detection in\nSentinel-2 imagery. The classification is based on a *single-scene pixel-based cloud detector*\ndeveloped by Sentinel Hub's research team and is described in more details\n[in this blog](https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13).\n\n## Installation\n\nThe package requires a Python version >= 3.5. The package is available on\nthe PyPI package manager and can be installed with\n\n```\n$ pip install s2cloudless\n```\n\nTo install the package manually, clone the repository and\n```\n$ python setup.py build\n$ python setup.py install\n```\n\nOne of `s2cloudless` dependecies is `lightgbm` package. If having problems during installation please\ncheck [LightGBM installation guide](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html).\n\nBefore installing `s2cloudless` on **Windows** it is recommended to install package `shapely` from\n[Unofficial Windows wheels repository](https://www.lfd.uci.edu/~gohlke/pythonlibs/)\n\n## Input: Sentinel-2 scenes\n\nThe input to cloud detector are Sentinel-2 images. In particular, the cloud detector requires the following 10\nSentinel-2 band reflectances: B01, B02, B04, B05, B08, B8A, B09, B10, B11, B12, which are obtained from raw\nreflectance value in the following way: `B_i/10000`.\n\nYou don't need to worry about any of this, if you're doing classification of scenes obtained using Sentinel Hub's\nWMS or WCS services (i.e. using ours Python library [sentinelhub-py](https://github.com/sentinel-hub/sentinelhub-py)).\n\n## Examples\n\nJupyter notebook on how to use the cloud detector to produce cloud mask or cloud probability map\ncan be found in the [examples folder](https://github.com/sentinel-hub/sentinel2-cloud-detector/tree/master/examples).\n\n## License\n\n\n![\"Creative](\"https://i.creativecommons.org/l/by-sa/4.0/88x31.png\")
\n
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