Metadata-Version: 2.1
Name: s2cloudless
Version: 1.5.0
Summary: Sentinel Hub's cloud detector for Sentinel-2 imagery
Home-page: https://github.com/sentinel-hub/sentinel2-cloud-detector
Author: Sinergise EO research team
Author-email: anze.zupanc@sinergise.com
License: CC BY-SA 4.0
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        # Sentinel Hub's cloud detector for Sentinel-2 imagery
        
        **NOTE: s2cloudless masks are now available as a precomputed layer within Sentinel Hub. Check the [announcement blog post](https://medium.com/sentinel-hub/cloud-masks-at-your-service-6e5b2cb2ce8a) and [technical documentation](https://docs.sentinel-hub.com/api/latest/#/API/data_access?id=cloud-masks-and-cloud-probabilities).**
        
        The **s2cloudless** Python package provides automated cloud detection in
        Sentinel-2 imagery. The classification is based on a *single-scene pixel-based cloud detector*
        developed by Sentinel Hub's research team and is described in more details
        [in this blog](https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13).
        
        ## Installation
        
        The package requires a Python version >= 3.6. The package is available on
        the PyPI package manager and can be installed with
        
        ```
        $ pip install s2cloudless
        ```
        
        To install the package manually, clone the repository and
        ```
        $ pip install .
        ```
        
        One of `s2cloudless` dependencies is `lightgbm` package. If having problems during installation, please
        check the [LightGBM installation guide](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html).
        
        Before installing `s2cloudless` on **Windows**, it is recommended to install package `shapely` from
        [Unofficial Windows wheels repository](https://www.lfd.uci.edu/~gohlke/pythonlibs/)
        
        ## Input: Sentinel-2 scenes
        
        The inputs to the cloud detector are Sentinel-2 images. In particular, the cloud detector requires the following 10
        Sentinel-2 band reflectances: B01, B02, B04, B05, B08, B8A, B09, B10, B11, B12, which are obtained from raw
        reflectance value in the following way: `B_i/10000`.
        
        You don't need to worry about any of this, if you're doing classification of scenes obtained using [Sentinel Hub
        Process API](https://docs.sentinel-hub.com/api/latest/api/process/). The API is supported in Python with 
        [sentinelhub-py](https://github.com/sentinel-hub/sentinelhub-py) package.
        
        ## Examples
        
        Jupyter notebook on how to use the cloud detector to produce cloud mask or cloud probability map
        can be found in the [examples folder](https://github.com/sentinel-hub/sentinel2-cloud-detector/tree/master/examples).
        
        ## License
        
        <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">
        <img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a>
        <br />
        This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: DEV
