%global _empty_manifest_terminate_build 0
Name: python-s2cloudless
Version: 1.7.0
Release: 1
Summary: Sentinel Hub's cloud detector for Sentinel-2 imagery
License: CC BY-SA 4.0
URL: https://github.com/sentinel-hub/sentinel2-cloud-detector
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a6/03/2b226619795b87bac5367fd5dd90bd7df0199cf4a9fc0905926f27d8a91f/s2cloudless-1.7.0.tar.gz
BuildArch: noarch
Requires: python3-lightgbm
Requires: python3-numpy
Requires: python3-scikit-image
Requires: python3-scipy
Requires: python3-sentinelhub
Requires: python3-typing-extensions
Requires: python3-codecov
Requires: python3-mypy
Requires: python3-pre-commit
Requires: python3-pylint
Requires: python3-pytest-cov
Requires: python3-pytest
Requires: python3-twine
%description
[](https://pypi.org/project/s2cloudless)
[](https://anaconda.org/conda-forge/s2cloudless)
[](https://pypi.org/project/s2cloudless)
[](https://github.com/sentinel-hub/sentinel2-cloud-detector/actions)
[](https://pepy.tech/project/s2cloudless)
[](https://pepy.tech/project/s2cloudless)
[](https://codecov.io/gh/sentinel-hub/sentinel2-cloud-detector)
# 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 detail
[in this blog](https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13).
The **s2cloudless** algorithm was part of an international collaborative effort aimed at intercomparing cloud detection algorithms. The s2cloudless algorithm was validated together with 9 other algorithms on 4 different test datasets and in all cases found to be on the Pareto front. See [the paper](https://www.sciencedirect.com/science/article/pii/S0034425722001043?via%3Dihub)
## Installation
The package requires a Python version >= 3.7. 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 values in the following way: `B_i/10000`. From product baseline `04.00` onward additional harmonization factors have to be applied to data according to [instructions from ESA](https://sentinels.copernicus.eu/en/web/sentinel/-/copernicus-sentinel-2-major-products-upgrade-upcoming).
You don't need to worry about any of this, if you are using Sentinel-2 data obtained from [Sentinel Hub Process API](https://docs.sentinel-hub.com/api/latest/api/process/). By default, the data is already harmonized according to [documentation](https://docs.sentinel-hub.com/api/latest/data/sentinel-2-l1c/#harmonize-values). The API is supported in Python with [sentinelhub-py](https://github.com/sentinel-hub/sentinelhub-py) package and used within `s2cloudless.CloudMaskRequest` class.
## Examples
A 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>.
%package -n python3-s2cloudless
Summary: Sentinel Hub's cloud detector for Sentinel-2 imagery
Provides: python-s2cloudless
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-s2cloudless
[](https://pypi.org/project/s2cloudless)
[](https://anaconda.org/conda-forge/s2cloudless)
[](https://pypi.org/project/s2cloudless)
[](https://github.com/sentinel-hub/sentinel2-cloud-detector/actions)
[](https://pepy.tech/project/s2cloudless)
[](https://pepy.tech/project/s2cloudless)
[](https://codecov.io/gh/sentinel-hub/sentinel2-cloud-detector)
# 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 detail
[in this blog](https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13).
The **s2cloudless** algorithm was part of an international collaborative effort aimed at intercomparing cloud detection algorithms. The s2cloudless algorithm was validated together with 9 other algorithms on 4 different test datasets and in all cases found to be on the Pareto front. See [the paper](https://www.sciencedirect.com/science/article/pii/S0034425722001043?via%3Dihub)
## Installation
The package requires a Python version >= 3.7. 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 values in the following way: `B_i/10000`. From product baseline `04.00` onward additional harmonization factors have to be applied to data according to [instructions from ESA](https://sentinels.copernicus.eu/en/web/sentinel/-/copernicus-sentinel-2-major-products-upgrade-upcoming).
You don't need to worry about any of this, if you are using Sentinel-2 data obtained from [Sentinel Hub Process API](https://docs.sentinel-hub.com/api/latest/api/process/). By default, the data is already harmonized according to [documentation](https://docs.sentinel-hub.com/api/latest/data/sentinel-2-l1c/#harmonize-values). The API is supported in Python with [sentinelhub-py](https://github.com/sentinel-hub/sentinelhub-py) package and used within `s2cloudless.CloudMaskRequest` class.
## Examples
A 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>.
%package help
Summary: Development documents and examples for s2cloudless
Provides: python3-s2cloudless-doc
%description help
[](https://pypi.org/project/s2cloudless)
[](https://anaconda.org/conda-forge/s2cloudless)
[](https://pypi.org/project/s2cloudless)
[](https://github.com/sentinel-hub/sentinel2-cloud-detector/actions)
[](https://pepy.tech/project/s2cloudless)
[](https://pepy.tech/project/s2cloudless)
[](https://codecov.io/gh/sentinel-hub/sentinel2-cloud-detector)
# 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 detail
[in this blog](https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13).
The **s2cloudless** algorithm was part of an international collaborative effort aimed at intercomparing cloud detection algorithms. The s2cloudless algorithm was validated together with 9 other algorithms on 4 different test datasets and in all cases found to be on the Pareto front. See [the paper](https://www.sciencedirect.com/science/article/pii/S0034425722001043?via%3Dihub)
## Installation
The package requires a Python version >= 3.7. 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 values in the following way: `B_i/10000`. From product baseline `04.00` onward additional harmonization factors have to be applied to data according to [instructions from ESA](https://sentinels.copernicus.eu/en/web/sentinel/-/copernicus-sentinel-2-major-products-upgrade-upcoming).
You don't need to worry about any of this, if you are using Sentinel-2 data obtained from [Sentinel Hub Process API](https://docs.sentinel-hub.com/api/latest/api/process/). By default, the data is already harmonized according to [documentation](https://docs.sentinel-hub.com/api/latest/data/sentinel-2-l1c/#harmonize-values). The API is supported in Python with [sentinelhub-py](https://github.com/sentinel-hub/sentinelhub-py) package and used within `s2cloudless.CloudMaskRequest` class.
## Examples
A 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>.
%prep
%autosetup -n s2cloudless-1.7.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-s2cloudless -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 1.7.0-1
- Package Spec generated