diff options
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-s2cloudless.spec | 259 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 261 insertions, 0 deletions
@@ -0,0 +1 @@ +/s2cloudless-1.7.0.tar.gz diff --git a/python-s2cloudless.spec b/python-s2cloudless.spec new file mode 100644 index 0000000..61b48bb --- /dev/null +++ b/python-s2cloudless.spec @@ -0,0 +1,259 @@ +%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 +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 1.7.0-1 +- Package Spec generated @@ -0,0 +1 @@ +62214e9dd4618f6ba740aa88fd29d3e4 s2cloudless-1.7.0.tar.gz |