1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
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
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 1.7.0-1
- Package Spec generated
|
