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https://github.com/sentinel-hub/sentinel2-cloud-detector
Sentinel Hub Cloud Detector for Sentinel-2 images in Python
https://github.com/sentinel-hub/sentinel2-cloud-detector
classification cloud-detector python-library sentinel-2 sentinel-hub
Last synced: 5 days ago
JSON representation
Sentinel Hub Cloud Detector for Sentinel-2 images in Python
- Host: GitHub
- URL: https://github.com/sentinel-hub/sentinel2-cloud-detector
- Owner: sentinel-hub
- License: cc-by-sa-4.0
- Created: 2018-01-22T13:28:57.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-09-27T09:10:26.000Z (4 months ago)
- Last Synced: 2025-01-16T00:35:17.903Z (11 days ago)
- Topics: classification, cloud-detector, python-library, sentinel-2, sentinel-hub
- Language: Python
- Size: 57.8 MB
- Stars: 440
- Watchers: 21
- Forks: 95
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- awesome-earthobservation-code - sentinel2-cloud-detector - Sentinel Hub Cloud Detector for Sentinel-2 images in `Python` (`Python` processing of optical imagery (non deep learning) / Company specific examples)
README
[](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 >= 3.8. 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
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.