https://github.com/daleroberts/s2cloudmask
Sentinel-2 Cloud and Shadow Detection using Machine Learning
https://github.com/daleroberts/s2cloudmask
machine-learning python python3 satellite-imagery sentinel2
Last synced: about 1 year ago
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Sentinel-2 Cloud and Shadow Detection using Machine Learning
- Host: GitHub
- URL: https://github.com/daleroberts/s2cloudmask
- Owner: daleroberts
- Created: 2018-09-19T03:41:50.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2020-07-07T23:42:36.000Z (about 6 years ago)
- Last Synced: 2025-03-26T22:51:18.651Z (over 1 year ago)
- Topics: machine-learning, python, python3, satellite-imagery, sentinel2
- Language: Python
- Homepage:
- Size: 47.8 MB
- Stars: 14
- Watchers: 4
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# s2cloudmask
[](https://opensource.org/licenses/Apache-2.0) 
The [s2cloudmask](https://github.com/daleroberts/s2cloudmask) Python package provides machine learning classifiers for **Cloud and Shadow Detection** in [Sentinel-2](https://en.wikipedia.org/wiki/Sentinel-2) observations. The aim of this package is to open-source and showcase some of the tools being developed as part of the [Digital Earth Australia](https://www.ga.gov.au/dea) initiative, and further, to push the state-of-art in the area of cloud classification.
The package currently provides a number of classifiers:
* `spectral`: A spectral pixel-based cloud classifier
* `fast`: A spectral pixel-based cloud classifier (using a decision tree for speed and interpretability)
* `temporal`: A spectral-temporal pixel-based cloud classifier
* `fast-shadow`: A spectral-temporal pixel-based shadow classifier (using a decision tree for speed and interpretability)
The *spectral classifiers* are useful if you only have a couple of observations (i.e., satellite images) while the the *spectral-temporal classifiers* (aka. *temporal classifiers*) give better classifications of clouds (and shadows) provided that you can supply it with a geomedian pixel-composite mosaic [Roberts et al. 2017] of the area (or a stack of data so that one can be created by this package).
We note the existence of Python package [s2cloudless](https://github.com/sentinel-hub/sentinel2-cloud-detector) developed by [Sentinel Hub](https://www.sentinel-hub.com/)'s research team that, as they argue in their [blog post](https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13), "didn't observe significant improvement using derived features instead of raw band values" so their "final classifier uses the following 10 bands as input: B01, B02, B04, B05, B08, B8A, B09, B10, B11, B12". By releasing this package, we argue the contrary and demonstrate that you can obtain a better classification of clouds by (thinking hard and) developing new derived features for your machine learning algorithm.
In the image above: Baseline is s2cloudless, Spectral is our spectral classifier, Temporal is our temporal classifier.
### Installation
```
$ pip install git+https://github.com/daleroberts/s2cloudmask
```
### Easy interface
This package has an easy interface. Given a numpy array `obs` ordered as (y,x,band) we can obtain a cloud `mask`.
```
>>> import s2cloudmask as s2cm
>>> mask = s2cm.cloud_mask(obs, model='spectral')
```
### Tests
Tests (and examples) are available in `tests/test_.py` and can be run with pytest from the project root.
```
$ pytest
```
### Further References
You may be interested to read:
Roberts, D., Mueller, N., McIntyre, A. (2017). [High-dimensional pixel composites from Earth observation time series](https://ieeexplore.ieee.org/document/8004469). *IEEE Transactions on Geoscience and Remote Sensing*, PP, 99. pp. 1--11.
or maybe some of [my other open-source projects](https://github.com/daleroberts).
### Examples
#### Cloud detection
#### Cloud and Shadow detection
