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https://github.com/sentinel-hub/eo-learn

Earth observation processing framework for machine learning in Python
https://github.com/sentinel-hub/eo-learn

eo-data eo-research machine-learning python-package

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Earth observation processing framework for machine learning in Python

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# eo-learn



**eo-learn makes extraction of valuable information from satellite imagery easy.**



The availability of open Earth observation (EO) data through the Copernicus and Landsat programs represents an

unprecedented resource for many EO applications, ranging from ocean and land use and land cover monitoring,

disaster control, emergency services and humanitarian relief. Given the large amount of high spatial resolution

data at high revisit frequency, techniques able to automatically extract complex patterns in such _spatio-temporal_

data are needed.



**`eo-learn`** is a collection of open source Python packages that have been developed to seamlessly access and process

_spatio-temporal_ image sequences acquired by any satellite fleet in a timely and automatic manner. **`eo-learn`** is

easy to use, it's design modular, and encourages collaboration -- sharing and reusing of specific tasks in a typical

EO-value-extraction workflows, such as cloud masking, image co-registration, feature extraction, classification, etc. Everyone is free

to use any of the available tasks and is encouraged to improve the, develop new ones and share them with the rest of the community.



**`eo-learn`** makes extraction of valuable information from satellite imagery as easy as defining a sequence of operations to be performed on satellite imagery. Image below illustrates a processing chain that maps water in satellite imagery by thresholding the Normalised Difference Water Index in user specified region of interest.



![](docs/source/figures/eo-learn-illustration.png)



**`eo-learn`** _library acts as a bridge between Earth observation/Remote sensing field and Python ecosystem for data science and machine learning._ The library is written in Python and uses NumPy arrays to store and handle remote sensing data. Its aim is to make entry easier for non-experts to the field of remote sensing on one hand and bring the state-of-the-art tools for computer vision, machine learning, and deep learning existing in Python ecosystem to remote sensing experts.



## Package Overview



**`eo-learn`** package is structured into several modules according to different functionalities. Some modules contain extensions under the `extra` subfolder. Those modules typically require additional package dependencies which don't get installed by default, since they are usually very specific to the task.



The modules are:



- **`core`** - The main module which implements basic building blocks (`EOPatch`, `EOTask` and `EOWorkflow`) and commonly used functionalities.

- **`coregistration`** - Tasks which deal with image co-registration.

- **`features`** - A collection of utilities for extracting data properties and feature manipulation.

- **`geometry`** - Geometry-related tasks used for transformation and conversion between vector and raster data.

- **`io`** - Input/output tasks that deal with obtaining data from Sentinel Hub services or saving and loading data locally.

- **`mask`** - Tasks used for masking of data and calculation of cloud/snow/other masks.

- **`ml-tools`** - Various tools that can be used before or after the machine learning process.

- **`visualization`** - Visualization tools for the core elements of eo-learn.



## Installation



### Requirements



The package requires Python version **>=3.8**.



#### Linux



Before installing `eo-learn` on **Linux** it is recommended to install the following system libraries:



```bash

sudo apt-get install gcc libgdal-dev graphviz proj-bin libproj-dev libspatialindex-dev

```



#### Mac OS



Before installing `eo-learn` on `Mac OS` it is recommended to install the following system libraries with [Homebrew](https://brew.sh/):



```bash

brew install graphviz gcc gdal cmake spatialindex proj

```



#### Windows



Before installing `eo-learn` on **Windows** it is recommended to install the following packages from [Unofficial Windows wheels repository](https://www.lfd.uci.edu/~gohlke/pythonlibs/):



```bash

gdal

rasterio

shapely

fiona

```



### PyPI distribution



`eo-learn` is available on PyPI and can be installed with:



```bash

pip install eo-learn

```



For some modules there are extra dependencies available, related to specific tasks. These were kept separate in order to keep the `eo-learn` installation light. You can install these with, e.g.:



```bash

pip install "eo-learn[EXTRA]"

pip install "eo-learn[VISUALIZATION]"

```



The full list (including their descriptions) is available here:



- `RAY` for installing ray and its dependencies

- `ZARR` for installing the zarr functionality for chunked timestamp saving/loading

- `EXTRA` for installing interpolation- and clustering-specific dependencies, or for installing `s2cloudless` in cloud masking

- `VISUALIZATION` for using plotting libraries and utilities

- `FULL` for installing all dependencies described so far

- `DOCS` for developers, dependencies for building documentation

- `DEV` for developers, dependencies for testing and code contribution



### Conda Forge distribution



The package requires a Python environment **>=3.8**.



Thanks to the maintainers of the conda forge feedstock (@benhuff, @dcunn, @mwilson8, @oblute, @rluria14), `eo-learn` can

be installed using `conda-forge` as follows:



```bash

conda config --add channels conda-forge

conda install eo-learn

```



### Run with Docker



A docker image with the latest released version of `eo-learn` is available at [Docker Hub](https://hub.docker.com/r/sentinelhub/eolearn). It provides a full installation of `eo-learn` together with a Jupyter notebook environment. You can pull and run it with:



```bash

docker pull sentinelhub/eolearn:latest

docker run -p 8888:8888 sentinelhub/eolearn:latest

```



An extended version of the `latest` image additionally contains all example notebooks and data to get you started with `eo-learn`. Run it with:



```bash

docker pull sentinelhub/eolearn:latest-examples

docker run -p 8888:8888 sentinelhub/eolearn:latest-examples

```



Both docker images can also be built manually from GitHub repository:



```bash

docker build -f docker/eolearn.dockerfile . --tag=sentinelhub/eolearn:latest

docker build -f docker/eolearn-examples.dockerfile . --tag=sentinelhub/eolearn:latest-examples

```



## Documentation



For more information on the package content, visit [readthedocs](https://eo-learn.readthedocs.io/).



## More Examples



Examples and introductions to the package can be found [here](https://github.com/sentinel-hub/eo-learn/tree/master/examples). A larger collection of examples is available at the [`eo-learn-examples`](https://github.com/sentinel-hub/eo-learn-examples) repository. While the examples there are not always up-to-date they can be a great source of ideas.



In the past, `eo-learn` served as a collection of many useful tasks, originating from various contributors or projects. In order to keep `eo-learn` light and easy to maintain, we have decided to move these specific tasks to [`eo-learn-examples/extra-tasks`](https://github.com/sentinel-hub/eo-learn-examples/tree/main/extra-tasks), .



## Contributions



The list of all `eo-learn` contributors are listed in the [credits file](./CREDITS.md). If you would like to contribute to `eo-learn`, please check our [contribution guidelines](./CONTRIBUTING.md).



## Blog posts and papers



- [Introducing eo-learn](https://medium.com/sentinel-hub/introducing-eo-learn-ab37f2869f5c) (by Devis Peressutti)

- [Land Cover Classification with eo-learn: Part 1 - Mastering Satellite Image Data in an Open-Source Python Environment](https://medium.com/sentinel-hub/land-cover-classification-with-eo-learn-part-1-2471e8098195) (by Matic Lubej)

- [Land Cover Classification with eo-learn: Part 2 - Going from Data to Predictions in the Comfort of Your Laptop](https://medium.com/sentinel-hub/land-cover-classification-with-eo-learn-part-2-bd9aa86f8500) (by Matic Lubej)

- [Land Cover Classification with eo-learn: Part 3 - Pushing Beyond the Point of “Good Enough”](https://medium.com/sentinel-hub/land-cover-classification-with-eo-learn-part-3-c62ed9ecd405) (by Matic Lubej)

- [Innovations in satellite measurements for development](https://blogs.worldbank.org/opendata/innovations-satellite-measurements-development)

- [Use eo-learn with AWS SageMaker](https://medium.com/@drewbo19/use-eo-learn-with-aws-sagemaker-9420856aafb5) (by Drew Bollinger)

- [Spatio-Temporal Deep Learning: An Application to Land Cover Classification](https://www.researchgate.net/publication/333262625_Spatio-Temporal_Deep_Learning_An_Application_to_Land_Cover_Classification) (by Anze Zupanc)

- [Tree Cover Prediction with Deep Learning](https://medium.com/dataseries/tree-cover-prediction-with-deep-learning-afeb0b663966) (by Daniel Moraite)

- [NoRSC19 Workshop on eo-learn](https://github.com/sentinel-hub/norsc19-eo-learn-workshop)

- [Tracking a rapidly changing planet](https://medium.com/@developmentseed/tracking-a-rapidly-changing-planet-bc02efe3545d) (by Development Seed)

- [Land Cover Monitoring System](https://medium.com/sentinel-hub/land-cover-monitoring-system-84406e3019ae) (by Jovan Visnjic and Matej Aleksandrov)

- [eo-learn Webinar](https://www.youtube.com/watch?v=Rv-yK7Vbk4o) (by Anze Zupanc)

- [Cloud Masks at Your Service](https://medium.com/sentinel-hub/cloud-masks-at-your-service-6e5b2cb2ce8a)

- [ML examples for Common Agriculture Policy](https://medium.com/sentinel-hub/area-monitoring-concept-effc2c262583)

  - [High-Level Concept](https://medium.com/sentinel-hub/area-monitoring-concept-effc2c262583)

  - [Data Handling](https://medium.com/sentinel-hub/area-monitoring-data-handling-c255b215364f)

  - [Outlier detection](https://medium.com/sentinel-hub/area-monitoring-observation-outlier-detection-34f86b7cc63)

  - [Identifying built-up areas](https://medium.com/sentinel-hub/area-monitoring-how-to-train-a-binary-classifier-for-built-up-areas-7f2d7114ed1c)

  - [Similarity Score](https://medium.com/sentinel-hub/area-monitoring-similarity-score-72e5cbfb33b6)

  - [Bare Soil Marker](https://medium.com/sentinel-hub/area-monitoring-bare-soil-marker-608bc95712ae)

  - [Mowing Marker](https://medium.com/sentinel-hub/area-monitoring-mowing-marker-e99cff0c2d08)

  - [Pixel-level Mowing Marker](https://medium.com/sentinel-hub/area-monitoring-pixel-level-mowing-marker-968402a8579b)

  - [Crop Type Marker](https://medium.com/sentinel-hub/area-monitoring-crop-type-marker-1e70f672bf44)

  - [Homogeneity Marker](https://medium.com/sentinel-hub/area-monitoring-homogeneity-marker-742047b834dc)

  - [Parcel Boundary Detection](https://medium.com/sentinel-hub/parcel-boundary-detection-for-cap-2a316a77d2f6)

  - Land Cover Classification (still to come)

  - Minimum Agriculture Activity (still to come)

  - [Combining the Markers into Decisions](https://medium.com/sentinel-hub/area-monitoring-combining-markers-into-decisions-d74f70fe7721)

  - [The Challenge of Small Parcels](https://medium.com/sentinel-hub/area-monitoring-the-challenge-of-small-parcels-96121e169e5b)

  - [Traffic Light System](https://medium.com/sentinel-hub/area-monitoring-traffic-light-system-4a1348481c40)

  - [Expert Judgement Application](https://medium.com/sentinel-hub/expert-judgement-application-67a07f2feac4)

- [Scale-up your eo-learn workflow using Batch Processing API](https://medium.com/sentinel-hub/scale-up-your-eo-learn-workflow-using-batch-processing-api-d183b70ea237) (by Maxim Lamare)



## Questions and Issues



Feel free to ask questions about the package and its use cases at [Sentinel Hub forum](https://forum.sentinel-hub.com/) or raise an issue on [GitHub](https://github.com/sentinel-hub/eo-learn/issues).



You are welcome to send your feedback to the package authors, EO Research team, through any of [Sentinel Hub communication channel](https://sentinel-hub.com/develop/communication-channels).



## License



See [LICENSE](https://github.com/sentinel-hub/eo-learn/blob/master/LICENSE).



## Acknowledgements



This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 776115, No. 101004112, No. 101059548 and No. 101086461.