Earth observation processing framework for machine learning in Python
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.
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.
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.
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 (
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.
The package requires Python version >=3.8.
eo-learn on Linux it is recommended to install the following system libraries:
sudo apt-get install gcc libgdal-dev graphviz proj-bin libproj-dev libspatialindex-dev
Mac OS it is recommended to install the following system libraries with Homebrew:
brew install graphviz gcc gdal cmake spatialindex proj
eo-learn on Windows it is recommended to install the following packages from Unofficial Windows wheels repository:
gdal rasterio shapely fiona
eo-learn is available on PyPI and can be installed with:
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.:
pip install "eo-learn[EXTRA]" pip install "eo-learn[VISUALIZATION]"
The full list (including their descriptions) is available here:
RAYfor installing ray and its dependencies
ZARRfor installing the zarr functionality for chunked timestamp saving/loading
EXTRAfor installing interpolation- and clustering-specific dependencies, or for installing
s2cloudlessin cloud masking
VISUALIZATIONfor using plotting libraries and utilities
FULLfor installing all dependencies described so far
DOCSfor developers, dependencies for building documentation
DEVfor 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),
be installed using
conda-forge as follows:
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. It provides a full installation of
eo-learn together with a Jupyter notebook environment. You can pull and run it with:
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:
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:
docker build -f docker/eolearn.dockerfile . --tag=sentinelhub/eolearn:latest docker build -f docker/eolearn-examples.dockerfile . --tag=sentinelhub/eolearn:latest-examples
For more information on the package content, visit readthedocs.
Examples and introductions to the package can be found here. A larger collection of examples is available at the
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
Blog posts and papers
- Introducing eo-learn (by Devis Peressutti)
- Land Cover Classification with eo-learn: Part 1 - Mastering Satellite Image Data in an Open-Source Python Environment (by Matic Lubej)
- Land Cover Classification with eo-learn: Part 2 - Going from Data to Predictions in the Comfort of Your Laptop (by Matic Lubej)
- Land Cover Classification with eo-learn: Part 3 - Pushing Beyond the Point of “Good Enough” (by Matic Lubej)
- Innovations in satellite measurements for development
- Use eo-learn with AWS SageMaker (by Drew Bollinger)
- Spatio-Temporal Deep Learning: An Application to Land Cover Classification (by Anze Zupanc)
- Tree Cover Prediction with Deep Learning (by Daniel Moraite)
- NoRSC19 Workshop on eo-learn
- Tracking a rapidly changing planet (by Development Seed)
- Land Cover Monitoring System (by Jovan Visnjic and Matej Aleksandrov)
- eo-learn Webinar (by Anze Zupanc)
- Cloud Masks at Your Service
- ML examples for Common Agriculture Policy
- High-Level Concept
- Data Handling
- Outlier detection
- Identifying built-up areas
- Similarity Score
- Bare Soil Marker
- Mowing Marker
- Pixel-level Mowing Marker
- Crop Type Marker
- Homogeneity Marker
- Parcel Boundary Detection
- Land Cover Classification (still to come)
- Minimum Agriculture Activity (still to come)
- Combining the Markers into Decisions
- The Challenge of Small Parcels
- Traffic Light System
- Expert Judgement Application
- Scale-up your eo-learn workflow using Batch Processing API (by Maxim Lamare)
Questions and Issues
You are welcome to send your feedback to the package authors, EO Research team, through any of Sentinel Hub communication channel.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 776115 and No. 101004112.
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