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 is divided into several subpackages according to different functionalities and external package dependencies. Therefore it is not necessary for user to install entire package but only the parts that he needs.
At the moment there are the following subpackages:
eo-learn-core- The main subpackage which implements basic building blocks (
EOWorkflow) and commonly used functionalities.
eo-learn-coregistration- The subpackage that deals with image co-registraion.
eo-learn-features- A collection of utilities for extracting data properties and feature manipulation.
eo-learn-geometry- Geometry subpackage used for geometric transformation and conversion between vector and raster data.
eo-learn-io- Input/output subpackage that deals with obtaining data from Sentinel Hub services or saving and loading data locally.
eo-learn-mask- The subpackage used for masking of data and calculation of cloud masks.
eo-learn-ml-tools- Various tools that can be used before or after the machine learning process.
The package requires Python version >=3.5 . It can be installed with:
pip install eo-learn
In order to avoid heavy package dependencies it is possible to install each subpackage separately:
pip install eo-learn-core pip install eo-learn-coregistration pip install eo-learn-features pip install eo-learn-geometry pip install eo-learn-io pip install eo-learn-mask pip install eo-learn-ml-tools
eo-learn on Windows it is recommended to install the following packages from Unofficial Windows wheels repository:
gdal rasterio shapely fiona
One of dependecies of
eo-learn-mask subpackage is
lightgbm package. If having problems during installation please check LightGBM installation guide.
For more information on the package content, visit readthedocs.
If you would like to contribute to
eo-learn, check out our contribution guidelines.
- 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)
Questions and Issues
You are welcome to send your feedback to the package authors, EO Research team, through any of Sentinel Hub communication channel.