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eo-learn-examples

eo-learn makes extraction of valuable information from satellite imagery easy. This is where we show you how.

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 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.

The eo-learn-examples repository contains example Earth observation workflows that extract valuable information from satellite imagery, giving you hints and ideas how to use the EO data.

Sentinel-Hub account

In order to run (some of) the examples you need a Sentinel Hub account. You can get a trial version here.

Once you have the account set up, login to Sentinel Hub Configurator. By default you will already have the default confoguration with an instance ID (alpha-numeric code of length 36). For these examples it is recommended that you create a new configuration ("Add new configuration") and set the configuration to be based on Python scripts template. Such configuration will already contain all layers used in these examples. Otherwise you will have to define the layers for your configuration yourself.

After you have decided which configuration to use, you have two options You can either put configuration's instance ID into sentinelhub package's configuration file following the configuration instructions or you can write it down in the example notebooks.

Overview

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Installation

Generally, examples should run with having latest eo-learn installed. In other cases, the example should come with instructions how to set-up environment in order to be able to run it.

Contributions

We are very curious to see how you use eo-learn. If you would like to contribute to eo-learn-examples, please check out our contribution guidelines.

Blog posts and papers

Questions and Issues

Feel free to ask questions about the package and its use cases at Sentinel Hub forum or raise an issue on GitHub.

You are welcome to send your feedback to the package authors, EO Research team, through any of Sentinel Hub communication channel.

License

See LICENSE.

Acknowledgements

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