eo-grow-examples

Earth Observation framework for scaled-up processing eo-grow in action.

Analyzing Earth Observation (EO) data is complex and solutions often require custom tailored algorithms. In the EO domain most problems come with an additional challenge: How do we apply the solution on a larger scale?

Examples

The examples in this repository are fully self contained in corresponding sub-directories. Each example has instructions on how to set-up a working environment, and (typically) an end-to-end notebook that user can tweak according to their needs.

GlobalEarthMonitor examples include:
- Large scale processing demo using Ray on Copernicus Data Space Ecosystem (CDSE) or AWS
- example notebook for running small example on your laptop
- continuous monitoring example for details about ongoing monitoring of large areas.
GEM Workshop includes material used in Land Cover Continuous Monitoring Service (LC-CMS) GEM Workshop. This workshop example can also be followed on youtube. It shows:
- An overview and firsthand experience of a LC-CMS pipeline prototype, with results generated for the region of Paris.
- How eo-grow and eo-learn libraries could be used to build a pipeline for your requirements.
- The concept of Change Detection and its application in monitoring land cover evolution and change monitoring, with results generated by LC-CMS in demonstration area.

Installation and running of examples

To run the examples provided in this repository, the user has to clone the repository first, and then follow the instruction given in the README.md of each particular example.

git clone https://github.com/sentinel-hub/eo-grow-examples.git

Documentation

The documentation of each example should be given in the corresponding README.md files. Documentations for the main building blocks of the examples can be found here:

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.

License

See LICENSE.

Acknowledgements

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