Face to Face course on Cloud Computing and Algorithms for EO Analyses

Face to Face course on Cloud Computing and Algorithms for EO Analyses
20 - 24 Jun 2022 09:30 - 16:30

Face to Face course on Cloud Computing and Algorithms for EO Analyses

The third F2F course of the EO AFRICA R&D Facility will take place in Rabat in close cooperation with CRASTE-LF. African Regional Centre for Space Science and Technology in French Language.

This course introduces participants to Cloud Computing and its usage for Earth Observation (EO) data analyses. It starts with big geospatial data concepts and extends to Cloud Computing as one of the solutions for solving the problems of big EO data. The EO AFRICA Facility Innovation Lab will be introduced as an example of a cloud computing platform for working with EO data. We will cover Jupyter Notebooks and JupyterLab as the proper solution for developing analytical procedures accompanied with documentation on cloud computing platforms. In the next step, the course focuses on some of the Python libraries to develop programs that handle and analyze EO data. We will explain how participants can programmatically access different EO datasets using online catalogue services and DIAS platforms on the Innovation Lab and utilize the data in their algorithms.


Who can participate?

Space is limited to max. 25 Participants. Participants will be selected on the basis of their academic background, work experience and motivation to participate. If you are selected you will receive a confirmation e-mail with further information by June 4.

Participants should reside in one of the African countries, should have an academic background related to Geoinformation/Earth Observation Science in combination with knowledge on Water Resources Management, Irrigation, Agriculture or similar, Young researchers in this field are encouraged to apply!

Attendance to the training course is free of charge.

Event Themes

Cloud Computing.
Innovation Lab (the cloud computing platform provided by the EO AFRICA R&D Facility).
Python Libraries for processing EO data.
Processing procedures as interactive Jupyter Notebooks on the Innovation Lab.
Load and Process EO data from DIAS platforms using Python.