So, what’s the process of setting up a machine learning workflow?

In simple terms: you firstly need to understand what your problem is.

After that, you need to choose appropriate data and the correct learning algorithm.

Then comes the most crucial part of machine learning: preparing your training data set.

In EO, data set generation can be performed by exploitation of different resources which should be added to the data collected by the satellites. Among the most important ones we have the ground truth data collections, often organized and distributed through networks managed by scientific organisations. Moreover radiative transfer models can be considered. They yield synthetic physical data, simulating real observations of the process under investigation

Once that’s done you can train your model. It is very important to evaluate the performance of the model – you do this by testing it on a selection of the training data that you held back from the ML, and didn’t use before starting the learning process.

Then – if everything has gone to plan, your problem is solved! But if necessary, you can re-run your model with optimised parameters.

Throughout this course, you will have the opportunity to look at and interact with ML workflows.

Featured Educators:

• Julia Wagemann

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