Sharing data across workflows

Modified on Tue, 04 Jul 2023 at 09:14 AM

In this article, we look at cases when you might want to use the result of one workflow and use it in another.  To do this we will use the Save Data for Re-use block and the Re-use This Saved Dataset block:



Sharing Data Across Workflows

TABLE OF CONTENTS



Step 1: Save the data in a workflow

  • To save the outputs from a workflow, simply include a Save Data for Re-Use block. 
  • This will save all the bands that are available and assign a name to this dataset.
  • Remember to give your saved data an easy-to-recognise name.
  • It doesn't matter how many workflows you have, you can save all of them, but do give them different names. 


Step 2: Input the saved data into a different workflow

  • To input the data, include a Re-use This Saved Dataset block at the beginning of the workflow.
  • The Re-use This Saved Dataset block will offer a dropdown menu showing all the available datasets.  Choose one.
  • You can include more than one saved dataset in a workflow, but you will need a new Re-use This Saved Dataset block for each one.


Note: The table block can be used as a freestanding block.  When you do this, all of the datasets will appear as options, even though you haven't saved them. 



An example where you might want to share data across workflows

One of the main reasons why you would want to save datasets for use in other workflows is for masking out one dataset using properties from another.  The workflows show an example where a landcover dataset is being used to mask out all non-forest areas in a forest biomass dataset.  This way, the final biomass map is only showing areas known to be forests.

Another example where you will want to share data across workflows is for classification (either supervised or unsupervised). You can create individual workflows to generate your input datasets for the classification, and then bring these into your classification workflow. You can explore these examples in the Template Workflows under the Classification section. You can also learn more about classification in this article here



Note: You can also use this method to conduct calculations across multiple datasets.





 

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