Credits allow users to order high and very high resolution images from the data platform (catalog interface and API) or apply algorithms using the wide variety of processing blocks available on the processing platform. Users can extract useful insights from geospatial datasets with algorithms such as car detection, vegetation indices, flood mapping, building detection, deforestation mapping and many more.
In the example below, you will compute a flood map derived from three satellite images. The advantage of using these images is the very high-spatial resolution (0.5 m) and daily revisit rate.
The first step is to open a project, in order to store your workflow and the jobs that will be run in order to download the data and algorithm outputs.
The second step is to create a workflow and add the data and processing blocks necessary to perform your geospatial analysis.
After adding the blocks, click Save & Configure Job.
You are redirected to the job configuration window, where you need to draw/upload an AOI, select the geometric filter, adjust the job parameters (JSON key-value pairs) and select the job type (test query or live job).
Before running the job, you can view the estimation of the credit costs and make sure that you have a sufficient amount. Adjust the size of your AOI if needed. This job will consume 9516-9992 credits, which is the equivalent of 95-99 Euro/US dollars.
To download the data and algorithm outputs, select the job type Live Job, scroll down and click on Run Job.
A new window appears and informs you about the minimum amount of credits that will be held from your account balance until the completion of the job run. If you agree, click on Confirm & Run Job.
You will be redirected to the job dashboard, where the job is currently running.
The outputs are successfully generated if the job status changes from Running to Successful.
After successfully running the job, you can download and visualize the outputs in a third-party GIS software.
The outputs show a flood map overlaid on a pansharpened satellite image: