Author: Philip Godfrey
What is Earth Observation data?
Earth Observation (EO) data, as defined by the EU Science Hub as data that is “used to monitor and assess the status of, and changes in, the natural and manmade environment”
With human civilization having an increasingly powerful influence on the Earth system, now seemed like the perfect time to explore what can be done with EO data in Oracle Cloud.
How is Earth Observation data captured?
The process of gathering observations of the Earth's surface and atmosphere via remote sensing instruments. The data is typically in the form of digital imagery.
There are many ways to gather this type of information, through various remote sensing platforms. Instantly with Earth Observation we think of space, but this isn’t the case. It can be through Drone / Aerial or Satellites.
Using Earth Observation data in Oracle Cloud
In this blog series, we will explore all around the Oracle world in terms of technology and will utilise a number of Oracle platforms:
The first blog focused on loading the data into the ADW, and creating a machine learning model in Oracle Data Science, if you missed it you can read it here.
The second blog focused on understanding model performance and how you can improve machine learning model performance, if you missed it you can read it here.
The third blog of this series focus on applying the model against unseen data, storing results back in the ADW.
The fourth and final blog of the series will make the output available to business end-users, through Oracle Analytics Cloud.
• Our journey begins in the Autonomous Data Warehouse (ADW) - to store the data
• We then move onto Oracle Data Science – to explore the data and utilise Machine Learning with our Earth Observation data
• We come back into ADW – to store the results back to the ADW database
• To enable the business to see the results we can present them in Oracle Analytics Cloud (OAC)
Output in Oracle Analytics Cloud
As an endpoint, we want to return the result to an end-user via Oracle Analytics Cloud. We will create a canvas in Oracle Analytics Cloud for an end-user to work with.
Note: To display the image, we must use an Oracle Analytics plug-in (Base64image) which allows us to see the image on screen.
This canvas will allow the user to select a field of interest (such as "River") and they can then adjust the confidence score via a slider (e.g., return those images with only 80% confidence or more) and the user will be returned with a set of images with predictions meeting that criteria.
This is just one example of how we can start exploring Earth Observation data in Oracle ADW -> Data Science -> Oracle Analytics Cloud, but this same approach can be applied to any kind of business area, while also ensuring we are answering a business problem and providing a service for an end-user.
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