Multidimensional Visualization of Data
from Space Missions.
A single datapoint may not tell the whole story.
It is important to give a datapoint its place in the larger picture.
In space layers of data can have interactions with each other.
When viewed in the context of space and time, new ideas may emerge.
Machine learning models make it easy to do this.
By mapping datapoints and putting them over the surface that has been observed,
data comes to life for researchers in an intuitive way.
New patterns emerge between layers that would otherwise go unnoticed.
AerospaceAI has a built-in data explorer.
Instead of having to import new data,
AerospaceAI has a vast library of already available data
that has been mapped and using its Machine Learning model.
Use Cases
View space data in 3D.
Images and data are usually observed in a 2D format.
Machine learning is able to create a 3D image from an initial 2D set, allowing the data to be viewed from multiple angles.
Shift between different types of data.
Different layers of data may tell a different story.
Easy filters allow researchers to shift through the layers of the same observation taken by different instruments.
See how data has changed over time.
Drawing on a vast library of available data,
Researchers get an overview of the relevant missions that have collected data on their topic, allowing to scroll through the observations through time.