A significant development in geospatial intelligence will be the presence of small black boxes in satellites. These are capable of taking in large quantities of data in space and simultaneously analyzing it. Now, there will be no need to download the data anymore.

Manipulation of geospatial data and analyzing it in real is one of the landmarks in the field of military intelligence. According to Melanie Stricklan, Chief Technology Officer and co-founder of Slingshot Aerospace, California, the development can help commanders to acquire better vision when faced with a bulk of data in scenarios when quick decisions have to be taken.

Stricklan worked with U.S. Air Force for 21 years and flew on the back of JSTARS radar surveillance plane. The plane’s sensors were taking in lots of data; however, it was challenging to draw intelligence from it. At times, that data would be forwarded to analysts, and it took weeks to bring some fruitful conclusion out of it.

Later, Stricklan was associated with the Air Force Space and Missile Systems Center, specializing in satellite programs. It was clear to her and others in the industry that figuring out how to utilize technology to make sense of the data collected by space-crafts and airplanes would be instrumental in gaining an edge over other organizations.

Slingshot Aerospace has come up with a cloud-based platform which will pull data from different sensors and make use of machine learning algorithms to make sense of the information. The aim is to have AI onboard the satellite, implemented on a small chip.

The technology is in its premature stage. The organization is in the process of providing cloud-based platforms with more data, to improve algorithms. This platform will then be embedded in a chip which will further be embedded in the satellites, according to Stricklan. If things go to plan, the analysis of data will take onboard the satellite itself.

“Data fusion” has been a focal point of interest for the military since decades. The significance of this phrase is on the rise as the remote sensing industry has the intention of deploying clusters of satellites which will collect different forms of signals and data from high-resolution imagery, to radio-frequency, radar and hyperspectral pictures. AI and machine learning need to be incorporated to analyze that amount of data to bring it to a form where it is relevant in decision-making as per a warfighter’s needs.