The talk I moderated was on “Big (Geospatial) Data and AI”, which aimed to explore how the two rapidly developing worlds of satellite data collection and artificial intelligence interact. Talks. I was joined by Nathan Kunditz, formerly of satellite antenna company Kymeta and now leading a synthetic data startup called Rendered, and Rachael Martin, Maven Office director at the National Geospatial-Intelligence Agency. It's rare to hear from someone like Martin, who is inside the intelligence community and has a front-row seat at the intersection of classified information and advanced technology.
Martin leads the Defense Department's flagship AI program, Project Maven, from within NGA, effectively a sister agency to the National Reconnaissance Office. Simply put, Project Maven at NGA is working on how AI can use satellite images and data to detect objects and activities around the world.
Or, in Martin's words, the NRO will “launch them and we'll tell them where to go.”
Introducing AI into the realm of satellite data is a necessity, Martin emphasized, because “we have billions of georeferenced data points,” so “how do we make sense of them in a way that can provide value?”
“We're in a position where you have satellites everywhere but there are so many of them that you still don't know what's going on everywhere, and you can't possibly see all that data and Can't understand it helpfully.” Martin said.
“As the volume of this data grows, it's beyond the capacity of the human brain to derive any kind of useful understanding from this kind of data,” Martin said. Also, “there are many different types of geospatial data and you don't necessarily need to use the same type of AI technique to get value from each of them”, he added.
One of the big changes Martin has seen in recent years is that more and more companies in the geographic realm “want to be a part of helping us develop solutions to some of our challenges.”
“From a national security perspective, our adversaries are not interested in putting things of interest where we can find them. And so in many cases, we have to use [artificial intelligence] to help us visualize what they look like in other scenarios that would be of interest to us,” Martin said.
And more change is coming: The next step in the evolution of geospatial and AI, he believes, is applying creative AI to “primarily non-experts equipped with the ability to skillfully use geospatial data.” to do.”
“What we can do with some of the generative AI tools that are coming out is to create the ability for a non-expert to query complex geographic data and get an answer much faster than if They would have just outsourced it to a data scientist,” Martin said.