Dan Smoot0:20
Maxar for decades was the global leader in regards to exquisite imagery. The last two years, we have been working to drive more an application-based solutioning, because the fact the world has become more about the consumption of information, with a lot more intelligence around it. With the new compute capabilities that are going on in the marketplace, we've been able to really look at information completely different. AI is a great tool, but if you don't have phenomenal foundational data, it's actually not a very good tool. When you build 20 years of an archive of the digital twin of the Earth and then you can actually apply models to it, you can actually do some pretty incredible things. You can do things like understanding site monitoring or change. In the past, you'd be looking at things that could be two to three year old maps. Now, we can actually update maps almost near-time. We're seeing rapid capabilities in regards to 3D. 3D used to be — if you've ever seen GeoInt 3D in the past, it kind of had this ice cream cone look to it. It's effective to show buildings and stuff like that. Now if you look at what we can do with the compute power that's available, we can actually do it in high definition, where you can actually see the corners of buildings, the tops of trees, the window panes — and all done from a geospatial perspective. Why that's powerful is scale. The only way you could do that in the past is you had to use aerial, but aerial is very narrow.
Vantor is now a spatial intelligence company. What's the story behind spatial intelligence? So if you think about things like what we're doing, we have a product called Sentry, and this is where the industry is trying to get to, which is applying true data intelligence around site. So you're seeing site and maritime, by the way. So if you think about things like you want to understand objects on the ground, movements of vehicles, movements of aircraft, movements of military weaponry — if you're in that side of the business, you need to see very accurate information. You need to be able to do object identification. So it's not just about having this foundational data. It's about having the capabilities to apply it to the foundational data to actually learn at a rapid pace. So you can actually see a rail car, you can actually see not just a ship, what kind of ship. How are you actually getting into this GPS-denied applications?
When you have the foundational data in 3D like we do, and you have the ability to rapidly update it and apply it to much more clarity level, you can actually do it in an autonomous way. You can navigate based on landscape. GPS is about 9 meters accurate. Landscape is about 3 meters accurate. And so what we've applied is the capability to take that exquisite 3D capability and put it either on the drones or central capability from an autonomous control. And when drones are flying in areas where they're GPS-denied, you can now actually fly them based on landscape navigation. If you think about last-mile drone delivery, that autonomous capability can be really important. And if you're going to be delivering via drone to a location, you're going to want accuracy.