Alec Wilson (00:00):
My name's Alec. I'm the Chief operating Officer at Spexi, and I'm gonna take you through how to fly a drone on the network. So first things first is you want a compatible drone and device. So we work with the DGI mini series, specifically the DGI, mini two, mini three, and mini four series currently, which is the vast majority of drones that exist in the world today. So you need one of those drones and then you need an Android device. We don't currently work with iOS devices because of a limitation on the drone hardware side, but luckily there's lots of them out there. So first thing first is you power on the drone and the controller. And if you come with me, come with me over here, it's okay, you can walk over this. The next step is to power on the app and connect it to your controller.
Alec Wilson (00:41):
So at this point, the drone is gathering itself. It's trying to find some GPS satellites to make sure that it can locate itself, which is totally normal. And then we're gonna open up our mobile app and get going. One of the really nice things about this network is you don't need to install anything. You don't need to do any hardware installations. The drone out of the box comes ready to fly, and all you need to do is download our mobile application and then away you go. There's a few setup steps within the app itself, but they're very quick and so that we make sure you can get paid when you fly and things like that. So then all we do is we look at a map of available hexes and we click on one close to us and then we go over and we click set up, and then it's going to open up the hexagon that you need to fly and you can visually see where it is.
Alec Wilson (01:29):
And then from there it's as simple as hitting the fly button and we're gonna step back a little bit and then the drone takes off and away we go. And so now the drone, as you can see, is gonna fly up to its flight plan altitude of about 80 meters above ground. It's then gonna follow train at that altitude to keep itself there for image quality. And as you can see, I'm not doing any of the flying, the software's doing all the flying. So we're gonna look up monitor the airspace for safety. We're looking around monitoring the airspace for safety. We fly to certain altitudes such that, um, it's very unlikely that there would be any incursion with manned aircraft. Flight flight patterns. They fly over 500 feet above ground. We keep the drones under 300 feet above ground at all times. Um, I actually used to fly helicopters in this airspace all day long.
Alec Wilson (02:18):
This was my backyard, thousands of hours flying in the Vancouver area. And so I'm quite sensitive to the safety risk that drones can cause. And we've designed the system to be incredibly safe. And so now you can see the drone is actually centering itself over its pre-programmed station and it's gonna start taking a series of images. This happens throughout the hexagon in, in a completely standardized way. It takes about seven minutes and then you as the pilot go to the next hex, the next hex, you change the batteries next, next, next. And so you can do this all day and then you get rewarded for each successful flight that you do. Can
Jennifer Strong (02:49):
You describe why a hex and, and
Alec Wilson (02:52):
Things like that? So this is actually, most drone pilots won't even fully understand this either. So we gotta start from beginning. What we've developed here is a really novel system to capture imagery over large spaces. The reason we've used the hexagon system is 'cause we can divide the earth into equal parts using hexagons, and therefore we can make the capture process the same everywhere, everywhere on earth. This is the same process that you would follow. That's really important because customers want the same imagery everywhere. And so if you make the input process the same and you use the same methodologies and input sensors like we do with these drones, then you generate really, really good data across the entire earth's surface. And so the flight plans are already set up for you. So simply all you have to do is get close to one of these hexes, open up the mobile application, and then make sure it's safe to fly and there's really not much else to it. So as you can see, the drone is, well, we'll have to find it. Oh, there it is. You can see it just over there. It almost looks like a bird, but it's not flying like a bird. It's flying very straight. So it just centered right there, just right above these cranes. If you look up, up, up, up, it's right in that cloud there. You see it. It's just a tiny little speck. Yeah. Oh,
Jennifer Strong (03:57):
Oh, now I see it.
Alec Wilson (03:58):
Wow. It's, so that's it. It's just a little drone. And um, that's one of the reasons this this also scales around the world is because the drones are so small and so light, they introduce a very little amount of risk into the airspace, but also you can't hear them where you can't see them. And so for most folks around the world, they just don't realize that there's drones flying around, which is the same with satellites and planes. There's satellites circling the Earth 24 7 imaging us, but we're not, we're not worried about it because we don't see them or hear them. And it's the similar situation. So there, it's moving, it's moving to its next point. Now again, see fully autonomously, I'm not doing anything. The software's controlling the flight. The imagery is privacy protected. The imagery's not high enough resolution to, um, pick up faces or license plates or, or specific details, but it's good enough that it unlocks an entirely new use case use cases for customers because it's much, much better than the next best imagery source. So this is kind of this new, it's this new model that we've unlocked using software, um, using all of our experience, flying helicopters, flying planes, and putting it all together into software and product so that we can then scale this product.
Jennifer Strong (05:01):
How do you deal with different restrictions in different airspace? Licensing or do deal with different requirements for every city?
Alec Wilson (05:07):
We do not, not city, but drone regulations are country specific for the most part in the United States. State by state can get a little bit tricky. But another one of the, the magic, uh, the magic pieces of this network is that the pilots, the local pilots are doing the flying and drones are not a new technology. They've been around for a while. There's existing regulations in place for pilots to follow. So simply they're required to make sure that they have the right things in place, whether that be a pilot's license or some insurance requirement or what have you, so that they can fly safely and legally in their hometown or their home country. In Canada, you don't actually need a license to fly these small drones because of their weight in the us you do. And in Europe, in some countries in Europe you don't.
Alec Wilson (05:46):
In some countries you do. And so it varies. But ultimately drones are being flown in these countries all day every day by lots of different people for different use cases. This is just another one of those use cases. So when you're done simply all you have to do, um, is let the drone finish and then we can hit the return to home button and you can hear it beeping at us. And that means that the drone is gonna fly itself home. So again, not doing any flying worst case scenario, we could take manual control of this drone completely and bring it back down to whatever altitude I wanted or fly it. However I want it as a manual flyer, but I've got it programmed, um, based on my safety settings, which all pilots will have slightly different ones depending on their operating environment to just come straight home at a hundred meters and to descend. So if you look straight up, you can see it's directly over top of us and it's, I know it's hard with the sunglasses, but then now it's just gonna start descending down directly over top of us. And that's that.
Speaker 3 (06:57):
All
Alec Wilson (06:57):
Right. There you go. Cool. Cool. Yeah. Okay. That's it. This is fun. Yeah.
Bill Lakeland (07:02):
Hi, I am Bill Lakeland. I'm the CEO at Specy. So Specy is a decentralized network of drone pilots collecting imagery at scale around the world. It's very automated, very simple, and it's basically collecting a new image layer of the planet at a higher resolution than what satellites and aircraft are collecting at and at a higher frequency. And because we're collecting data like that, there's a whole bunch of new use cases that this unlocks, uh, especially in the spatial ai, metaverse kind of systems and platforms unlocks these exciting new use cases because of the potential of the data and the fidelity of it.
Bill Lakeland (07:50):
I come from a long line of flying aerial mapping over the last 20 years and have sort of reinvented the way that that's happening with the drone platform for decentralization of drone pilots and their hardware to collect imagery at scale globally to create a new image layer. So the company is founded in Vancouver, bc We're here because that's where our team is. That's where, you know, I've been aerial mapping based out of BC for over 20 years now. We've been flying all over North America for that period of time. A lot of what you've seen in Google Earth would've come from one of my airplanes over the years. So yeah, we started building the team here. Now we're growing across Canada and into the US as we scale. Um, we're recent series a, uh, companies, so that closed last December. And so we're scaling up and so our teams in, in different areas and, and the pilot network is, is all over. So the pilots themselves are up to like 4,000 contributors across North America right now with over double that on the wait list. And so that team is spreading, uh, very quickly.
Bill Lakeland (08:56):
Traditionally, cities and governments would buy the data. They do that with aircraft and satellite data. They've been doing that for years. But because we're making higher resolution data, decentralizing the capture, there's, you know, it's higher frequency and it's lower cost because of the decentralized nature, meaning we're using passionate pilots that just want to go out and fly. We give them a really simple and fun platform to fly and earn. And so it makes it very easy for us to collect data all over the world at a much lower cost. And so that opens up opportunity to new buyers as well as the data being higher in, uh, quality, like the fidelity of it. You can see more, you can actually see the cracks in the sidewalks, you can see what type of roof shingles are on the roofs. You can see all these things that you couldn't see before. It opens up new use cases. And because AI and spatial computing is advancing at the pace that it's at, it needs really good data to be trained on. It needs better. The better quality of the data is trained, the better quality, the outputs that it provides, right? So it's sort of unlocking the use cases for us in so many different dimensions and so many different enterprises.
Bill Lakeland (10:09):
For one, you need to understand the space. It's fairly niche. There's not a lot of companies that do it. Even back when I first got into this, there's one company in the province we're in that, that flew this type of imagery. And so really understanding that ecosystem, really understanding what good quality and consistent products are that companies can reliably build on top of that as a reliable data set to, you know, start verticalizing into whatever it's they're doing. Whether that's insurance, whether that's utilities, whether that's city operations for smart cities, whether that's surveying companies or construction. It's just goes on and on. But they need a really consistent data layer. And so then it's like, Hey, we're burning a lot of carbon 'cause they're flying airplanes around. They can't scale. You can buy another plane, you can buy another sensor and go farther, but that's not really scale.
Bill Lakeland (10:52):
How do you actually scale this kind of knowledge and get this in the hands of more people and and make the world a more efficient place? And so that was a problem we set out to solve. And I think that sort of the technology changes over the past five years have really unlocked this. Drones are now mic, they're miniaturized. So micro drones are what we use, so below 250 gram drones. So they're all over the world. We can access 'em everywhere. Social media is the way that we access the pilots 'cause we're all there and they're available and they're passionate and they're like, Hey, I, I have a drone now what I, what can I do? We give them some purpose and, and cash to be able to go out and fly and it's very simple and fun. So that network is there, there's blockchain elements to this project as well. And because we're using decentralized hardware and pilots, we need to be able to make sure the data can't be tampered with. And so blockchain is actually a technology we can put into the system to actually authenticate that data and make sure the data is, is what it's supposed to be and can't be tampered with, with, as well as creating ownership economies down the road as decentralized networks can do if we choose to go that route.
Bill Lakeland (12:13):
So our goal is essentially to create the most accurate data layer of the planet that's very referenced so that any system can put all their data on top of it to align it with the earth. Because you're, if you talk about using contractors in cities like an airplane contractor in every city, you've got different data providers, you've got different like alignment issues and things. You know, the good data and good data problem starts to become a real problem when you're building like large geospatial models, kinda like an LLM, but it's just like a geospatially charged model where computers, you know, humans and computers can interact with a digital world to query it. Um, and when you want to create a very consistent layer across the planet, you need a very reliable base layer of information to build on top of. And that's one of the main kind of quota markets that we have right now.
Bill Lakeland (13:11):
We're in almost 200 cities now in North America, so it's scaling very, very quickly. We're starting to work in the UK and some stuff in Mexico, so we can kind of, we can do this here, we can do this anywhere. And the US is actually a harder country to actually do this in, believe it or not. And, and we being able to do that as well, very well. So this is global competitors. It's interesting because there's no drone platforms that are doing what we're doing. There's pilot networks. So if you owned a windmill and you want an inspection of a windmill, you could contact a company, they would assign a pilot, they would do a flight of your windmill, you'll probably get data that maybe you're not totally happy with or you have to sort of handhold through the process to really get what your company needs.
Bill Lakeland (13:49):
We don't do that. We're, we're literally an image layer. We're like a data layer of the planet, just like how Google Earth is at just a much higher resolution. There's no other drone platforms that are doing that. The only other companies that do that are satellites that are imaging the earth and airplanes that are imaging the earth at a bit of a, you know, less of a cadence than satellites. And so we can do, we're doing the same thing as what that is, right? And it's a very sort of generic layer of data that can be used for thousands upon thousands of applications that a AI is really amplifying right now for us.
Bill Lakeland (14:33):
Now we're seeing demand where we've got product market fit with the pilots, the demand side of this is starting to take off. And now it's, we're really coming down to like scaling internationally. And from everything we've seen, there's no faster way to scale supply than actually having a decentralized pilot network, um, supplier network or us being with pilots. So we are looking at adopting a token into the system. There's been a foundation recently launched called Layered Drone, which is the place where pilots will actually live. It'll be the landing spot for them, and that'll be where the protocol is, the set of rules and the technology stack for the automation of the collection and storage. And of course, PXI will be the first adopter of the data coming through and, and tuning that data specifically for lgm and ai, spatial computing and smart city operations, and all these different use cases that really need the data to streamline their operations and, and add value to the business.
Speaker 5 (18:28):
Hey, hey. All right.