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DRONE ON explores how drones are reshaping the world. Hosted by Bryce Bladon, the podcast documents the tech, economics and people piloting the world's largest standardized drone imagery network.
Mapping 3.0_Locate, Navigate, Understand
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Bryce Bladon: [00:00:00] Hello and welcome to Drone on the only podcast in the air and on the airwaves. I am your host, Bryce Bladon. And on today's episode, we consider the evolution of mapping and the role drones and other technologies will play as mapping 3.0 follows the same trajectory as Web3. I believe mapping, as we know it, is facing its first major upgrade in decades, if not thousands of years.
And similar to how a convergence of technologies upgraded the internet to so-called Web3, I believe something similar is upgrading the world map to mapping 3.0. So first, let's start with a term I used there that I think you should know in the context of this conversation. And that's Web3. It's a buzzword that's come up in the past decade from full disclosure.
A former investor in one of my businesses, uh, Chris Dixon. Chris Dixon, coined the term Web3 to describe it as the third evolution of the internet building on the foundations of so-called Web one and Web two. Web one would be the internet you are thinking of from the [00:01:00] 1990s. The static read only web read is an important verb there.
Websites are static. Users can consume content, but they can't easily create or interact with it. Yahoo Basic, HTML pages. Very simple information sharing, decentralized and structure, but limited in functionality. This brings us to web two from the two thousands to roughly this point in time. This is the Read right internet.
We've introduced another verb here, enabling user-generated content, often referred to as UGC, interactive platforms, social media, blogs, wikis. These were quickly dominated by centralized platforms like Facebook, Google, Amazon. Apple. Users can create content, but platforms own the data and control the experience.
And this is what most modern internet business models are built on advertising and data collection. This brings us to Web3. Which came into existence as a concept sometime around 20 17, 20 18, but has since [00:02:00] gained a lot of traction. Fun fact, my current job title uses the word Web3 in it, less Fun fact.
Very difficult to explain to the bank, but I digress. Web3 is the decentralized web, uh, web one static web two, social Web3 decentralized. The new verb here is own. It is the Read, write, own Internet where users can control their data and their digital assets. And it's built on blockchain technology and the distributed ownership and powers.
Things like users owning tokens, NFTs, and the associated governance rights these would enable in platforms is the basic conceit for why Web3 would be a evolutionary shift. Economic transactions would be built into the fabric of the web and it aims to combine the so-called web one decentralization with Web Two's interactivity while adding ownership.
What this represents is user agency, at least in theory, web one, lets you read web two, lets you read and write, and Web3 lets you read, write, and own. But again, Web3, largely theoretical, but [00:03:00] a very, very useful framework for considering how internet infrastructure has evolved over time and how a group of people can often engage with a common resource and advance it as a whole.
So, with that framework in mind, let me introduce you to the concept of mapping 3.0. Where this starts is with mapping 1.0. This basic conceit about the future of drones and mapping, it's been something I've thought about in a lot of different ways. I, one of my favorite books I've read recently is David Grand's The Wager, a large portion of which is dedicated to the challenges of circumnavigating the globe, specifically navigating udal and longitudal areas accurately in large swaths of open ocean.
That is mapping 1.0 you are thinking of when you think of big ships trying to navigate the ocean. In fact, anything from about the first map around 3000 b, c, e, or so to the 1990s is in my mind mapping 1.0. This establishes [00:04:00] where things are in space or more accurately where they were. 3000 BCE is the first known maps that I could think of.
Babylonian clay tablets. 150 CE would see Potoma Lee's, uh, geography established coordinate systems. 1884 had the prime meridian established. Uh, Sputnik landed and began the satellite area in 1957. GPS came to us in 1973, and in 1995, GPS became consumer usable. So, mapping 1.0 is fixed. It is latitude and longitude, an x and a y axis.
It is static. It is represented on physical media. It is survey-based accuracy through triangulation and celestial navigation. That is why a lot of sailors had a lot of trouble, uh, in the open ocean when perhaps they couldn't see the stars. And it uses universal reference poist to enable global standardization and should be noted, very limited accessibility maps were expensive and highly specialized.
Basically, if you are thinking of a map that has [00:05:00] a dagger, keeping it on a table, if you are thinking of a globe spinning, you are thinking of mapping 1.0. Basically the idea of seeing the world for what it was. But that brings us to mapping 2.0, which I think shows us what the world is. So 1.0 lets you locate, it's an x and y axis.
It's static mapping 2.0, lets you navigate. It's X, Y, and Z. That Z access being topographic, it's interactive. It's Google Maps, baby. The core function of mapping 2.0 is interactive wayfinding spatial accessibility. The first example of this I can think would be MapQuest got handed to the og, came out in 1996.
Google Maps didn't actually come out until 2005 with Ajax based interface, which is very relevant to this mapping conversation. The iPhone in 2007 introduced GPS to mobile mapping. Google Street View came out in 2008, turn by turn. Navigation became standard on [00:06:00] smartphones in 2009. Apple Maps launched in 2012, and Google acquired Waze in 2013 to show you the value of crowdsourced traffic.
What I'm getting at here is that the defining characteristics of mapping 2.0, dynamic realtime updates, it's no longer static. It's universally accessible through smartphones and internet. It's interactive. You can ban, you can zoom, you can select different layers. There are algorithms for helping you figure out your optimal path.
There's multimodal transportation. You can get a map for walking, for driving, for taking your bike, for taking transit, and it's also crowdsourced. So. Key examples here, Google Maps, Waze, OpenStreetMap, any GPS device, you use, any transit maps. These are all examples of mapping 2.0 the age that I think we are just passing through now.
And now. What I think is really interesting about this conceit I'm trying to sell you guys on is that whereas I believe Web3 is very interesting and conceit and we are approaching something similar to it, I think we are actually [00:07:00] in the age of mapping 3.0. I think we've been here since the 20 tenths. So the core function of mapping 3.0, whereas mapping 2.0 was interactive wayfinding and spatial accessibility mapping 3.0 brings us to, these are gonna be some pretty impressive sounding words, but they're a lot easier than they sound.
Temporal intelligence and predictive spatial insights just means time. In 2010, Google began using machine learning for traffic prediction, and in 2013, Uber demonstrated location based demand prediction. 2016 Pokemon Go showcased augmented reality mapping. 2017 Tesla's autopilot begins crowdsourcing real world driving data.
2019 Google Maps starts giving you eco-friendly routing options. In 2021, apple Maps has detailed 3D City experiences. In 2023 Chat, GPT began integrating with mapping applications. And in 2024, we are seeing realtime wildfire and climate impact mapping become standard. So what is different about this and what came before it?
First of all, [00:08:00] temporal. What that means is showing change over time. So by this I mean if mapping 1.0 was letting you locate something, mapping 2.0 lets you navigate something. Mapping 3.0 lets you understand something. It is temporal. It is predictive. It can forecast conditions, it can forecast demand, it can, uh, figure out optimal timing.
It is contextual. It can interpret what locations mean, not just where they are. It is multidimensional. You can look at environmental, social, economic, and temporal data layers in the conceit of mapping 3.0. Machine learning augmentation, augmented reality overlay. Very impressive sounding words, but what I'm getting at is semantically rich context for coordinates to really just break it down to its brass tacks.
Mapping 1.0 is what was mapping 2.0 is what is mapping 3.0 is in my mind what will be. This is computer vision interpreting [00:09:00] satellite and street imagery at the same time, it is predictive urban planning. It is personal spatial AI that understands your individual needs and preferences for navigating a city or other space.
It's real time ecosystem monitoring. It's metaverse integration, it's quantum enhanced positioning. If you really wanna throw down some $3 words. What I'm getting at here is we have a progression of complexity and ironically accessibility mapping 1.0, established the fundamental question, where is it?
Mapping 2.0 added the practical question. How do I get there? Mapping 3.0 introduces the analytical questions. Why is it there? What will happen next? How should I respond? The evolution mirrors technological trends from information storage to information access to information intelligence. Just as the internet progressed from read only to read, write, to perhaps read, write, own maps have already progressed from locate to navigate to understand.[00:10:00]
Now, one little caveat I brought up here that I think is really, really, really important to mention is that mapping 2.0 started to become mapping 3.0 around 2010. And that is when drone technology really started to find its footing. That's not to say it's a one-to-one connection, but a lot of what Web3 was promising was based on the convergence of internet technologies and blockchain technologies.
What I'm seeing with mapping 3.0 is the convergence of a lot of technologies, not just drones, not just computer vision, not just internet of things, not just metaverse. All these things, AI and LLMs need better data to navigate spatial awareness and things of this effect. What we are seeing here is an accelerated time between each one of these things.
Mapping 1.0 lasted millennia. Mapping 2.0 lasted about 20 years, and 3.0 is evolving rapidly. We're seeing it converge with ai, the Internet of things, social platforms, and what's really interesting is that it's making it a [00:11:00] less distinct domain and a more foundational layer for other applications. Put another way.
If I was to ask you what, I dunno, coding language your favorite app was written with, you might not be able to know. And I think mapping 3.0 follows a similar thing where it's being used, you're using it. But because it is so intuitive, because it is so foundational, it's not as distinct or defined in someone's mind.
I did not realize how far mapping had come until I was basically challenged to understand it. We've also gone from scarcity to abundance. Here. Maps are no longer rare. They are no longer expensive, and they are no longer limited to people with a very specific set of education and expertise. Now we have unlimited access to real-time global mapping intelligence, and it's in your pocket.
I mean, this raises the question of what mapping 4.0 will look like, but I think we're maybe letting ourselves get a little past our skis on that one. In the context of this conversation I'm trying to have right now, I've come to learn that drones mapping and spatial imagery is a [00:12:00] lot more foundational, a lot more globally universal, and a lot more personally applicable than I ever really appreciated.
I also, for lack of a better way, putting it, I'm just so astounded that nobody else is kind of banging this drum. I don't ever wanna be a cheerleader for a technology or a idea of what might come. I think it's formless and not the way to approach work or expertise. But at the same time, let's learn from history.
Let's look at comparative parallels and let's look at where things are going. And what I'm seeing with so-called mapping 3.0 is fascinating and big and growing and seemingly inevitable. I'd say the most remarkable thing I'm seeing here is the lack of other people trying to capture this opportunity.
Throughout this podcast, you're gonna hear about Specy and Laird Drone and maybe even some other projects, like I'm not gonna just start listing everything I can, but what I'm getting at is I can only think of a handful of businesses that are even attempting to do anything. I would consider basic table stakes to catalyze and capture the opportunity.[00:13:00]
Mapping 3.0 offers us. I am gonna leave it there. Uh, thank you so much for your time today, and I hope you enjoyed the show. Thanks for listening to Drone On Subscribe wherever you get your podcasts. Get a new episode every week and leave us a five star review on your podcast app of choice. You can learn more about our sponsors at Spexi.com.
That's Spexi.com and LayerDrone.org. Find out how you can contribute to the world's largest drone imagery network too. Thanks again for listening.