But its Planets ability to make the data accessible to every government and every business in
the world that makes our TAM truly massive. Today, our business starts with our core data, a daily 3- to 4-meter per pixel scan of the entire Earth, thousands of daily
high-resolution tasked images and of course, our archive of over 1 billion images going back five years. Thats over 1,500 images for every single point on the face of the Earth. But the even bigger opportunity, and were still in the
early days here but we are clearly on this path, is in moving up the stack from imagery to data and APIs, to machine learning, and time series.
Were building a platform on top of our proprietary data, services that could be mixed and matched together like Lego blocks, things that provide new
capabilities, extract new insights, and generally make our data more valuable and easier to use. Ive seen this before in my career. When I was at Twitter, we built a product called the Firehose, which is a way for developers to subscribe to
the Firehose of all public tweets every day. It still exists, so every time you tweet, or I tweet or President Obama or Biden tweets, or Nicki Minaj tweets, 50 milliseconds later, any partner subscribing to the Firehose has it pushed to them.
When we launched this, we were beyond excited at Twitter. The pulse of the planet every single day in developers hands. Just think of the cool things
theyre going to build with it. We were partially right because when youif you look at sophisticated partners like Google, they did some amazing things. They built tweets into real time search so that two minutes after something happened
out in the real world, they were answering search queries they had never seen before with tweets. But most developers blanched at the idea of managing 5,000 tweets per second coming at them in 100 different languages.
It turned out, the way to really scale Twitters data business was to make it easier to consume, to provide services that say, provided a count of tweets
talking about your brand, gave each one a sentiment score, linked them back to your advertising campaigns, and sent that to you daily in a CSV. Some companies needed the full Firehose, but every company needed a time series of buzz about their
brand. There is a real analog with our path here at Planet, though on a much bigger scale for us. Im going to take you through this in more detail.
In fact, Im going to show you how were building the strategy from the bottom up, with each layer stacking on top of and reinforcing the ones below
it. Each of the icons in these slides represents an example of data or a single service at each layer. Here you see our PlanetScope monitoring with our doves, high-res tasking, our future hyperspectral
imagery. Ill warn you; this slide is going to get busy, but youll see its exactly because the market opportunity is so large and so varied for Planet and for our partners.
Just this first layer. Planet began by providing a cloud interface to our raw imagery, right. Naturally, our first customers were highly sophisticated at
processing large volumes of geospatial imagery. As an example, we help Corteva monitor 800,000 fields daily across all the farmers that they serve. But we havent stopped there. We have over 25 terabytes of data coming down from our satellites
every day. We have to move beyond humans using their eyeballs to look at imagery. The future is about computers running algorithms and analysis to automatically identify objects, patterns, time series.
All of our data is analytics ready and that is a new Lego block that our partners and us can use to train machine learning, remote sensing algorithms on our
proprietary data. Were already doing this. For example, we use our data as a base to fuse with data that comes from other sensors, other optical sensors that have different characteristics, microwave sensors, even radar in the form of SAR and
that allows us to mix in new capabilities to get at things like soil temperature, or the ability to see through clouds.
With these blocks in place,
were starting to extract even more sophisticated products. As we do, notice that these new products dont look anything like geospatial data. They start to look more like a matrix, spreadsheet, a time series, something that anybody who
knows how to use Excel can use. This is why with each step along this path, were not just reducing time to value for our customers, were also meaningfully increasing our customer base and our TAM. Examples at this level are things like
road building or ship detection, something that you can imagine any government being interested in. Or crop classification, so how many acres of soybean are growing in China this year? Or things like monitoring the growth of coral or other natural
assets, looking at water reservoir levels, and so on.