Space 2.0

The New Frontier of Commercial Satellite Imagery for Business

One of my last speaking gigs of the spring season was, for me, both the least typical and one of the most interesting. Space 2.0 was a brief glimpse into a world that is both exotic and fascinating. It’s a gathering of high-tech, high-science companies driving commercialization of space.

Great stuff, but what the heck did they want with me?

Well, one of the many new frontiers in the space industry is the commercialization of geo-spatial data. For years now, the primary consumer of satellite data has been the government. But the uses for satellite imagery are hardly limited to intel and defense. For the array of Space startups and aggressive tech companies, intel and defense are relatively mature markets – slow moving and difficult to crack if you’re not an established player. You ever tried selling to the government? It’s not easy.

So the big opportunity is finding ways to open up the information potential in geo-spatial data and satellite imagery to the commercial marketplace. Now I may not know HyperSpectral from IR but I do see a lot of the challenges that companies face both provisioning and using big data. So I guess I was their doom-and-gloom guy – in my usual role of explaining why everything always turns out to be harder than we expect when it comes to using or selling big data.

For me, though, attending Space 2.0 was more about learning that educating. I’ve never had an opportunity to really delve into this kind of data and hearing (and seeing) some of what is available is fascinating.

Let’s start with what’s available (and keep in mind you’re not hearing an expert view here – just a fanboy with a day’s exposure). Most commercial capture is visual (other bands are available and used primarily for environmental and weather related research). Reliance on visual spectrum has implications that are probably second-nature to folks in the industry but take some thought if you’re outside it. Once speaker described their industry as “outside” and “daytime” focused. It’s also very weather dependent. Europe, with its abundant cloudiness, is much more challenging than the much of the U.S. (though I suppose Portland and Seattle must be no picnic).

Images are either panchromatic (black and white), multi-spectral (like the RGB we’re used to but with an IR band as well and sometimes additional bands) or hyperspectral (lots of narrow bands on the spectrum). Perhaps even more important than color, though, is resolution. As you’d probably expect, black and white images tend to have the highest resolution – down to something like a 30-40cm square. Color and multi-band images might be more in the meter range but the newest generation take the resolution down to the 40-50cm range in full color. That’s pretty fine grained.

How fine-grained? Well, with a top-down 40cm square per pixel it’s not terribly useful for things like people. But here’s an example that one of the speakers gave in how they are using the data. They pick selected restaurant locations (Chipotle was the example) and count cars in the parking lot during the day. They then compare this data to previous periods to create estimates of how the location is doing. They can also compare competitor locations (e.g. Panera) to see if the trends are brand specific or consistent.

Now, if you’re Chipotle, this data isn’t all that interesting. There are easy ways to measure your business than trying to count cars in satellite images. But if you’re a Fund Manager looking to buy or sell Chipotle stock in advance of earnings reports, this type of intelligence is extremely valuable. You have hard-data on how a restaurant or store is performing before everyone else. That’s the type of data that traders live for.

Of course, that’s not the only way to get that information. You may have heard about the recent FourSquare prediction targeted to exactly the same problem. Foursquare was able to predict Chipotle’s sales decline almost to the percentage point. As one of the day’s panelist’s remarked, there are always other options and the key to market success is being cheaper, faster, easier, and more accurate than alternative mechanisms.

You can see how using Foursquare data for this kind of problem might be better than commercial satellite. You don’t have weather limitations, the data is easier to process, it covers walk-in and auto traffic, and it covers a 24hr time band. But you can also see plenty of situations where satellite imagery might have advantages too. After all, it’s easily available, relatively inexpensive, has no sampling bias, has deep historical data and is global in reach.

So how easy is satellite data to use?

I think the answer is a big “it depends”. This is, first of all, big data. Those multi and hyper band images at hi-res are really, really big. And while the providers have made it quite easy to find what you want and get it, it didn’t seem to me that they had done much to solve the real big data analytics problem.

I’ve described what I think the real big data problem is before (you can check out this video if you want a big data primer). Big data analytics is hard because it requires finding patterns in the data and our traditional analytics tools aren’t good at that. This need for pattern recognition is true in my particular field (digital analytics), but it’s even more obviously true when it comes to big data applications like facial recognition, image processing, and text analytics.

On the plus side, unlike digital analytics, the need for image (and linguistic) processing is well understood and relatively well-developed. There are a lot of tools and libraries you can use to make the job easier. It’s also a space where deep-learning has been consistently successful so that libraries from companies like Microsoft and Google are available that provide high-quality deep-learning tools – often tailor made for processing image data – for free.

It’s still not easy. What’s more, the way you process these images is highly likely to be dependent on your business application. Counting cars is different than understanding crop growth which is different than understanding storm damage. My guess is that market providers of this data are going to have to develop very industry-specific solutions if they want to make the data reasonably usable.

That doesn’t necessarily mean that they’ll have to provide full on applications. The critical enabler is providing the ability to extract the business-specific patterns in the data – things like identifying cars. In effect, solving the hard part of the pattern recognition problem so that end-users can focus on solving the business interpretation problem.

Being at Space 2.0 reminded me a lot of going to a big data conference. There’s a lot of technologies (some of them amazingly cool) in search of killer business applications. In this industry, particularly, the companies are incredibly sophisticated technically. And it’s not that there aren’t real applications. Intelligence, environment and agriculture are mature and profitable markets with extensive use of commercial satellite imagery. The golden goose, though, is opening up new opportunities in other areas. Do those opportunities exist? I’m sure they do. For most of us, though, we aren’t thinking satellite imagery to solve our problems. And if we do think satellite, we’re likely intimidated by difficulty of solving the big data problem inherent in getting value from the imagery for almost any new business application.

That’s why, as I described it to the audience there, I suspect that progress with the use and adoption of commercial satellite imagery will seem quite fast to those of us on the outside – but agonizingly slow to the people in the industry.