The world is one big dataset. Now, how to photograph it ...

Five years ago, I was a Ph.D student

living two lives.

In one, I used NASA supercomputers

to design next generation spacecraft,

and in the other I was a data scientist

looking for potential smugglers

of sensitive nuclear technologies.

As a data scientist, I did a lot of analyses,

mostly of facilities,

industrial facilities around the world.

And I was always looking for a better canvas

to tie these all together.

And one day, I was thinking about how

all data has a location,

and I realized that the answer

had been staring me in the face.

Although I was a satellite engineer,

I hadn't thought about using satellite imagery

in my work.

Now like most of us, I'd been online,

I'd see my house, so I thought,

I'll hop in there and I'll start looking up

some of these facilities.

And what I found really surprised me.

The pictures that I was finding

were years out of date,

and because of that,

it had relatively little relevance

to the work that I was doing today.

But I was intrigued.

I mean, satellite imagery is pretty amazing stuff.

There are millions and millions of sensors

surrounding us today,

but there's still so much we
don't know on a daily basis.

How much oil is stored in all of China?

How much corn is being produced?

How many ships are in all of our world's ports?

Now, in theory, all of these questions

could be answered by imagery,

but not if it's old.

And if this data was so valuable,

then how come I couldn't get my hands

on more recent pictures?

So the story begins over 50 years ago

with the launch of the first generation

of U.S. government photo reconnaissance satellites.

And today, there's a handful

of the great, great grandchildren

of these early cold war machines

which are now operated by private companies

and from which the vast majority of satellite imagery

that you and I see on a daily basis comes.

During this period, launching things into space,

just the rocket to get the satellite up there,

has cost hundreds of millions of dollars each,

and that's created tremendous pressure

to launch things infrequently

and to make sure that when you do,

you cram as much functionality in there as possible.

All of this has only made satellites

bigger and bigger and bigger

and more expensive,

now nearly a billion, with a b, dollars per copy.

Because they are so expensive,

there aren't very many of them.

Because there aren't very many of them,

the pictures that we see on a daily basis

tend to be old.

I think a lot of people actually
understand this anecdotally,

but in order to visualize just how sparsely

our planet is collected,

some friends and I put together a data set

of the 30 million pictures that have been gathered

by these satellites between 2000 and 2010.

As you can see in blue, huge areas of our world

are barely seen, less than once a year,

and even the areas that are seen most frequently,

those in red, are seen at best once a quarter.

Now as aerospace engineering grad students,

this chart cried out to us as a challenge.

Why do these things have to be so expensive?

Does a single satellite really have to cost

the equivalent of three 747 jumbo jets?

Wasn't there a way to build a smaller,

simpler, new satellite design that could enable

more timely imaging?

Now I realize that it does sound a little bit crazy

that we were going to go out and just

begin designing satellites,

but fortunately we had help.

In the late 1990s, a couple of professors

proposed a concept for radically reducing the price

of putting things in space.

This was hitchhiking small satellites

alongside much larger satellite.

This dropped the cost of putting objects up there

by over a factor of 100,

and suddenly we could afford to experiment,

to take a little bit of risk,

and to realize a lot of innovation.

And a new generation of engineers and scientists,

mostly out of universities,

began launching these very small,

breadbox-sized satellites called CubeSats.

And these were built with electronics obtained

from RadioShack instead of Lockheed Martin.

Now it was using the lessons
learned from these early missions

that my friends and I began a series of sketches

of our own satellite design.

And, you know, I can't remember a specific day

where we made, like, a conscious decision

that we were actually going to
go out and build these things,

but it was just, once we got that idea in our minds

of the world as a data set,

of being able to capture millions of data points

on a daily basis, describing the global economy,

of being able to unearth billions of connections

between them that had never before been found,

it just seemed boring

to go work on anything else.

And so we moved into a cramped,

windowless office in Palo Alto,

and began working to take our design

from the drawing board into the lab.

Now the first major question we had to tackle

was just how big to build this thing.

In space, size drives cost,

and we had worked with these very small,

breadbox-sized satellites in school,

but as we began to better
understand the laws of physics,

we found that the quality of pictures

those satellites could take was very limited,

because the laws of physics dictate

that the best picture you
can take through a telescope

is a function of the diameter of that telescope,

and these satellites had a very small,

very constrained volume.

And we found that the best picture we would

have been able to get looked something like this.

And although this was the low-cost option,

quite frankly it was just too blurry

to see the things that make
satellite imagery valuable.

So about three or four weeks later,

we met a group of engineers randomly

who had worked on the first

private imaging satellite ever developed,

and they told us that back in the 1970s,

the U.S. government had found a powerful

optimal tradeoff,

that in taking pictures at right
about one meter resolution,

being able to see objects one meter in size,

they found that they could not
just get very high quality images,

but get a lot of them at an affordable price.

From our own computer simulations,

we quickly found that one meter really was

the minimum viable product

to be able to see the drivers of our global economy,

for the first time, being able to count

the ships and cars and shipping
containers and trucks

that move around our world on a daily basis,

while conveniently still not
being able to see individuals.

We had found our compromise.

We would have to build something larger

than the original breadbox,

now more like a mini-fridge,

but we still wouldn't have to build a pickup truck.

So now we had our constraint.

The laws of physics dictated

the absolute minimum-sized
telescope that we could build.

What came next was making the rest of the satellite

as small and as simple as possible,

basically a flying telescope with four walls

and a set of electronics smaller than a phone book

that used less power than a 100 watt light bulb.

The big challenge became actually taking

the pictures through that telescope.

Traditional imaging satellites use a line scanner,

similar to a Xerox machine,

and as they traverse the earth, they take pictures,

scanning row by row by row

to build the complete image.

Now people use these because they get a lot of light,

which means less of the noise you see

in a low-cost cell phone image.

The problem with them is they require

very sophisticated pointing.

You have to stay focused on a 50-centimeter target

from over 600 miles away

while moving at more than
seven kilometers a second,

which requires an awesome degree of complexity.

So instead, we turned to a new
generation of video sensors,

originally created for use in night vision goggles.

Instead of taking a single, high quality image,

we could take a videostream

of individually noisier frames,

but then we could recombine

all of those frames together

into very high quality images

using sophisticated pixel processing techniques

here on the ground,

at a cost of one one-hundredth a traditional system.

And we applied this technique

to many of the other systems on the satellite as well,

and day by day, our design evolved

from cad to prototypes

to production units.

A few short weeks ago,

we packed up SkySat 1,

put our signatures on it,

and waved goodbye for the last time on earth.

Today, it's sitting in its final launch configuration

ready to blast off in a few short weeks.

And soon, we'll turn our attention to launching

a constellation of 24 or more of these satellites

and beginning to build the scalable analytics

that will allow us to unearth the insights

in the pedabytes of data we will collect.

So why do all of this? Why build these satellites?

Well, it turns out imaging satellites

have a unique ability to provide global transparency,

and providing that transparency on a timely basis

is simply an idea whose time has come.

We see ourselves as pioneers of a new frontier,

and beyond economic data,

unlocking the human story, moment by moment.

For a data scientist

that just happened to go to space camp as a kid,

it just doesn't get much better than that.

Thank you.

(Applause)