And that's just from comparing the map to the satellite imagery. But there are also a variety of other tools at Google's disposal. One is bringing in data from other sources, say the US Geological Survey. But Google's Ground Truthers can also bring another exclusive asset to bear on the maps problem: the Street View cars' tracks and imagery. In keeping with Google's more-data-is-better-data mantra, the maps team, largely driven by Street View, is publishing more imagery data every two weeks than Google possessed total in 2006.*

Let's step back a tiny bit to recall with wonderment the idea that a single company decided to drive cars with custom cameras over every road they could access. Google is up to five million miles driven now. Each drive generates two kinds of really useful data for mapping. One is the actual tracks the cars have taken; these are proof-positive that certain routes can be taken. The other are all the photos. And what's significant about the photographs in Street View is that Google can run algorithms that extract the traffic signs and can even paste them onto the deep map within their Atlas tool. So, for a particularly complicated intersection like this one in downtown San Francisco, that could look like this:

Google Street View wasn't built to create maps like this, but the geo team quickly realized that computer vision could get them incredible data for ground truthing their maps. Not to detour too much, but what you see above is just the beginning of how Google is going to use Street View imagery. Think of them as the early web crawlers (remember those?) going out in the world, looking for the words on pages. That's what Street View is doing. One of its first uses is finding street signs (and addresses) so that Google's maps can better understand the logic of human transportation systems. But as computer vision and OCR improve, any word that is visible from a road will become a part of Google's index of the physical world.



Later in the day, Google Maps VP Brian McClendon put it like this: "We can actually organize the world's physical written information if we can OCR it and place it," McClendon said. "We use that to create our maps right now by extracting street names and addresses, but there is a lot more there."

More like what? "We already have what we call 'view codes' for 6 million businesses and 20 million addresses, where we know exactly what we're looking at," McClendon continued. "We're able to use logo matching and find out where are the Kentucky Fried Chicken signs ... We're able to identify and make a semantic understanding of all the pixels we've acquired. That's fundamental to what we do."

For now, though, computer vision transforming Street View images directly into geo-understanding remains in the future. The best way to figure out if you can make a left turn at a particular intersection is still to have a person look at a sign -- whether that's a human driving or a human looking at an image generated by a Street View car.

There is an analogy to be made to one of Google's other impressive projects: Google Translate. What looks like machine intelligence is actually only a recombination of human intelligence. Translate relies on massive bodies of text that have been translated into different languages by humans; it then is able to extract words and phrases that match up. The algorithms are not actually that complex, but they work because of the massive amounts of data (i.e. human intelligence) that go into the task on the front end.

Google Maps has executed a similar operation. Humans are coding every bit of the logic of the road onto a representation of the world so that computers can simply duplicate (infinitely, instantly) the judgments that a person already made.

This reality is incarnated in Nick Volmar, the operator who has been showing off Atlas while Weiss-Malik and Gupta explain it. He probably uses twenty-five keyboard shortcuts switching between types of data on the map and he shows the kind of twitchy speed that I associate with long-time designers working with Adobe products or professional Starcraft players. Volmar has clearly spent thousands of hours working with this data. Weiss-Malik told me that it takes hundreds of operators to map a country. (Rumor has it many of these people work in the Bangalore office, out of which Gupta was promoted.)

The sheer amount of human effort that goes into Google's maps is just mind-boggling. Every road that you see slightly askew in the top image has been hand-massaged by a human. The most telling moment for me came when we looked at couple of the several thousand user reports of problems with Google Maps that come in every day. The Geo team tries to address the majority of fixable problems within minutes. One complaint reported that Google did not show a new roundabout that had been built in a rural part of the country. The satellite imagery did not show the change, but a Street View car had recently driven down the street and its tracks showed the new road perfectly.

Volmar began to fix the map, quickly drawing the new road and connecting it to the existing infrastructure. In his haste (and perhaps with the added pressure of three people watching his every move), he did not draw a perfect circle of points. Weiss-Malik and I detoured into another conversation for a couple of minutes. By the time I looked back at the screen, Volmar had redrawn the circle with perfect precision and upgraded a few other things while he was at it. The actions were impressively automatic. This is an operation that promotes perfectionism.

And that's how you get your maps to look this this:

Some details are worth pointing out. In the top at the center, trails have been mapped out and coded as places for walking. All the parking lots have been mapped out. All the little roads, say, to the left of the small dirt patch on the right, have also been coded. Several of the actual buildings have been outlined. Down at the bottom left, a road has been marked as a no-go. At each and every intersection, there are arrows that delineate precisely where cars can and cannot turn.

Now imagine doing this for every tile on Google's map in the United States and 30 other countries over the last four years. Every roundabout perfectly circular, every intersection with the correct logic. Every new development. Every one-way street. This is a task of a nearly unimaginable scale. This is not something you can put together with a few dozen smart engineers.