Google/Skybox could offer a searchable DIFF of the world
June 19, 2014 | By Peter Bihr |
Since Google announced to buy satellite company Skybox recently, there’s been quite a bit of speculation about the reasons and potential implications of the acquisition. Some wondered about the emerging picture of a Google that owns military robots and drones and has access to information about both outside and inside our homes; others looked at how regularly updated satellite images could improve maps, or how a real-time map of, say, available parking spots might be possible with this technology. Or predictions about the market and economics developments. Wired speculates about Really Big Data and geopolitical forecasting.
Writes The Atlantic:
Right now, the raw imagery created by satellite cameras can be hard to decode and process for non-experts. Therefore, many companies like Skybox hope to sell “information, not imagery.” Instead of pixels, they’ll give customers algorithmically-harvested assessments of what’s in the pixels. For example, using regular satellite-collected data, an algorithm could theoretically look for leaks in an Arctic pipeline and alert the pipeline’s owners when one appeared.
This at least is one of the visions Skybox promotes in their videos:
It’s hard to tell how much of this is possible yet; I’d assume it’s nowhere near as complete now as it might seem. But it is a near-real time video feed of a large part of the surface of the world that – at some point – could be analyzed and converted into actionable data.
A searchable DIFF
And that’s where it gets really interesting: With this kind of technology, once it’s ready for prime time, Google could offer a complete over-time picture, a searchable visual and data representation – a DIFF of the world.
Imagine a cargo container, sitting in a dock, loaded onto a ship, the ship moving (and recognized by the image processing algorithms as such), the container being unloaded and put on a train, then a truck, then opened up and emptied. At any given time, you can trace (and trace back, if in hindsight it becomes interesting) how the tracked object has moved over time.
Live analysis combining a variety of data sources
Fast forward a few years and into version two of the toolkit (maybe) being built here. Then we’re looking at a much bigger picture. Assume a lot more processing power is now available to process, analyze, categorize and save the data available from the satellite images. Maybe enriched by other data sources, too. Now you can offer to pull together unforeseen searches on the real world as a service, similar to the way Wolfram Alpha lets you perform calculations by pulling together data from various sources – weather and traffic data; processed video feeds from drones; market and stock info; communications and network data, etc. – and combining them into one powerful analysis tool.
I find it hard to come up with good examples for this off the top of my head; let’s try anyway. Say you want to know how many trucks vs cars pass over a certain bridge. Or where to find the highest density of SUVs globally. Or the ratio of swimming pools per capita in LA compared to New Delhi compared to London. Or correlate the length of lines at bus stops to the local weather. Or want to know where your car ended up after it got stolen, and where the person went who stole it.
These examples are pretty weak, admittedly. But suffice to say that the range of applications – in commercial, military & security, social contexts – are enormous – ludicrously enormous – for good and evil alike.