Matthew Griffin, described as “The Adviser behind the Advisers” and a “Young Kurzweil,” is the founder and CEO of the World Futures Forum and the 311 Institute, a global Futures and Deep Futures consultancy working between the dates of 2020 to 2070, and is an award winning futurist, and author of “Codex of the Future” series. Regularly featured in the global media, including AP, BBC, CNBC, Discovery, RT, and Viacom, Matthew’s ability to identify, track, and explain the impacts of hundreds of revolutionary emerging technologies on global culture, industry and society, is unparalleled. Recognised for the past six years as one of the world’s foremost futurists, innovation and strategy experts Matthew is an international speaker who helps governments, investors, multi-nationals and regulators around the world envision, build and lead an inclusive, sustainable future. A rare talent Matthew’s recent work includes mentoring Lunar XPrize teams, re-envisioning global education and training with the G20, and helping the world’s largest organisations envision and ideate the future of their products and services, industries, and countries. Matthew's clients include three Prime Ministers and several governments, including the G7, Accenture, Bain & Co, BCG, Credit Suisse, Dell EMC, Dentons, Deloitte, E&Y, GEMS, Huawei, JPMorgan Chase, KPMG, Lego, McKinsey, PWC, Qualcomm, SAP, Samsung, Sopra Steria, T-Mobile, and many more.
WHY THIS MATTERS IN BRIEF
People have been using satellites to take photos of the Earth for decades but until now you’ve never been able to search it to find things of interest, or combine it with other data to identify patterns.
Recently a company called Planet increased the size of their satellite network so they could observe, and photograph, every square inch of the planet, but taking photos of everything is one thing, being able to find something in all those photos that you might be interested in is quite another. And there are plenty of companies, such as environmental groups, hedge funds and retailers, and many more, who’d like to do the latter so they can gather new information.
For example, there’s a single wind turbine near the intersection of 760th St. and Quincy Road in Massena, Iowa. It’s just one of thousands of them located around the country. What if you wanted to know where the others are?
Or what if you were looking at the solar farm near the intersection of New Mexico State Road 26 and New Mexico State Road 27, outside Deming, New Mexico and wanted to know where others are located?
Neither Google Earth, nor any other platform for that matter, has the ability to let you search for things, so how would you do it?
Thanks to a new tool from Descartes Labs – a New Mexico startup that provides artificial intelligence (AI) analysis of satellite imagery to industry, academia, and government – helping them to find every corn field, sports arena, wind turbine, smokestack, or any other object visible on satellite imagery – it’s now as easy as clicking on one you know about and letting the machines take over.
Launched yesterday, GeoVisual Search – go on, give it a try – lets anyone run an automatic query on one of three collections of satellite imagery – one for the US, one of the world, and one for China – in order to look for the location of just about any feature that’s identifiable in one of those collections.
“The companies we talk to and our clients get really excited when we talk to them about the new service,” says Mark Johnson, the CEO of Descartes Lab, “and they start brainstorming ideas on how to use geospatial imagery and machine intelligence for their business.”
The possibilities are endless, and the new tool gets even more powerful, for example, if you begin combining the data with other data sets – such as the ability to find out where all the wind farms are, correlate them with the local wind data and extrapolate out the amount of energy they might be producing. If you’re an energy trader then that information is gold dust, and if you’re an energy buyer then similarly you might be able to identify which utilities might have a surplus of cheap electricity. And what if you wanted to find out how many corn fields there are? And then combine that data with hyperspectral imaging data from other satellite systems to figure out how well the crops are growing?
And all of that is just the snowflake on the top of the iceberg.
Johnson said there have been previous, small scale attempts at creating such a search tool, including one done by a team at Carnegie Mellon University that let users query images across seven US cities.
“It’s cool to look at San Francisco, but San Francisco is just 50 square miles,” Johnson says, “we thought, ‘How do we do this for the entire planet?’”
The answer is by breaking the map of the US, China, or the world into a large number of tiles, employing a number of neural nets to evaluate a similarity score across each tile, and then quickly providing the tiles that are judged by the system to be most similar to the one originally searched.
The system uses the neural nets to look for “thumbprints,” Johnson explains, and then tries to find the closest matches.
The trick isn’t just finding the proper matches. It’s also providing them quickly. While the tool returns plenty of false positives – results that look similar to what’s being queried, but aren’t actually the same – it does an admirable job of delivering a list of quality results almost immediately.
Those results are terrific in the case of very distinct objects, like wind turbines, and a bit less impressive when searching for things like stadiums or suburbs.
But Johnson isn’t bothered by false positives. He even gets a bit excited talking about how a query for suburbs returns some results that are actually river channels running through mountains.
It does also return numerous actual locations of suburbs, and even with the errors, Johnson thinks that’s impressive, particularly given that the tool is capable of running these searches without ever being shown what a suburb is, or a smokestack, or a wind turbine, and so on. The system simply finds the results by comparing the contents of thousands of tiles to the contents of the original tile. And quickly.
As for the privacy implications? Johnson isn’t particularly worried about them because the imagery is from public satellites. Still, he does acknowledge that the ability to quickly analyse the imagery, which is updated daily, is something that’s never before been possible.
“Hopefully, people will use this for the good of the planet,” he says, “and not for nefarious purposes.”