Dani Arribas-Bel

Last week I came back from ten days in China on a research project. It was a whirlwind tour with five cities, and lots of opportunities to interact with both the, academic and otherwise, Chinese world. It was fascinating. At many levels, it felt like a peek into the future.

As part of the trip, I had the opportunity to give two talks, one at the Chinese Academy of Sciences (CAS) in Beijing and another at Southwest University in Chongqing. Both were titled “Making satellite imagery more useful, usable, and used in Social Science and policymaking”, and both had a very similar structure. The CAS talk was a bit longer, and it allowed me to illustrate practically many of the ideas I talked about in the first part using our ongoing EuroFab. Here I want to focus on the more conceptual bit, which aligns very much with the motivation behind Imago.

The talk tries to build the case for paying more attention and interest to satellite imagery as a source of data and information in the context of social science and policymaking. I structured this argument in four acts¹: the value proposition, the promise, the reason, and the gap.

Start with the value proposition. We’re at the confluence of three trends that make this a unique moment to look at satellite imagery with “untraditional” eyes. One, there’s never been more and better satellite(s and) imagery available. Launching stuff into space is becoming dramatically cheaper than it used to be. As Econ 101 would predict, you lower the price of something, more of that is bought, and we have more and more satellites being placed on orbit (more data). These are also benefitting from the revolution in consumer electronics of the last few decades, so the sensors on board are able to capture much more than before (better data). Second, we have better ways of extracting information from such imagery. Images contain a lot of useful information but it’s all encoded in pixels that don’t mean anything in themselves. To make something useful, you need a “magic decoder” that turns pixels into buildings, roads, land uses, etc. Well, the last fifteen years of progress in computer vision have delivered just that. From the first deep learning models of the early 2010s, into the more advanced neural nets of the end of the decade to contemporary foundation models for vision, how we teach computers to see things has undergone nothing short of a revolution. And third, which seems less relevant but it’s the piece that makes it all possible, the technology stack required to access such algorithms and operate on such data has been dramatically democratised. Compute is cheap(ish) and software is free(ish). It’s a brave new world.

Now the promise. What the confluence of the three trends above means for social researchers and policymakers is we can now do at least two things we couldn’t before. One, to measure things we haven’t been able to measure (at scale); and two, to measure better, sooner and/or more often things we’re now measuring slowly, late and sparsely. Both are, in some ways, the two sides of the same coin. They capture how the same “opening up” of new sources of information affect different status quo. In some cases (e.g., temperature, air quality, certain features of the built environment such as solar panel installations), we have not been able to measure certain phenomena that are relevant for social processes at the scale and detail required to consider them seriously in our work. In others, we do have a current solution, but it is lacking in many ways and we use it because it’s better than nothing, not because it’s good. For many of these cases, I think satellite imagery is poised to redefine what we consider acceptable for data feeding into research and policy.

Why, beyond “just because we can”, should we try to realise such promise? The short version is we’ve never needed new, timely and accurate data more than now. The world is becoming more unstable and unpredictable (e.g., climate change, spatial inequalities). We can’t “wait for the next Census” (which might not even happen in some places!) to know how society and the built environment are changing. Decisions need to be made before then and, for those to be meaningful, there’s a whole lot of understanding we need to unlock first. And, by the way, this is not a clash between traditional sources of data like the Census and newer ones like satellites. We need both of them, separately, but particularly in tandem: satellites can “stretch” the value of Censuses by, for example, providing additional context; and most of the value held by imagery relies almost entirely on our ability to relate them to existing measurements of phenomena we want, of labels. Our best shot at that “labelling” in the social realm are traditional sources.

And, finally, why aren’t we already realising this promise? This is what I called The Gap. Again, three main reasons in my view. One, imagery is big. If nothing else, there is a lot of it and it takes a lot of space on storage disks. To a point that changes how you have to think about working with it (this is the “it doesn’t fit in your laptop” problem). Two, imagery is hard. The problem is not only that managing a large volume is challenging in itself, it’s that it is also non-trivial to make something useful out of images. Something something, magic decoders, something, something. And, yes, these have become much more accessible, but it’s still the case the science underpinning all of this is tricky. And, three, imagery is different. We don’t train social scientists to work with images. Local government officers and government analysts don’t know where to start to make sense of it all. This is in contrast to other disciplines where imagery is front and centre, their bread and butter. Social sciences have, for a long time, spent all of their training allowance on other skills and techniques that are fundamentally different.

So that’s it, my 20 minutes² to convince you are up. If you’re interested in these ideas and in acting on them, stay tuned for everything we’re doing at Imago. It’s still early days so much of what we’re focused on is on getting started. Like any big ship, getting momentum takes a lot of energy and time. But once you’re going…