That is where the second technological trend comes in.

Dr Kruitwagen and his colleagues trained a machine-learning system to spot the solar panels for them.

Computer vision is a hot field.

But the specifics of orbital reconnaissance meant that off-the-shelf software was not suitable for the task the researchers had in mind.

Machine-learning systems are taught what to do by examining a "training set", which contains examples of what is being searched for.

For common tasks such as facial recognition, pre-built training sets are often available.

But Dr Kruitwagen's team had to build their own.

For this, they turned to OpenStreetMap, an open-source rival to Google Maps in which volunteers had already tagged large numbers of solar plants.

But there was little consistency.

"Some people had just drawn rough outlines around an entire field,"Dr Kruitwagen says.

"Others had gone in and traced the outline of each row of panels separately."

Fixing that involved a great deal of manual labour.

Once the training data had been cleaned up, the learning algorithms had to be tweaked as well.

From space, even big solar installations look small.

Each pixel in the Sentinel images represented a ten-by-ten-metre square.

Even for the higher-resolution SPOT satellites, the squares’ sides are one and a half metres long.

Existing classifiers, trained for things like facial recognition or self-driving cars, are used to spotting objects that loom large in their field of vision.

Hunting for smaller ones meant tinkering with the software to boost its ability to detect tiny features.

False positives—things like tennis courts and agricultural greenhouses that resemble solar panels from space—had to be removed.

Though extraordinary, Dr Kruitwagen’s results are already out of date.

The data-gathering phase of the project ended in 2018, meaning that the thousands of new plants built since then are not included.

But the project, he says, proves that the method works.

He intends to make his results, including the labour-intensive training set, available for others to use.

One logical extension of his project, he says, would be to expand the analysis to include solar panels installed on domestic rooftops.

Such “behind-the-meter” installations are particularly tricky to track in other ways.

More generally, Dr Kruitwagen hopes that his eye-in-the-sky approach—which, despite the planetary scale of the project, cost only around $15,000 in cloud-computing time—could presage more accurate estimates of other bits of climate-related infrastructure, such as fossil-fuel power stations, cement plants and terminals for ships carrying liquefied natural gas.

The eventual result could be the assembly of a publicly available, computer-generated inventory of every significant bit of energy infrastructure on Earth.

Quite apart from such a model's commercial and academic value, he says, an informed public would be one better able to hold politicians’ feet to the fire.