Bringing to mind the landscape paintings of Peder Balke—or maybe Hokusai is more appropriate—entire cliffs seem to “wander” across the surface of Comet 67P.
“The hills may not be alive, but they are moving,” New Scientist reports. “The comet 67P/Churyumov-Gerasimenko has small cliffs that migrate across the landscape for months at a time,” apparently moving toward—not away from—the sun “at a rate of between 3 and 7 centimetres an hour.”
“The cliffs, or scarps, in question are only between 1 and 2 metres tall,” we read, “but on a comet the size of 67P, which is just 4 kilometres across at its longest point, they aren’t negligible—cliffs of a similar scale on Earth would be about 3 kilometres high.”
Frozen waves of geology, marching toward the sun in space.
Imagine a novel about a landscape photographer sent to record such sights, and the things she sees, the weird remoteness of it all, the camp sites and technical difficulties, where exposure time and depth-of-focus becomes an interplanetary concern, the ground pulsing continuously beneath her feet in a slow tide, a creeping sludge, that will never reach completion.
In the story of how European Space Agency researchers are scrambling to locate—and possibly move—the Philae probe, which they successfully landed on Comet 67P two days ago, there’s an interesting comment about computer vision and the perception of exotic landscapes.
“We’re working our eyes off,” one of the scientists says to New Scientist, describing how they are personally and individually poring over photographs of the comet.
“It’s an entirely manual process,” New Scientist continues, “because the complex and bizarre landscape of comet 67P defies any kind of automated search. ‘We don’t have an algorithm for this,’ he says.”
We don’t have an algorithm for this.
[Image: The irregular terrain of Comet 67P, via ESA].
It would be interesting to develop a taxonomy of landscapes based on their recognizability to algorithms. This would tell you as much about how computers see the world as it would about the aesthetic assumptions—even the geological biases—of the people who programmed those computers.
Think, for example, of Adam Harvey’s work, asking When Is An Apple No Longer An Apple? That project explored the point at which machine-learning algorithms could no longer distinguish the iconic fruit from a jumble of colorful objects.
Or take Harvey’s more recent CV Dazzle experiment, which looked at how to prevent facial recognition software from identifying a face at all through the clever use of cosmetic camouflage.
However, in the case of Comet 67P and other extreme topographic environments, we would be looking at when a landscape is no longer a landscape, so to speak, at least in terms of the computer-vision algorithms programmed to analyze it.
What other landscapes fall within this category—of spatial environments unrecognizable to machines—and what do those spaces reveal about the dimensional prejudices of the algorithm? Light and shadow; depth and range; foreground and background; geometry and complexity.
Bump Adam Harvey’s investigations up to the scale of a landscape, and a million potential design projects beckon. Learning from Comet 67P.