How to Track Down an Invisible Fugitive from Space ⚡ экспресс
A new method based on the Mask R-CNN neural network searches for methane plumes in satellite images from MethaneSAT. Due to a lack of real data, the network was initially trained on aircraft data and synthetic examples — similar to training a facial recognition system on just a few photos. The resulting system works in two modes: "broad search" catches 94% of leaks, and "precise detector" yields 92% correct detections. This approach will accelerate the discovery and mitigation of methane leaks that impact the climate.
The MethaneSAT satellite scans for leaks of метана — a gas that's invisible but packs a powerful planetary warming punch. Its "nose" is a спектрометр, a device that spots the subtlest color changes in light passing through the atmosphere — the telltale sign of methane.
But picking up a scent isn't enough — you have to single it out from thousands of others. Where humans once pored over images, now the program is trained like a search dog: first it lunges at anything suspicious, like a pup following every random trail, then a second algorithm — the "handler" — confirms the catch. The real surprise? When the bloodhound "barks" at an empty spot, it often turns out to be an actual leak that cautious human experts missed.
This kind of hunt lets us quickly repair pipelines and landfills, cutting methane emissions that trap 80 times more heat than углекислого газа over the first 20 years.
🎯 Over 20 years, methane traps 80 times more heat than carbon dioxide.
🎬 Someday, such orbital bloodhounds will sniff the atmospheres of exoplanets in search of life.