  Methane is one of the most potent greenhouse gases, and its short atmospheric
half-life makes it a prime target to rapidly curb global warming. However,
current methane emission monitoring techniques primarily rely on approximate
emission factors or self-reporting, which have been shown to often dramatically
underestimate emissions. Although initially designed to monitor surface
properties, satellite multispectral data has recently emerged as a powerful
method to analyze atmospheric content. However, the spectral resolution of
multispectral instruments is poor, and methane measurements are typically very
noisy. Methane data products are also sensitive to absorption by the surface
and other atmospheric gases (water vapor in particular) and therefore provide
noisy maps of potential methane plumes, that typically require extensive human
analysis. Here, we show that the image recognition capabilities of deep
learning methods can be leveraged to automatize the detection of methane leaks
in Sentinel-2 satellite multispectral data, with dramatically reduced false
positive rates compared with state-of-the-art multispectral methane data
products, and without the need for a priori knowledge of potential leak sites.
Our proposed approach paves the way for the automated, high-definition and
high-frequency monitoring of point-source methane emissions across the world.
