The new generation of hyperspectral imagers, such as PRISMA, has improvedsignificantly our detection capability of methane (CH4) plumes from space at highspatial resolution (∼30m). We present here a complete framework to identifyCH4 plumes using images from the PRISMA satellite mission and a deep learningtechnique able to automatically detect plumes over large areas. To compensatefor the sparse database of PRISMA images, we trained our model by transposinghigh resolution plumes from Sentinel-2 to PRISMA. Our methodology avoidscomputationally expensive synthetic plume from Large Eddy Simulations whilegenerating a broad and realistic training database, and paves the way for large-scale detection of methane plumes using future hyperspectral sensors (EnMAP, EMIT, CarbonMapper).