Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation
Dec 2, 2022
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Alexis Groshenry
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Clément Giron
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Thomas Lauvaux
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Thibaud Ehret
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Alexandre d'Aspremont
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About
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).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 t…
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NeurIPS 2022
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