  Greenhouse gases are pivotal drivers of climate change, necessitating precise
quantification and source identification to foster mitigation strategies. We
introduce GeoViT, a compact vision transformer model adept in processing
satellite imagery for multimodal segmentation, classification, and regression
tasks targeting CO2 and NO2 emissions. Leveraging GeoViT, we attain superior
accuracy in estimating power generation rates, fuel type, plume coverage for
CO2, and high-resolution NO2 concentration mapping, surpassing previous
state-of-the-art models while significantly reducing model size. GeoViT
demonstrates the efficacy of vision transformer architectures in harnessing
satellite-derived data for enhanced GHG emission insights, proving instrumental
in advancing climate change monitoring and emission regulation efforts
globally.
