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.

GeoViT 是一种紧凑的视觉转换模型，熟练处理卫星图像以进行多模式分割、分类和回归任务，目标是 CO2 和 NO2 排放。利用 GeoViT，我们在估计发电率、燃料类型、CO2 的尾气覆盖率和高分辨率 NO2 浓度地图方面获得了卓越的准确性，超过了先前的最先进模型，同时显著减小了模型尺寸。GeoViT 表明了视觉转换器架构在利用卫星数据提升温室气体排放见解方面的功效，对全球气候变化监测和排放规章制度的推进具有关键作用。