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.

翻译：温室气体是气候变化的关键驱动因素，精确量化与源识别对制定减排策略至关重要。本文提出GeoViT——一种轻量级视觉Transformer模型，专为处理卫星影像而设计，可执行针对CO2与NO2排放的多模态分割、分类及回归任务。基于GeoViT，我们在发电速率估算、燃料类型识别、CO2烟羽覆盖范围绘制及高分辨率NO2浓度映射等任务中取得了领先精度，在显著缩减模型规模的同时超越了现有最优模型。GeoViT验证了视觉Transformer架构在利用卫星数据增强温室气体排放洞察方面的有效性，为全球气候变化监测与排放监管工作提供了关键技术支持。