GeoViT: A Versatile Vision Transformer Architecture for Geospatial Image Analysis
This addresses the need for precise climate change monitoring and emission regulation globally, representing a domain-specific advancement.
The paper tackled the problem of quantifying greenhouse gas emissions from satellite imagery by introducing GeoViT, a compact vision transformer model, which achieved superior accuracy in tasks like estimating power generation rates and NO2 concentration mapping compared to previous state-of-the-art models while reducing model size.
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.