  Air pollution represents a pivotal environmental challenge globally, playing
a major role in climate change via greenhouse gas emissions and negatively
affecting the health of billions. However predicting the spatial and temporal
patterns of pollutants remains challenging. The scarcity of ground-based
monitoring facilities and the dependency of air pollution modeling on
comprehensive datasets, often inaccessible for numerous areas, complicate this
issue. In this work, we introduce GeoFormer, a compact model that combines a
vision transformer module with a highly efficient time-series transformer
module to predict surface-level nitrogen dioxide (NO2) concentrations from
Sentinel-5P satellite imagery. We train the proposed model to predict
surface-level NO2 measurements using a dataset we constructed with Sentinel-5P
images of ground-level monitoring stations, and their corresponding NO2
concentration readings. The proposed model attains high accuracy (MAE 5.65),
demonstrating the efficacy of combining vision and time-series transformer
architectures to harness satellite-derived data for enhanced GHG emission
insights, proving instrumental in advancing climate change monitoring and
emission regulation efforts globally.
