  Road transportation is one of the largest sectors of greenhouse gas (GHG)
emissions affecting climate change. Tackling climate change as a global
community will require new capabilities to measure and inventory road transport
emissions. However, the large scale and distributed nature of vehicle emissions
make this sector especially challenging for existing inventory methods. In this
work, we develop machine learning models that use satellite imagery to perform
indirect top-down estimation of road transport emissions. Our initial
experiments focus on the United States, where a bottom-up inventory was
available for training our models. We achieved a mean absolute error (MAE) of
39.5 kg CO$_{2}$ of annual road transport emissions, calculated on a
pixel-by-pixel (100 m$^{2}$) basis in Sentinel-2 imagery. We also discuss key
model assumptions and challenges that need to be addressed to develop models
capable of generalizing to global geography. We believe this work is the first
published approach for automated indirect top-down estimation of road transport
sector emissions using visual imagery and represents a critical step towards
scalable, global, near-real-time road transportation emissions inventories that
are measured both independently and objectively.
