Towards Indirect Top-Down Road Transport Emissions Estimation

TL;DR

This paper presents a machine learning approach using satellite imagery to estimate road transport emissions indirectly, aiming for scalable, global, near-real-time inventories to better address climate change.

Contribution

It introduces the first automated method for indirect top-down estimation of road transport emissions using visual satellite data, enabling scalable global monitoring.

Findings

Achieved a mean absolute error of 39.5 kg CO₂ per pixel in US data.

Demonstrated the feasibility of satellite-based emission estimation.

Discussed challenges for global model generalization.

Abstract

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.