Machine Learning for Activity-Based Road Transportation Emissions Estimation (Papers Track) Spotlight
Derek Rollend (JHU); Kevin Foster (JHU); Tomek Kott (JHU); Rohita Mocharla (JHU); Rodrigo Rene Rai Munoz Abujder (Johns Hopkins Applied Physics Laboratory); Neil Fendley (JHU/APL); Chace Ashcraft (JHU/APL); Frank Willard (JHU); Marisa Hughes (JHU)
Abstract
Measuring and attributing greenhouse gas (GHG) emissions remains a challenging problem as the world strives towards meeting emissions reductions targets. As a significant portion of total global emissions, the road transportation sector represents an enormous challenge for estimating and tracking emissions at a global scale. To meet this challenge, we have developed a hybrid approach for estimating road transportation emissions that combines the strengths of machine learning and satellite imagery with localized emissions factors data to create an accurate, globally scalable, and easily configurable GHG monitoring framework.