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This paper presents a novel two-part pipeline for monitoring progress towardsthe UN Sustainable Development Goals (SDG's) related to Climate Action andSustainable Cities and Communities. The pipeline consists of two main parts:the first part takes a raw satellite image of a motorway section and producestraffic count predictions for count sites within the image; the second parttakes these predicted traffic counts and other variables to produce estimatesof Local Authority (LA) motorway Average Annual Daily Traffic (AADT) andGreenhouse Gas (GHG) emissions on a per vehicle type basis. We also provideflexibility to the pipeline by implementing a novel method for estimatingemissions when data on AADT per vehicle type or/and live vehicle speeds are notavailable. Finally, we extend the pipeline to also estimate LA A-Roads andminor roads AADT and GHG emissions. We treat the 2017 year as training and 2018as the test year. Results show that it is possible to predict AADT and GHGemissions from satellite imagery, with motorway test year $R^2$ values of 0.92and 0.78 respectively, and for A-roads' $R^2$ values of 0.94 and 0.98. Thisend-to-end two-part pipeline builds upon and combines previous research in roadtransportation traffic flows, speed estimation from satellite imagery, andemissions estimation, providing new contributions and insights into theseareas.

Predicting Regional Road Transport Emissions From Satellite Imagery