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Towards Indirect Top-Down Road Transport Emissions Estimation
- Source :
- CVPR Workshops
- Publication Year :
- 2021
- Publisher :
- arXiv, 2021.
-
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.
- Subjects :
- Estimation
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer Science - Artificial Intelligence
business.industry
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Climate change
Top-down and bottom-up design
Environmental economics
Machine Learning (cs.LG)
Inventory valuation
Artificial Intelligence (cs.AI)
Work (electrical)
Greenhouse gas
Scale (social sciences)
Satellite imagery
Artificial intelligence
business
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- CVPR Workshops
- Accession number :
- edsair.doi.dedup.....dab233aa438881dc645093cbb522d712
- Full Text :
- https://doi.org/10.48550/arxiv.2103.08829