Truck traffic monitoring with satellite images
Open accessAbstract
The road freight sector is responsible for a large and growing share of greenhouse gas emissions, but reliable data on the amount of freight that is moved on roads in many parts of the world are scarce. Many low- and middle-income countries have limited ground-based traffic monitoring and freight surveying activities. In this proof of concept, we show that we can use an object detection network to count trucks in satellite images and predict average annual daily truck traffic from those counts. We describe a complete model, test the uncertainty of the estimation, and discuss the transfer to developing countries.
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Vehicles can be detected in high-resolution satellite images with high accuracy, and image counts can estimate average vehicle traffic.
“Vehicles can also be detected in high-resolution satellite images with high accuracy [177,390,566,747], and image counts can serve to estimate average vehicle traffic [405].”
From Tackling climate change with machine learning (2023) · cites this paper as [405]
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Cited by
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Tackling climate change with machine learning
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Self-citation
- Authors
- Kaack, L. H., Chen, G. H., & Morgan, M. G.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies
- Publisher
- ACM
- Published
- 2019-07-03
- Pages
- 155-164
- OpenAlex
- W2958535910
- Cited by
- 24
- References
- 23
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 9 · 2021: 5 · 2020: 2 · 2019: 1
- FWCI
- 1.39
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- OPUS 4 (Zuse Institute Berlin) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- arXiv (Cornell University)
- ETH Zürich Research Collection
- Funders
- National Science Foundation (0949710)
- Carnegie Mellon University (SES-0949710)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Environmental Science › Global and Planetary Change › Impact of Light on Environment and Health
- Keywords
- satellite imagery, object detection, road freight transport, greenhouse gas emissions, uncertainty estimation, low- and middle-income countries
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