TY - CHAP A1 - Kaack, Lynn A1 - Chen, George A1 - Granger Morgan, M T1 - Truck traffic monitoring with satellite images T2 - Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies N2 - 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. Y1 - 2019 UR - https://opus4.kobv.de/opus4-hsog/frontdoor/index/index/docId/4122 U6 - https://doi.org/10.1145/3314344 SP - 155 EP - 164 PB - Association for Computing Machinery CY - New York ER -