  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.
