Traffic

Deep learning has already proven its potential in many areas of remote sensing. One example is the classification of land use and land cover from satellite and aerial imagery. But perhaps we can go one step further: once an AI model has learned how certain things are related, can we also ask it “what if?” questions? By deliberately changing the data we feed into the model, we can observe how its output changes. In this way, an AI could serve as a kind of simplified simulator, for example in so-called digital twins. These are virtual replicas of real cities or landscapes in which changes can be tested before they are implemented in the real world. ...

Commercial vehicle traffic is currently responsible for 7% of global CO2 emissions. While road freight will remain the dominant mode of surface freight transportation, its contribution to climate change is likely to increase in the short term. Therefore, the quantitative monitoring of commercial vehicle (CV) traffic is essential for implementing targeted road emission regulations. However, ground monitoring stations are costly and less than half of all countries worldwide collect road freight activity. In this work, we investigate the feasibility of detecting and monitoring CV traffic in freely available satellite imagery from ESA’s Sentinel-2 satellites. ...