DeepAir (Deep Learning and Satellite Imaginary to Estimate Air Quality Impact at Scale) is one of two projects at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) which is utilizing deep learning algorithms. Led by Professor of City and Regional Planning Marta González, DeepAir analyzes a combination of satellite images, traffic information from cell phones, and data collected by environmental monitoring stations. The resulting analysis will provide researchers a more complex picture of the sources and distribution of pollutants, which will ultimately allow for more efficient and more timely interventions to improve air quality conditions
“The novelty here is that while the environmental models, which show the interaction of pollutants with weather – such as wind speed, pressure, precipitation, and temperature – have been developed for years, there’s a missing piece,” González said. “In order to be reliable, those models need to have good inventories of what’s entering the environment, such as emissions from vehicles and power plants.
“We bring novel data sources such as mobile phones, integrated with satellite images. In order to process and interpret all this information, we use machine learning models applied to computer vision. The integration of information technologies to better understand complex natural system interactions at large scale is the innovative piece of DeepAir.”
González has also made use of cell phone data in previous research to study how people move around cities and to recommend electric vehicle charging schemes to save energy and costs.
The other project, CIRCLES, or Congestion Impact Reduction via CAV-in-the-loop Lagrangian Energy Smoothing, is a traffic optimization project led by Alexandre Bayern, professor of Electrical Engineering and Computer Science at UC Berkeley and Director of UC Berkeley’s Institute of Transportation Studies. CIRCLES is based on a software framework called Flow, developed by Bayen’s team of students and post-doctoral researchers.
Read more about CIRCLES and DeepAir here.