AI Case Study
Berkeley Lab to use deep learning to analyse alternate data sources for more accurate predictions of air quality
The US Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) are using deep learning methods to analyse additional information, such as satellite images and cellphone data, to more accurately predict air quality levels.
Public And Social Sector
DeepAir (Deep Learning and Satellite Imaginary to Estimate Air Quality Impact at Scale) "will take advantage of the power of deep learning algorithms to analyze satellite images combined with traffic information from cell phones and data already being collected by environmental monitoring stations.... 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... 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." The project will 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."
Planned; results not yet available
"Thirty percent of energy use in the U.S. is to transport people and goods, and this energy consumption contributes to air pollution, including approximately half of all nitrogen oxide emissions, a precursor to particular matter and ozone – and black carbon (soot) emissions".
Satellite images, cell phone data