DeepAir – Deep Learning and Satellite Imagery to Estimate Air Quality Impact at Scale, uses deep learning algorithms to analyze satellite images combined with traffic information from cell phones and data already being collected by environmental sensors to improve air quality predictions.
In past research, we have used cell phone data to study how people move around cities and to recommend electric vehicle charging schemes to save energy and costs. For this project, we 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.
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. 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.
We anticipate that the resulting analysis will allow them to gain insights into the sources and distribution of pollutants, and ultimately allow for the design of more efficient and more timely interventions. For example, the Bay Area has “Spare the Air” days, in which traffic restrictions are voluntary, and other cities have schemes to restrict traffic or industry.