DeepAir: Deep learning and Satellite Imaginary to estimate Air Quality impacts at scale
DeepAir is an innovative application of data fusion of infrastructure imagery (urban form and transportation networks) with environmental sensors. The goal is to enable science-informed policy by understanding interdependencies between infrastructure, transportation and environment. We adopt state of the art techniques in computer vision and urban traffic data from mobile phones to quantitatively link transportation policy interventions with air quality improvement. Such tools will allow for the design of improved static and mobile air pollution sensing networks that are rapidly evolving with emerging sensor technologies (personal monitors, satellite monitoring, and even non-air quality measurements). These methodologies are aimed to be fully scalable, and can be applied at the local to regional scales. The methods applied here can be extended to other domains that involve both the human and environmental interactions.
Marta Gonzalez, is a Faculty Research Scientist in the Energy Analysis and Environmental Impacts Division. She is also an associate professor, Civil and Environmental Engineering and City and Regional Planning in the College of Environmental Design. Her research includes the application of big data to understanding human network behavior, with applications in transportation networks, energy efficiency planning, and characterization of disease proliferation. Prior to joining Berkeley, Marta worked as an Associate Professor of Civil and Environmental Engineering at MIT.