Tamma Carleton, postdoctoral scholar at the Energy Policy Institute at UChicago, will present on:
Generalizing global observation with satellite imagery and machine learning
Combining satellite imagery with machine learning presents an opportunity for assembling globally comprehensive observations of many variables simultaneously. However, current approaches require custom systems, expert knowledge, access to imagery, and extensive computational resources. We develop a generalized system that enables researchers with basic statistical training to use satellite imagery and machine learning to study any variable visible from space at low computational cost. We demonstrate the generalizability of our system by constructing high resolution estimates for seven domains across the globe; we find comparable performance to a state-of-the-art convolutional neural network at a fraction of the cost.
Thursday, May 30, 2019
(12-12:15 lunch; 12:15-1:15: presentation; 1:15-1:30 Q+A and networking)
Searle Chemistry Lab
5735 S. Ellis Ave, Room 240A
Chicago, IL 60637
Take the elevator to the 2nd floor. When you exit, take two right turns and walk to the end of the hallway.
Lunch wil be provided.
More info about the Environmental Data Science Lunch:
2019 winter and spring schedule: