Deep Learning for Rare Energy Infrastructures in Satellite Imagery

Project Summary

A team of students led by researchers in the Energy Data Analytics Lab, Electrical & Computer Engineering, and with participation from the Energy Access Project will investigate how to use synthetically-generated satellite imagery to improve the identification of energy infrastructure in satellite imagery. The detected energy infrastructure will fill outstanding data gaps in the ability to identify pathways for electrification in low-income countries. The team will build the foundation for research that can identify objects that appear relatively rarely in satellite imagery and accomplish this using very limited training examples by creating realistic synthetic 3D models of those rare objects.  This would greatly scale up the applicability of computer vision techniques for energy object identification in overhead imagery.

Project Lead:  Kyle Bradbury

Themes and Categories
Year
2020
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

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