Electricity Access in Developing Countries from Aerial Imagery


A team of students led by researchers in the Energy Data Analytics Lab and the Sustainable Energy Transitions Initiative will develop means to evaluate electricity access in developing countries through machine learning techniques applied to aerial imagery data. Students will identify features of satellite imagery that can be used to demonstrate whether a community has access to electricity, create a reference dataset of key features, and apply machine learning methods to a large dataset. This work will provide a needed basis for research groups at Duke and elsewhere interested in understanding the path to electrification in underserved areas, and may result in comprehensive maps of electricity access. Partially sponsored by Bass Connections and the Duke University Energy Initiative.

Faculty Leads:

Kyle Bradbury

Leslie Collins

Timothy Johnson

Marc Jeuland

Guillermo Sapiro

Project Manager: Kyle Bradbury

Student Team: Ben Brigman, Gouttham Chandrasekar, Shamikh Hossain, Boning Li, Trishul Nagenalli



Ashlee Valente , Center for Applied Genomics and Precision Medicine


Energy Data Analytics