Energy Data Analytics Lab: Electricity Access in Developing Countries from Aerial Imagery (2017-2018)


Access to reliable electricity is strongly correlated with economic prosperity and health. Over 15% of humanity has no access to it at all, and far more have access only to intermittent supplies that preclude most productive energy uses. According to the World Bank, in some nations like South Sudan, Chad and Burundi, fewer than 7% of people have any access at all.

Research on sustainable energy transitions being conducted at Duke and elsewhere is trying to better understand the drivers and impacts of electrification on health, land use, the environment and the local economy. However, current methods for assessing access rely almost completely on household surveys or highly aggregated (e.g., national-level) data sources.

Project Description

This Bass Connections project will develop means to evaluate electricity access in developing countries through machine learning techniques applied to aerial imagery data. This work will provide the needed basis for research groups at Duke and elsewhere interested in understanding the path to electrification in underserved areas.

The project team will fill critical gaps by piloting and then applying automated algorithms for generating spatially-disaggregated data on electricity access in developing countries using aerial imagery. The team will develop new machine learning techniques to extract information on energy infrastructure and consumption using high-resolution aerial imagery.

In high-resolution aerial imagery, power transmission and distribution lines are often visible to the naked eye, and the poles and pylons supporting them and substations linking them are clearly visible. Utilizing the information from these images, the team will develop estimate network topologies of electric power lines. Additionally, imagery of lights at night can provide indicators of energy consumption.

The initial phase of this work will involve a related Data+ summer research project, which will launch the larger Bass Connections project by compiling, curating and publishing an initial ground-truthed dataset. The Bass Connections team will then use this as the basis of its work.

Anticipated Outcomes

Dataset of fully-identified electricity infrastructure in aerial imagery; published version of the dataset made available on the data repository Figshare; collection of code implementing the algorithm for identifying the power system infrastructure; conference or journal paper based on the methods developed and/or the dataset


Fall 2017 – Spring 2018

  • Fall 2017: Intensive short course on data analytics, energy systems, energy access, research techniques and project management; Kaggle machine learning competition; clearly defined subset of goals
  • Spring 2018: Project execution; reflections and presentations to wider community

Team Outcomes to Date

Automating Electricity Access Prediction Using Satellite Imagery (poster by Shamikh Hossain, Shijia Hu, Prithvir Jhaveri, Harshvardhan Sanghi, Joe Squillace, Brian Wong, Xiaolan You, Kyle Bradbury, Leslie Collins, T. Robert Fetter, Marc Jeuland, Timothy Johnson), presented at Bass Connections Showcase, April 18, 2018, and Visible Thinking, April 19, 2018

Brian Wong (M.E.M. Program), Bass Connections Award for Outstanding Mentorship

Automating Electricity Access Prediction with Satellite Imagery (Fangge Deng, Shamikh Hossain, Prithvir Jhaveri, Ashley Meuser, Harshvardhan Sanghi, Joe Squillace, Anuj Thakkar, Brian Wong, Xiaolan You, Kyle Bradbury, Leslie Collins, T. Robert Fetter, Marc Jeuland, Timothy Johnson; first prize, 2018 Duke Research Computing Symposium Poster Competition, January 22, 2018)

This Team in the News

Duke Expert Touts Transformative Potential of Energy Data Analytics in New Book on Digital Decarbonization

Bass Connections Showcase Presents Research Highlights from Durham to Malaysia

Duke University Team Vying for Hult Prize for Project to Transform Cold Storage in India

Three Graduate Students Honored for Outstanding Bass Connection Project Team Mentorship

Crossing Boundaries to Meet Our Energy Needs

Duke Introduces Interdisciplinary Energy Access Project at D.C. Event

Gauging Renewable Energy Generation Using Satellite Imagery

Energy Data Analytics Lab Team Takes Top Prize at 2018 Duke Research Computing Symposium with Electricity Access Project

New Innovation Space Opens in Gross Hall

Team of Sophomores Wins Hult Prize at Duke, Will Advance to Regional Finals

Mapping Electricity Access for a Sixth of the World’s People

Student Team’s Success in Energy Case Competition Is Powered by the Unique Duke Experience

Duke Undergraduates Use Machine Learning Techniques to Evaluate Electricity Access in Developing Countries

See related teams, Energy Data Analytics Lab: Energy Infrastructure Map of the World through Satellite Data (2018-2019) and Energy Data Analytics Lab (2016-2017), and a Data+ summer project, Electricity Access in Developing Countries from Aerial Imagery.

Kyle Bradbury and team members

/faculty/staff Team Members

  • Kyle Bradbury, Energy Initiative*
  • Leslie Collins, Pratt School of Engineering-Electrical & Computer Engineering*
  • T. Robert Fetter, Nicholas Institute for Environmental Policy Solutions
  • Marc Jeuland, Sanford School of Public Policy
  • Timothy Johnson, Nicholas School of the Environment-Earth and Ocean Sciences*
  • Guillermo Sapiro, Pratt School of Engineering-Electrical & Computer Engineering

/graduate Team Members

  • Brian Wong, Master of Environmental Management, Global Environmental Change

/undergraduate Team Members

  • Fangge Deng, Computer Science (AB), Environmental Sci/Policy (AB2)
  • Shamikh Hossain, Computer Science (BS), Economics (BS2)
  • Shijia Hu
  • Prithvir Jhaveri , Computer Science (BS)
  • Ashley Meuser, Electrical & Computer Egr(BSE), Computer Science (BS2)
  • Harshvardhan Sanghi, Mechanical Engineering (BSE), Economics (AB2)
  • Joseph Squillace, Computer Science (AB)
  • Anuj Thakkar, Mechanical Engineering (BSE)
  • Xiolan You, Computer Science (BS), Statistical Science (BS2)