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.

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.

This project designed innovative techniques to evaluate electricity access in developing countries through machine learning applied to aerial imagery data. The team filled critical gaps in energy access data collection and evaluation by piloting and then applying automated algorithms for generating spatially-disaggregated data on electricity access in developing countries using aerial imagery. The team also developed new machine learning techniques to extract information on energy infrastructure and consumption using high-resolution aerial imagery.

In 2017-2018, the team focused on producing high resolution estimates of electrification rates in the Indian states of Bihar and Uttar Pradesh. The team curated a dataset of electricity access survey data and satellite imagery for all villages in Bihar and extracted features from satellite bands to be input into an automated classifier. The team trained a gradient-boosted decision tree classifier using extracted features such as light at night, population density, and possible agricultural activity, to predict whether 16,389 individual villages in Bihar were either electrified or un-electrified based on ground truth data from the Indian government’s GARV dataset.

The team then then used this algorithm to predict electrification of villages in the neighboring state of Uttar Pradesh.

Timing

Fall 2017 – Spring 2018

Team Outcomes

Rui Wang, Leslie M. Collins, Kyle Bradbury, Jordan M. Malof. “Semisupervised Adversarial Discriminative Domain Adaptation, with Application to Remote Sensing Data.” 2018. Institute of Electrical and Electronics Engineers:1676-1679.

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

Meet the Members of the 2018-19 Student Advisory Council

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 (2017).