Mapping Energy Infrastructure

Mobilizing satellite imagery and big data to identify global infrastructure

Mobilizing artificial intelligence tools to understand the energy access playing field

Big data offers big opportunities to understand enduring problems in new ways, and Duke University’s interdisciplinary Energy Data Analytics Lab is at the forefront,  developing methods and tools to evaluate electricity access in developing countries using machine learning techniques applied to aerial imagery data.

Our understanding of the drivers and impacts of electrification on health, land use, the environment and the local economy relies heavily on household surveys or national-level data sources. Collecting and maintaining data on access to energy and the location of energy infrastructure is overly time-consuming.

Beginning with the development of tools to measure electricity access in developing countries through machine learning techniques applied to satellite image data – far beyond the relatively common “Lights at Night” data, and adding the automatic detection of features such as transmission lines, substations, and off-grid generation assets such as solar photovoltaic panels – the project has provided a much-needed base for understanding the path to electrification in underserved areas, as well as its impacts. Moving forward,  faculty and students are building on this work by employing state-of-the-art machine learning tools  to detect critical energy infrastructure in satellite imagery, including power distribution lines and off-grid distributed energy generation sources such as solar photovoltaics. This work will help to deepen understanding of both the drivers and impacts of electricity access and reliability in underserved areas, such as the productive use of power and improved development outcomes.


Duke Staff/Faculty: Kyle Bradbury, Leslie Collins, T. Robert Fetter, Marc Jeuland, Timothy Johnson, Guillermo Sapiro, Jordan Malof, Robyn Meeks

Students: Brian Wong, Wendell Cathcart, Fangge Deng, Shamikh Hossain, Shijia Hu, Prithvir Jhaveri, Ashley Meuser, Harshvardhan Sanghi, Joseph Squillace, Anuj Thakkar, Xiolan You, Ben Alexander, Yutao Gong, Xinchun Hu, Varun Nair, Lin Zuo

Publication: Automating Electricity Access Prediction Using Satellite Imagery, presented at Bass Connections Showcase, April 18, 2018, and Visible Thinking, April 19, 2018

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