Energy Data Analytics Lab: Energy Infrastructure Map of the World through Satellite Data (2018-2019)
Over 15% of humanity has no access to electricity, and far more have unreliable access that precludes most productive energy uses that are beneficial for improving economic prosperity, health and education.
Decision-makers require information to determine the optimal strategies for deploying energy resources to decide where to prioritize development and whether that development should be through grid expansion, microgrids or distributed generation.
However, two critical data sources for such planning—who has access to electricity and the location of electric infrastructure—are often unavailable or overly time-consuming to collect and maintain.
To address these needs, this Bass Connections project sets a bold goal of working toward creating an energy infrastructure map of the world using satellite imagery. The project team will employ state-of-the-art machine learning tools in order to detect critical energy infrastructure in satellite imagery including power distribution lines and off-grid distributed energy generation sources such as solar photovoltaics.
Team members will approach their analysis from an integrated systems perspective. Instead of modeling individual components of the system independently (generation, distribution, consumption), the team will employ techniques that consider these components as part of an integrated whole, comprising a national grid and perhaps local off-grid microgrids. This will be the first step toward a functional energy infrastructure map of the world.
The focus of this team will be in mapping out transmission and distribution lines: the foundation of the bulk energy system. A Data+ team will lay the groundwork for this project by generating a ground-truth dataset for power transmission and distribution infrastructure including power transmission lines, power stations and substations. The Bass Connections 2018-2019 project team will then develop machine learning techniques capable of automatically identifying these objects of interest in satellite data.
Team members will use both publicly available data from the U.S. as well as purchased data covering underserved parts of south and central Asia. This work, combined with the research from previous Bass Connections and Data+ projects, will provide a toolset that will enable the remote assessment of generation, transmission, distribution, end-use and access to electricity. Once all of these pieces are complete, the team will unify these components toward a vision of a methodology for automatically mapping and assessing energy systems in any place on Earth.
New dataset of transmission, distribution and substation data in satellite imagery and publication on the online data repository Figshare; collection of code implementing the algorithm for identifying the power system infrastructure shared on Github; team poster and final presentation open to the public; conference or journal paper based on the methods and/or datasets developed
Fall 2018 – Spring 2019
- Fall 2018: Short course and Kaggle competition (August-October); goal and role development (November); first semester sprint (December)
- Spring 2019: Project execution (January-February; Report out (March-May)
See earlier related team, Energy Data Analytics Lab: Electricity Access in Developing Countries from Aerial Imagery (2017-2018), and a related Data+ summer project, Energy Infrastructure Map of the World (2018).
/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
Jordan Malof, Pratt School of Engineering-Electrical & Computer Engineering
Robyn Meeks, Sanford School of Public Policy
Guillermo Sapiro, Pratt School of Engineering-Electrical & Computer Engineering
/graduate Team Members
Wendell Cathcart, Master of Environmental Management, Energy and Environment
/undergraduate Team Members
Varun Nair, Mechanical Engineering (BSE), Computer Science (BS2)
Lin Zuo, Statistical Science (BS), Computer Science (AB2)