A team of Data+ students, led by researchers in the Energy Initiative's Energy Data Analytics Lab and the Sustainable Energy Transitions Initiative, developed means to evaluate electricity access in developing countries through machine learning techniques applied to aerial imagery data.
Duke Undergraduates Use Machine Learning Techniques to Evaluate Electricity Access in Developing Countries
Related Projects
Boning Li (Masters Electrical and Computer Engineering), Ben Brigman (Electrical and Computer Engineering), Gouttham Chandrasekar (Electrical and Computer Engineering), Shamikh Hossain (Computer Science, Economics), and Trishul Nagenalli (Electrical and Computer Engineering, Computer Science) spent ten weeks creating datasets of electricity access indicators that can be used to train a classifier to detect electrified villages. This coming academic year, a Bass Connections Team will use these datasets to automatically find power plants and map electricity infrastructure.