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.
Project Results: The team gathered electrification ground-truth data for over 36,000 villages in the Indian state of Bihar, and also collected measurements relevant to electricity consumption for those villages including lights at night data and irrigation metrics. They also created an Amazon MTurk tool that crowdsourced the annotation of key electricity indicators (such as power plants and transmission lines) in imagery data.
Project Manager: Boning Li
“The Data+ team created two new datasets that we’ll immediately deploy as a part of our core research efforts and will serve as the basis for an upcoming Bass Connections in Energy project. The outputs will be used towards two new research projects on energy infrastructure and access in developing countries, and will serve as the ground truth data for developing machine learning techniques for identifying energy infrastructure and access. The students were fantastic – hardworking, passionate about their work, and all-around wonderful people to work with.” — Kyle Bradbury, Lecturing Fellow and Managing Director, Duke Energy Data Analytics Lab