Dive into this foundational talk on how #computervision turns images into information. The talk by Dr. Kyle Bradbury introduces common problems in the space: image classification, #segmentation, and #objectdetection. Bradbury explains how computer vision techniques can be applied to remotely sensed data to derive insights about #energy #systems. He also discusses how to get started in this space, noting #tools and #resources that can help you explore further.

0:00 – Intro / what is computer vision?
4:36 – How is computer vision used for energy system applications?
6:52 – Example #1: Solar PV identification
7:38 – Example #2: Building energy estimation
8:25 – Example #3: Large-scale infrastructure and poverty estimation
10:20 – Getting started / computer vision tools

Part of the Foundations for Energy Data Analytics Series. Some videos in the series (like this one!) introduce data science concepts and techniques that are applicable to energy challenges. Others are designed to provide background on energy systems and policy, elucidating contexts that are rich with opportunities for data scientists. (   • Foundations for Energy Data Analytics  )

This talk was originally presented during a workshop of the Energy Data Analytics Ph.D. Student Fellows Program (https://energy.duke.edu/energy-data-a..., organized by the Energy Data Analytics Lab (https://energy.duke.edu/research/ener...) at Duke University. The Fellows Program is funded by a grant from the Alfred P. Sloan Foundation, Grant-G2020-13922. (https://sloan.org)

(Note: Conclusions reached or positions taken by researchers or other grantees represent the views of the grantees themselves and not those of the Alfred P. Sloan Foundation or its trustees, officers, or staff).

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