Presentation by Martha Morrissey, Development Seed | Many countries lack accurate country-wide maps of energy #infrastructure, which makes key decisions that impact #energyaccess more difficult. This project uses #satellite imagery to map high-voltage infrastructure with machine learning--which it can accomplish 33 times faster per square kilometer than previous methods. With a goal to generate country-wide high-voltage (HV) grid maps quickly and cost-effectively, this work aims to improve #data access that can inform improvements around energy access.

0:00 - 0:48 - Introduction
0:48 - 14:05 - Presentation
14:05 - end - Audience Q&A

About the Presenter: Martha applies machine learning models to detect urban infrastructure like uncommon building types and energy grids faster and more efficiently. Martha is passionate about bringing open geospatial data to academics and innovators as a basis for addressing large-scale global issues. Martha previously worked at Maxar Technologies (formerly DigitalGlobe) where she helped on API development for machine learning training data access, model creation, and model sharing. Martha has a Masters in Geography from the University of Colorado, Boulder.

This presentation was a part of the Energy Data Analytics Symposium hosted by Duke University on December 8-9, 2020. The Symposium’s theme was “Transforming Energy Systems with Data Science Techniques.”

Learn more about the 2020 Energy Data Analytics Symposium and view all presentations:
https://energy.duke.edu/energy-data-a...
All presentations are also available on a YouTube playlist:
   • Energy Data Analytics Symposium  

The 2020 Energy Data Analytics Symposium was organized by the Energy Data Analytics Lab at Duke University and was supported by a grant from the Alfred P. Sloan Foundation. 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.

Learn about the Energy Data Analytics Lab at Duke University: https://energy.duke.edu/research/ener...

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