Name: Detecting Solar Farms Using Deep Learning
Start: 2019-04-11T16:45:00-0400
End: 2019-04-11T17:00:00-0400

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Detecting Solar Farms Using Deep Learning

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As environmentally-driven policy has shifted to incentivize renewable energy, large scale solar farms are being installed globally faster than can be reliably tracked by interested stakeholders. Photovoltaic solar arrays, or solar farms, can be clearly distinguished in aerial and satellite imagery, which makes solar farm detection a great candidate for deep learning. We have trained a convolutional neural network using Sentinel-2 imagery to detect the presence and extent of solar farms. As Sentinel-2 satellites have a revisit rate of 5 days on average, we are able to provide information in near real time. We have built an interactive web application that allows users to visualize and chart the growth of large scale solar farms. We will discuss how we prepared our labeled training data, how we trained a convolutional neural network, and show our model results.

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Speakers

Courtney Whalen

Data Scientist, Astraea, Inc.

Courtney Whalen is a data scientist at Astraea, Inc., where she is using satellite imagery and machine learning techniques to answer complex global questions. She has been working as a data scientist for 5 years and has experience developing machine learning models across several... Read More →

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