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Interpolation -- Applications to image processing, Image processing -- Digital techniques, Digital video, Convolutions (Mathematics) -- Data processing


The greatest source of uncertainty in model estimates of projected climate change involve clouds and aerosols. Photographic images of clouds in the sky are simple to acquire and archive, but climate scientists need an automated process for identifying clouds in these images. We bring machine learning to bear on this problem. Specifically, we use convolutional neural networks, which to our knowledge have not previously been applied to this task. We trained a network to identify clear sky, thin cloud, thick cloud, and non-sky pixels in photos taken by the Total Sky Imager. The trained network is capable of classifying 91.9% of pixels correctly. An ensemble of several networks increases this to 94.6%.


Poster was presented at Poster presented at the 2017 John Rogers Summer Research Conference at Lewis & Clark College and at the Consortium for Computer Sciences in Colleges Northwest Conference 2017 at Washington State University Tri-Cities.

Portland State University’s Coeus computing cluster was used in the research of this poster.

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Mathematics Commons