MacKinnon, JamesAmes, TroyMandl, DanIchoku, CharlesEllison, LukeManning, JacobSosis, Baram2024-09-242024-09-242017-08-29MacKinnon, James, Troy Ames, Dan Mandl, Charles Ichoku, Luke Ellison, Jacob Manning, and Baram Sosis. “Classification of Wildfires from MODIS Data Using Neural Networks.” August 29, 2017. https://ntrs.nasa.gov/citations/20180004230.http://hdl.handle.net/11603/36303Machine Learning Workshop, Mountain View, CA, USA, August 29, 2017Wildfires are destructive to both life and property, which necessitates an approach to quickly and autonomously detect these events from orbital observatories. This talk will introduce a neural network based approach for classifying wildfires in MODIS multispectral data, and will show how it could be applied to a constellation of low-cost CubeSats. The approach combines training a deep neural network on the ground using high performance consumer GPUs, with a highly optimized inference system running on a flight-proven embedded processor. Normally neural networks execute on hardware orders of magnitude more powerful than anything found in a space-based computer, therefore the inference system is designed to be performance even on the most modest of platforms. This implementation is able to be significantly more accurate than previous neural network implementations, while also approaching the accuracy of the state-of-the-art MODFIRE data products.20 pagesen-USThis is a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.Public Domainhttps://creativecommons.org/publicdomain/mark/1.0/Computer Programming And SoftwareText