Cai, ChangjieLee, JanghoShi, Yingxi RonaZerfas, CamilleGuo, PeiZhang, Zhibo2019-12-202019-12-202019Cai, Changjie; Lee, Jangho; Shi, Yingxi Rona; Zerfas, Camille; Guo, Pei; Zhang, Zhibo; Dust Detection in Satellite Data using Convolutional Neural Networks; http://hpcf-files.umbc.edu/research/papers/CT2019Team5.pdfhttp://hdl.handle.net/11603/16928Research assistant: Pei Guo Faculty mentor: Zhibo ZhangAtmospheric dust is known to cause health ailments and impacts earth’s climate and weather patterns. Due to the many issues atmospheric dust contributes to, it is important to study dust patterns and how it enters the atmosphere. In the past, many scientists have used satellite data and physical-based algorithms to detect and track dust, but these algorithms have many shortcomings. Herein, we consider Convolutional Neural Networks to classify dust in satellite images to try to improve the accuracy of dust detection. We describe the satellite data used, discuss the model structures, and provide results for the models built. These models show promising preliminary results.13 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.atmospheric dusthealth ailmentsdust patternssatellite dataphysical-based algorithmsconvolutional neural networksdust detectionUMBC High Performance Computing Facility (HPCF)Text