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[PAST EVENT] Applied Science Dissertation Defense - Seth Goodman
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- Open to the public
Title: Filling in the Gaps: Applications of Deep Learning, Satellite Imagery, and High Performance Computing for the Estimation and Distribution of Geospatial Data
Abstract: Many regions around the world suffer from a lack of authoritatively-collected data on factors critical to understanding human well-being. This challenges our ability to understand the progress society is making towards reducing poverty, improving lifespans, or otherwise improving livelihoods. A growing body of research is exploring how deep learning algorithms can be used to produce novel estimates of sparse development data, and how access to such data can impact development efforts. This dissertation contributes to this literature in three parts. First, using Landsat 8 satellite imagery and data from the Armed Conflict Location & Event Data Project, convolutional neural networks are trained to predict locations where conflict is likely to result in fatalities for one year. Second, building on the findings in chapter 1, this dissertation explores the potential to extend predictions to a time series using both yearly and six month intervals. Finally, chapter 3 introduces GeoQuery, a dynamic web application which utilizes a High Performance Computing cluster and novel parallel geospatial data processing methods to overcome challenges associated with integrating, and distributing geospatial data within research communities.
Bio: Seth completed his B.S. and M.S. in electrical engineering at Villanova University and began working for AidData, a research lab at William & Mary's Global Research Institute, in 2014. In 2017, Seth took the opportunity to pursue a Ph.D in computational geography under the guidance of Dan Runfola. Seth's work focuses on the use of geospatial data, machine learning, and high performance computing to produce tools and methods to address research questions in the field of international development.
To view Seth's dissertation presentation, join via Zoom. The URL is: https://meet.google.com/zuj-wded-auv