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Title:Waterlogging Detection in Champaign County with Remote Sensing Imagery and Decision Tree Learning
Author(s):Wang, Yichen
Contributor(s):Schwing, Alexander
Degree:B.S. (bachelor's)
Subject(s):waterlogging detection
remote sensing
decision tree
Abstract:Flooding has become the leading unresolved factor for maize yield loss. Extreme rainfall can cause large and separate waterlogging areas in crop fields, which makes loss prediction difficult. Current waterlogging detection projects usually apply traditional statistical models on public satellite imagery or drone imagery, which usually overlook the lack of resolution or scalability. In this research, we will solve these problems with high-resolution and wide-availability satellite imagery and decision tree learning models. 3-meter resolution PlanetScope CubeSat imagery is used in this research project. As no labels attached to this dataset, our team hand-labeled over two hundred satellite images and converted them to pixel labels. Then, decision tree models and random forest models are trained using these labels. We apply trained models to create pixel-by-pixel waterlogging maps in 2019 Champaign County, and finally, achieved above 94% accuracy.
Issue Date:2019-12
Genre:Dissertation / Thesis
Date Available in IDEALS:2020-01-09

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