Waterlogging Detection in Champaign County with Remote Sensing Imagery and Decision Tree Learning
Wang, Yichen
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https://hdl.handle.net/2142/106025
Description
Title
Waterlogging Detection in Champaign County with Remote Sensing Imagery and Decision Tree Learning
Author(s)
Wang, Yichen
Contributor(s)
Schwing, Alexander
Issue Date
2019-12
Keyword(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.
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