A U-Net to Identify Deforested Areas in Satellite Imagery of the Amazon
Description
Deforestation in the Amazon rainforest has the potential to have devastating effects
on ecosystems on both a local and global scale, making it one of the most environmentally
threatening phenomena occurring today. In order to minimize deforestation in the Ama-
zon and its consequences, it is helpful to analyze its occurrence using machine learning
architectures such as the U-Net. The U-Net is a type of Fully Convolutional Network that
has shown significant capability in performing semantic segmentation. It is built upon
a symmetric series of downsampling and upsampling layers that propagate feature infor-
mation into higher spatial resolutions, allowing for the precise identification of features
on the pixel scale. Such an architecture is well-suited for identifying features in satellite
imagery. In this thesis, we construct and train a U-Net to identify deforested areas in
satellite imagery of the Amazon through semantic segmentation.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2024-05
Agent
- Author (aut): Douglas, Liam
- Co-author: Giel, Joshua
- Thesis director: Espanol, Malena
- Committee member: Cochran, Douglas
- Contributor (ctb): Barrett, The Honors College
- Contributor (ctb): Computer Science and Engineering Program