david ouyang moench

Project - Forest Landcover Estimation using Image Segmentation

I took a class on Data Mining in Fall ‘22 as part of my Master’s at Hopkins, and used the final project as a chance to apply machine learning to a climate-change-related problem: Using ‘traditional’ machine learning to estimate the amount of forest coverage shown in a satellite image of a geographic area.

I developed a binary classifier to perform image segmentation of satellite images of the Chesapeake Bay watershed, labeling each pixel as either forest or non-forest landcover, as an application of the support vector machine (SVM) model approach to linear classification.

Image segmentation output figure — A visual comparison between SVM-predicted pixel labels with the true labels.

Here’s a video presentation of the project:

The full write-up and Jupyter Lab code can be found HERE