3-Minute Thesis: Quarantine Edition
AbstractHigh-resolution
underwater imagery can provide a detailed view of coral reefs and thus
facilitate insight into important ecological metrics of the health and
well-being of the reefs. In recent years, anthropogenic stressors, including
those related to climate change have altered the community composition of coral
reef habitats around the world. Currently the most common method of quantifying
the composition of these communities is through benthic quadrat surveys, which
requires manual annotation of numerous randomly projected points superimposed
on each image. This is a time-consuming task that does not scale well for large
studies. “Deep Learning” continues to show its usefulness in a
variety of fields, and our research investigates the potential for its application
to automatically characterize the contents of coral habitat imagery data. By
re-purposing existing point-based annotation data, trained deep learning models
can be used to classify class categories at the pixel-level and are capable of
generalizing to similar habitats, making it trivial to compute the change in
community composition across space and time. We also showcase how a trained
deep learning model can be used in conjunction with structure-from-motion
photogrammetry to easily quantify the community composition of a reef in 3D.
Jordan Pierce
www.jordanmakesmaps.com
