Earth Science Information Partners

Mentors: Student: Abhishek Singh

<p>Passive acoustic observation of whales is an increasingly important tool for whale research. Accurately detecting whale sounds and correctly classifying them into corresponding whale pods are essential tasks, especially in the case when two or more species of whales vocalize in the same observed area. Most of the current tasks of whale sound detection and classification still need to be implemented manually.</p> <p>We aim to develop two deep learning models for the detection and pod-classification of orca, or killer whale, calls in unknown long audio samples. These deep neural networks will help identify and verify killer whale calls so that researchers, grad students, and shipping vessels don't have to. The end-user interface can be made as a web-app which can easily be used by scientists in their research.</p>

Mentors: Student: Harman Deep Singh

<p>This project aims to create a Graphical User Interface (GUI) for big gridded geospatial data visualization in the browser interface backed by the full power of the Python ecosystem. This GUI would allow controlled data points selection, massive rendering, data display, custom interaction, selection of fields for plotting and layout of widgets in the browser using Intake, Xarray and Pyviz collection of tools. Currently majority of geospatial data exploration happens in stand-alone applications like Panoply and NcView, tools that have limited functionality, do not provide complex analysis methods and can only be reasonably extended by the software developers on those projects. This new tool, written in Python, but presented as a dashboard in the notebook environment, will be extendable directly by researchers by using it in conjunction with tools like Dask and will also provide complex analysis methods on the data being visualized. It holds the promise of saving Earth Science and other researchers significant amounts of time since they can directly focus on visual data analysis and research rather than writing custom code to explore data.</p>