Presenter: Jacob Freeman (Mississippi State University)

Description:
The development of advanced remotely operated vehicles (ROVs) has enabled extensive research in previously inaccessible deep-sea environments and resulted in the generation of vast quantities of publicly archived ROV video data. These video data are an immensely valuable oceanographic resource. However, for scientists not directly involved in their collection, the amount time and effort required to review tens to hundreds of hours of video in order to determine its potential value to their research is often prohibitive. Accordingly, this project developed an automated system to digitally map ROV video data, based upon the widely adopted Coastal and Marine Ecological Classification Standard (CMECS), in order to enhance accessibility and utilization of ROV video data by the broader oceanographic research community. Specifically, a series of open-source Python scripts, and associated standard operating procedure documents, were created to automate the generation of color-coded digital maps of seafloor substrate observed in video data acquired during dives of the ROV Deep Discoverer aboard NOAA Ship Okeanos Explorer (EX1803, EX1806, and EX1903L2). The source of the data used were substrate composition annotations created in Ocean Networks Canada’s (ONC) Seatube v2 software by scientists participating in NOAA Ship Okeanos Explorer expeditions, as well as navigation data and environmental sensor observations from the NOAA ROV Deep Discoverer. The resulting maps enable end users to rapidly asess observations made during an ROV dive and visualize geospatial relationships between observed features.  All scripts generated for this project are designed to work with open source QGIS software and have been published on public code repositories in order to support the broadest possible adoption and application.