Description: Optical imaging is a common technique in ocean research. Diving robots, towed cameras, drop-cameras and TV-guided sampling gear: all produce image data of the underwater environment. Technological advances like 4K cameras, autonomous robots, high-capacity batteries and LED lighting now allow systematic optical monitoring at large spatial scale and shorter time but with increased data volume and velocity. Volume and velocity are further increased by growing fleets and emerging swarms of autonomous vehicles creating big data sets in parallel. This generates a need for automated data processing to harvest maximum information. Systematic data analysis benefits from calibrated, geo-referenced data with clear metadata description, particularly for machine vision and machine learning. Hence, the expensive data acquisition must be documented, data should be curated as soon as possible, backed up and made publicly available. Here, we present a workflow towards sustainable marine image analysis. We describe guidelines for data acquisition, curation and management and apply it to the use case of a multi-terabyte deep-sea data set acquired by an autonomous underwater vehicle.

Description: The latest expedition to the DISCOL experimental area (DEA, www.discol.de) was conducted in 2015 during cruise SO242 with the German RV SONNE. The entire disturbance area was extensively mapped with the ship-mounted Multibeam Echosound (MBES) System. An AUV has been deployed several times in different modes for the acquisition of high resolution MBES, Side Scan Sonar (SSS) and optical data. Even with state-of-the-art positioning technology the remaining navigation uncertainties in the deep sea and the use of a variety of acoustical and optical sensors at different resolutions require detailed post-processing in terms of absolute geographic positioning to improve the overall accuracy of the data. The ship-based bathymetric map of the survey area was used as absolute geographic reference and the AUV-acquired high resolution-data sets available here were geo-referenced and aligned to each other (see the respective assigned PANGEA-links for the different data sets and Gausepohl et al., in review, for details) resulting in the most accurate geo-referenced dataset of the DEA to date. The plough tracks from the disturbance experiment in the DEA in 1989 are visible in all of the acquired high resolution data sets, which enabled digitizing, geo-referencing and age-sequencing of the disturbance tracks (Gausepohl et al., in review). This dataset includes the gridded MBES data from the Kongsberg EM122 mounted in RV SONNE (https://doi.pangaea.de/10.1594/PANGAEA.905579) and the geo-referenced AUV ABYSS-acquired MBES (https://doi.pangaea.de/10.1594/PANGAEA.905580) and SSS (https://doi.pangaea.de/10.1594/PANGAEA.905617) datasets. Furthermore it includes two georeferenced photo mosaics generated from two AUV photo transects (https://doi.pangaea.de/10.1594/PANGAEA.905581). The identified, digitized and georeferenced plough marks are available as GIS-readable shape files here. We further provide the Matrix that was used for the age succession evaluation of the plough marks (Gausepohl et al., in review) in .xlsx and .csv format.