A broad array of Earth science problems can be investigated using high-definition video imagery from the seafloor, ranging from those that are geological and geophysical in nature, to those that are biological and water-column related. A high-definition video camera was installed as part of the Ocean Observatory Initiative's core instrument suite on the Cabled Array, a real-time fiber optic data and power system that stretches from the Oregon Coast to Axial Seamount on the Juan de Fuca Ridge. This camera runs a 14-minute pan-tilt-zoom routine 8 times per day, focusing on locations of scientific interest on and near the Mushroom vent in the ASHES hydrothermal field inside the Axial caldera. The system produces 13 GB of lossless HD video every 3 hours, and at the time of this writing it has generated 2100 recordings totaling 28.5 TB since it began streaming data into the OOI archive in August of 2015.

Because of the large size of this dataset, downloading the entirety of the video for long timescale investigations is not practical. We are developing a set of user-side tools for downloading single frames and frame ranges from the OOI HD camera raw data archive to aid users interested in using these data for their research. We use these tools to download about one year's worth of partial frame sets to investigate several questions regarding the hydrothermal system at ASHES, including the variability of bacterial "floc" in the water-column, and changes in high temperature fluid fluxes using optical flow techniques. We show that while these user-side tools can facilitate rudimentary scientific investigations using the HD camera data, a server-side computing environment that allows users to explore this dataset without downloading any raw video will be required for more advanced investigations to flourish.

The Ocean Observatories Initiative's (OOI) Cabled Array (CA) is delivering high-definition video data since August 2015 via fiber optic cable from a statically positioned SubC 1Cam HD (CAMHD) video camera, deployed at the Mushroom hydrothermal vent in the Axial Seamount Hydrothermal Expeditions (ASHES) Field off the coast of Oregon (lat 45° 56.0186'N, long 130° 00.8185'W, depth 1,542 m). Over 20 TB of video data have been archived and are publically available via the OOI raw data repository. The CAMHD runs a 14-minute pan/tilt/zoom routine at eight even intervals throughout the day, producing 13GB of uncompressed HD video each time, with a focus on locations of scientific interest across the vent. Due to the amount of video data already collected, and the anticipated data volumes over the life of the project, automating analyses on the quality and consistency of these data, as well as developing tools for the automatic generation of value-added data products, is critical. In this paper we present results from automated analysis of CAMHD video data files for quality assurance purposes. Objectives include ensuring consistent file size, duration and naming convention on the archive, as well as producing time-series on frames of interest to analyze change in content or image quality over time. For example, we identified that an issue in the video streaming software, which has since been resolved, truncated ~25% of existing mp4 files on the archive. Analyses such as this allow scientists to rapidly understand the structure and quality of video data on the archive, laying the groundwork to create an array of customized analysis routines that meet a range of scientific needs.

The most scientifically diverse and technologically advanced component of the National Science Foundations' $386M investment in the Ocean Observatories Initiative (OOI), involves 900 kilometers of high power and bandwidth electro-optical cable extending from Pacific City, OR, across active portions of the Juan de Fuca tectonic plate and up into the overlying ocean. Completed on time and under budget in October, 2014, this mesoscale fiber-optic sensor array enables real-time, high-bandwidth, 2-way communication with seafloor and water-column sensor networks across: 1) a portion of the global Mid-Ocean Ridge (MOR), 2) a section of the Cascadia Subduction Zone, and, 3) a cross-section of the California Current, a component of the North Pacific Gyre. Much of the data generated from >130 fiber-linked instruments has become available for scientific, educational and public user communities over the past 6 to 12 months, via the OOI Cyber-infrastructure (http://oceanobservatories.org/data-portal/). Since the OOI Cabled System has been in use and streaming live data to shore, two major developments have emerged that bear on undersea volcano-hydrothermal systems: 1) The 2015 submarine eruption of Axial Seamount was documented in a unique fashion by 20 remote, hardwired instruments distributed across the floor of the summit caldera. 2) Live, streaming video of an active hydrothermal system within one of the vent fields inside the caldera reveals subtle changes taking place in the Axial system.

This paper describes the application of convolutional neural networks (CNNs) to the identification and classification of ten classes of benthic macrofauna in high-resolution photomosaics captured on the Pacific continental shelf by an ROV. Each photomosaic was previously hand-annotated with the location and classification of each animal, providing a training set for the machine learning algorithms. These annotations are used to extract image patches around each contact, resulting in approximately 5000 image samples, which are supplemented with randomly selected image patches representing the background. The resulting corpus of data is used to train a series of convolutional neural networks in the Nvidia DIGITS and Google Tensorflow environments. Due to the relatively sparse nature of the training data set, a number of data augmentation approaches are used to increase the diversity of training data. The performance of the resulting algorithm is evaluated in three problem scenarios: first, classification of fauna in an image patch known to contain a target; second, classification of a given image patch as either background or non-background; and third, a single-pass combination of the two problems. The presented networks prove highly accurate at background/non-background segmentation with ~96% accuracy. Fauna identification is less reliable at ~89% accuracy, and unified segmentation and identification proves to be the most challenging at ~88% accuracy.

The introduction of low-cost RGB-depth (RGB-D) sensors have led to a diversity of algorithms for robust 3D scene reconstruction under controlled settings, but the underwater realization of such algorithms has been hampered by the constrained performance of most RGB-D sensors in water. We explore the possibility of fusing a point cloud generated from a high-frequency, mechanically scanned 3D imaging sonar with visual data from a camera to create a rich 3D representation of objects in the water column. A state-of-the-art algorithm for depth sensor-to-camera registration utilizing concurrent images of spherical targets is adapted, and the resulting alignment is used to combine sonar and visual imagery.

This paper describes Smartpig, an algorithm for the iterative mosaicking of images of a planar surface using a unique parameterization which decomposes inter-image projective warps into camera intrinsics, fronto-parallel projections, and inter-image similarities. The constraints resulting from the inter-image alignments within an image set are stored in an undirected graph structure allowing efficient optimization of image projections on the plane. Camera pose is also directly recoverable from the graph, making Smartpig a feasible solution to the problem of simultaneous location and mapping (SLAM). Smartpig is demonstrated on a set of 144 high resolution aerial images and evaluated with a number of metrics against ground control.