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Aaron Marburg

Principal Electrical/Computer Engineer






Dr. Marburg's research focuses on the development of robotic platforms for ocean exploration and science, with a focus on perception, situational awareness, and mission planning. He also has a background in remote sensing, photogrammetry and precision navigation, and a strong interest in human-machine interfaces, and data and metadata management. He has over 15 years experience in electrical and software design for robotics, scientific instrumentation and high-performance computing. Dr. Marburg joined APL-UW as a SEED postdoctoral researcher in 2015 after completing his Ph.D. at the University of Canterbury in Christchurch, New Zealand.

Department Affiliation

Ocean Engineering


B.S. Engineering, Swarthmore College, 1998

M.S. Aeronautical & Astronautical Engineering, Stanford University, 2004

Ph.D. Electrical & Computer Engineering, Canterbury University, 2015


2000-present and while at APL-UW

Cloud-accelerated analysis of subsea high-definition camera data

Marburg, A., T.J. Crone, and F. Knuth, "Cloud-accelerated analysis of subsea high-definition camera data," Proc., OCEANS, 18-21 September, Anchorage, AK, 6 pp. (IEEE, 2017).

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18 Sep 2017

The seafloor high-definition camera (CamHD) installed on the Ocean Observatories Initiative (OOI) Cabled Array (CA) provides real-time video of the Mushroom vent at the ASHES hydrothermal field in the Axial Volcano caldera on the Juan de Fuca spreading zone (Figure 1). CamHD performs a pre-programmed 13-minute motion sequence every 3 hours. The video captured during this sequence is stored as a 13GB HD video file in the OOI Cyber-Infrastructure (CI) at Rutgers University. As of July 2017 there are approx. 6700 videos in the CI, all of which are publicly accessible through a conventional HTTP interface. Unfortunately, it is impractical for a researcher (and taxing on the CI bandwidth) to download, store, and process the extent of the video archive for analysis. We describe two elements of our efforts to accelerate CamHD video analysis: a cloud-hosted application which provides a simplified interface for extracting individual frames from CamHD videos in a time- and bandwidth-efficient manner; and a tool for the automatic isolation and identification of video subsets showing a sequence of known camera positions. Automatic identification of these video segments allows rapid and automatic development of e.g., time lapse videos.

Using the OOI cabled array HD camera to explore geophysical and oceanographic problems at Axial Seamount

Crone, T.J., F. Knuth, and A. Marburg, "Using the OOI cabled array HD camera to explore geophysical and oceanographic problems at Axial Seamount," AGU Fall Meeting, 12-16 December, San Francisco, CA, OS41C-1970 (AGU, 2016).

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15 Dec 2016

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.

Automated QA/QC and time series analysis on OOI high-definition video data

Knuth, F., L. Belabassi, L. Garzio, M. Smith, M. Vardaro, and A. Marburg, "Automated QA/QC and time series analysis on OOI high-definition video data," Proc., MTS/IEEE OCEANS Conference, 19-23 September, Monterey, CA, doi:10.1109/OCEANS.2016.7761396 (IEEE, 2016).

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1 Dec 2016

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.

More Publications

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center