Matthew Campbell - SEFSC (SI lead), Erin Moreland - AFSC, Thomas Oliver - PIFSC, Kresimir Williams - AFSC, Jennifer Fisher - NWFSC, Christian Reiss - SWFSC, Peter Chase - NEFSC, Elizabeth Clarke - NWFSC, Ben Richards - PIFSC, Derek Bolser - S&T
Vision: The Optics Strategic Initiative (OSI) envisions fostering a transformative shift in the National Marine Fisheries Service's (NMFS) approach to optical data collection and processing, reshaping the landscape of resource management and environmental stewardship. We aim to catalyze the transition from traditional ship-based and aerial surveys to platform-agnostic, scalable automated systems, thereby enriching the value and applicability of optics. This journey involves investment in the development of low-cost acquisition hardware, AI-bearing payloads and Uncrewed Systems (UxS), machine learning-assisted image processing, and hybrid cloud processing. Striving to equalize access to these tools across diverse regions, our endeavor is to establish efficient, accessible, and smart optical data acquisition and analysis pipelines, capable of swiftly transforming vast volumes of data into accurate, management-relevant products. In a world grappling with climate change and resource scarcity, the OSI stands at the forefront of NOAA's commitment to a resilient Blue Economy and a Climate Ready Nation.
Background: Optical imagery increasingly serves as the primary data source for many existing NOAA surveys (e.g. Fishery-independent surveys and electronic monitoring) which directly support Natural Resource Management, Sustainable Fisheries, Ecosystem-Based Fishery Management, the New Blue Economy, and a Climate Ready Nation. Optimized optical technology is critical for efforts to move away from traditional ship-based and visual aerial survey methods and towards uncrewed systems and platform agnostic data collections. Recent efforts such as the Advanced Sampling Technology Working Group (ASTWG) catalyzed significant advancements in optical and acoustic sampling platforms while the Automated Image Analysis (AIASI) and the Untrawlable Habitat (UHSI) strategic initiatives made significant progress addressing foundational aspects of using optics and which this Optical SI will build upon. For instance, several developments from the earlier SI’s have become valuable tools for NOAA analysts such as ML/DL based analysis and performance evaluation (e.g. VIAME and CoralNET). While useful in their current state, these tools would benefit from modernization, improved user interfaces, cloud deployment, and continued integration of the constantly evolving set of tools and algorithms developed by the computer science industry. Automation will be critical as the availability of days at sea (DAS) aboard NOAA Fishery Survey Vessels (FSV) is decreasing and recent initiatives to begin collecting data in otherwise inaccessible locations (e.g. wind lease areas) heighten the importance of a transition to platform-agnostic data collections. With increasing application of optical data collection platforms and scalable automated processing solutions, the volume and utility of optical data sets is increasing exponentially. To address this issue several science centers have created transition plans to move automated processes into operations. Realization of end-to-end automation of optical data processing is contingent upon components highlighted in this vision document to be resolved, in particular data management and processing speed. Significant investment is needed to make them efficient, accessible, and relevant for resource management (e.g. stock assessment).
Vision end-point: How will the Optical Strategic Initiative have transformed the optical landscape at the end of 4 years?
OSI envisions a transformative suite of products that enable end-to-end automation of optical sampling from acquisition to analysis. The creation of fully integrated hardware-to-software optical data pipelines, embedded into onboard processing systems of UxS where possible, will meaningfully reshape the NMFS monitoring enterprise, improving efficiency, flexibility, and data quality. We will demonstrate that end-to-end automated approaches to optical sampling can reduce survey days and hands-on processing time, while increasing our temporal coverage and spatial range of data acquisition, diversity of targets, and quality of management-relevant metrics. This transformation will reduce our reliance on white-ships, diversify our portfolio of sampling strategies, and avail monitoring technology to under-served regions.
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