Steller Sea Lion Artificial Intelligence Image Processing and Counting Enhancement Effort
ID: ACQ-AONCASAT01-25-0097Type: Special Notice
Overview

Buyer

COMMERCE, DEPARTMENT OFNATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATIONUS

NAICS

Custom Computer Programming Services (541511)

PSC

IT AND TELECOM - BUSINESS APPLICATION/APPLICATION DEVELOPMENT SOFTWARE AS A SERVICE (DA10)

Original Source

https://sam.gov/opp/51b29f735aa04ca4909bef215d3f5654/view
Timeline
    Description

    The National Oceanic and Atmospheric Administration (NOAA) intends to negotiate a sole-source contract with Kitware, Inc. for the enhancement of artificial intelligence (AI) image processing and counting methods related to Steller sea lion populations in Alaska. The project aims to refine aerial imagery collection processes by improving existing detection and classification models, which are currently time-consuming and inaccurate, often relying on manual counting methods that can take up to four months. This initiative is crucial for effective conservation efforts, as it seeks to streamline monitoring processes and enhance data analysis capabilities for protected marine species. Interested vendors must submit their affirmative responses, demonstrating their understanding and capability to meet the requirements outlined in the Performance Work Statement, by 3:30 P.M. Pacific Standard Time on August 1, 2025, to Jamie Abu Shakrah at Jamie.AbuShakrah@noaa.gov.

    Point(s) of Contact
    JAMIE ABU SHAKRAH
    jamie.abushakrah@noaa.gov
    Files
    Title
    Posted
    ACQ-AONCASAT01-25-0097 PWS.pdf
    The Performance Work Statement (PWS) from NOAA Fisheries outlines a project aimed at enhancing the monitoring of protected Steller sea lion populations in Alaska using artificial intelligence (AI). The Alaska Ecosystems Program seeks to refine its aerial imagery collection processes by improving current image registration and detection models employed for counting sea lions. Existing methods are time-consuming and somewhat inaccurate, involving traditional manual counting that can take up to four months. The project will focus on further developing two key models: the FUSION detection and classification model, which aims to improve accuracy in identifying individual sea lions, and the Suppression image registration model, meant to address overlapping imagery issues. Additionally, the contractor will refine relevant pipelines, scripts, and the DIVE GUI to enhance user interaction and data processing. Key deliverables include quarterly virtual meetings, monthly performance reports, improved tile mosaic capabilities for image sets, model assessments, and virtual training sessions. The overall goal is to streamline the monitoring process, facilitate data analysis, and ensure effective conservation efforts for the Steller sea lion populations while monitoring performance through stringent quality assurance metrics.
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