Boosting Innovative GEOINT - Science & Technology Broad Agency Announcement (BIG-ST BAA)
ID: 351404Type: Posted
Overview

Buyer

National Geospatial-Intelligence Agency (DOD-NGIA)

Eligible Applicants

Others

Funding Category

Science and Technology and other Research and Development

Funding Instrument

Procurement Contract

Opportunity Category

Discretionary

Cost Sharing or Matching Requirement

Yes

Original Source

https://grants.gov/search-results-detail/351404
Timeline
    Description

    The National Geospatial-Intelligence Agency (NGA) is inviting proposals for the Boosting Innovative GEOINT - Science & Technology Broad Agency Announcement (BIG-ST BAA), aimed at addressing complex geospatial intelligence (GEOINT) challenges through innovative concepts. Proposals should align with one or more of the specified technical domains, including Foundational GEOINT, Advanced Phenomenologies, and Analytic Technologies, with the intent to enhance the capabilities and practices within the GEOINT community. The BIG-ST BAA is an ongoing solicitation, with specific Topic Calls published periodically, the first of which is currently under review, while upcoming topics focus on academic grants and advanced signal chain development. Interested parties can submit proposals in response to Topic Calls, with the next deadlines for submissions on June 7, 2024, and July 9, 2024, and should direct inquiries to BIGSTBAA@nga.mil for further information.

    Point(s) of Contact
    BIGSTBAA@nga.mil
    Files
    Title
    Posted
    BIGST BAA_T02_MUSE.pdf
    The document is a solicitation for the Magnifying University Science Expertise (MUSE) program, which aims to increase knowledge and research in the fields of Synthetic Aperture Radar (SAR) and Geodesy. The program plans to make 1-3 awards per subject area and has a budget range of up to $500k per grant per year. Proposals must address specific technical requirements and include metrics and deliverables.
    BIGST_BAA_Topic 01_GFM_Rev1_20240124.docx
    The document provides details of a research opportunity focused on developing a geospatial intelligence foundational model. The project aims to create a trained neural network model capable of accurately answering geospatial questions and providing precise and high-confidence responses. The project will be executed in four phases: proof of concept, optimization, component integration, and relevant environment testing. The document also includes information on security considerations, deliverables, and performance metrics.
    BIGST_BAA_Topic.bin
    BIGST.bin
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