NGA opens BIG-ST BAA Topic 1
On December 15, the National Geospatial-Intelligence Agency (NGA) opened Topic 1 for the Boosting Innovative GEOINT – Science & Technology Broad Agency Announcement (BIG-ST BAA). For Topic 1, Geospatial-Intelligence Foundational Model (GFM), questions are due by 5:00 p.m. Eastern on January 2, and abstracts are due by 5:00 p.m. Eastern on January 23.
The BIG-ST BAA invites proposers to submit innovative concepts to address hard GEOINT problems that align to one or more of the following technical domains:
(1) Foundational GEOINT
(2) Advanced Phenomenologies
(3) Analytic Technologies
The BIG-ST BAA is a general announcement of NGA’s research interest, including criteria for selecting proposals and soliciting the participation of all offerors capable of satisfying the Government’s needs. The requests for abstracts and/or proposals are transmitted via Topic Calls that are published separately under the BIG-ST BAA general solicitation at various times during the open period of the general solicitation. The BIG-ST BAA General Solicitation, HM047623BAA0001, is posted and applies to Topic 1 and forward. Topic 1 – Geospatial-Intelligence Foundational Model (GFM) is active.
Questions Due on 01/02/2024 @ 5:00pm ET
Abstracts Due on 01/23/2024 @ 5:00pm ET
Proposals Due on 03/04/2024 @ 5:00pm ET
This research topic will explore approaches to developing a new type of Multimodal Foundation Model (MFM) to ingest overhead and ground-level imagery, vector data, terrain data, and ground level images, then show adaptability to perform novel, previously unencountered tasks without being retrained. It will answer specific geospatial questions with verifiable precision and accuracy paired with a rigorously determined level of confidence that can be expressed to an analyst, which will communicate the amount of trust that can be placed in the machine generated answer. It will demonstrate how performance scales with the size of the network designed for geospatial information.
This research topic covers GFM, geospatial artificial intelligence (GeoAI), multimodal machine learning, contrastive machine learning, masked autoencoding, unsupervised machine learning, semi- and self-supervised machine learning, fully supervised machine learning, transformer architectures, cross view image matching, neural networks, Vision Transformers, Shifted Windows (SWIN) Transformers, remote sensing, and geographic information system (GIS).
The goal of this research will be to deliver a geographically aware foundational model–or assembly of foundation models–that can form components of a powerful virtual assistant for analysts to geolocate media or objects or answer questions requiring specific geospatial understanding. By the end of the program, the assistant should be able to save analysts significant time in searching for, and within, geospatial sources.
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