INFERLINK CORP — Department of Defense STTR Phase II: AF19B-T006
INFERLINK CORP — STTR Phase II award from Department of Defense.
Phase II STTR prototype / development signal
- Phase II is where Department of Defense funds deeper R&D after feasibility. Incumbents with Phase II history are serious competitors on adjacent topics.
- Use this award as past-performance context and to map customer organizations for STRATFI/TACFI-style transition planning.
- At $1,800,000, this is a large obligation for typical SBIR Phase sizing — worth reviewing for scope breadth and teaming opportunity.
- Topic code AF19B-T006 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $1,800,000
- Agency
- Department of Defense · Defense Advanced Research Projects Agency
- Program / Phase
- STTR · Phase II
- Topic
- AF19B-T006
- Solicitation
- 19.B
- NAICS
- —
- Place of performance
- CA
- Period
- 2023-09-22 → 2025-03-25
Description
Critical minerals are essential components in many modern technologies used by modern society and the global economy. Finding new sources of critical materials depends on having accurate data regarding the geology of potential sites. The United States Geological Survey (USGS) has an extensive collection of geological maps containing detailed information about various geological features, such as rock formations, faults, folds, and mineral deposits. However, most of these maps exist only as scanned images, requiring significant expert effort to manually convert the information into an analytic-ready format. The objective of this Phase II effort is to research, develop, and evaluate state-of-the-art machine learning algorithms to help automate critical mineral assessments. Specifically, the algorithms will include automated map georeferencing to accurately geolocate maps of unknown locations and automated map feature extraction to identify point, line, and polygon features in geological maps. The expected outcome will be an end-to-end prototype system, called AIM (AI for Maps), for automated georeferencing and feature extraction from scanned geological maps.