NEXTGEN FEDERAL SYSTEMS LLC — Department of Defense SBIR Phase I: AF182-001
NEXTGEN FEDERAL SYSTEMS LLC — SBIR Phase I award from Department of Defense.
Phase I SBIR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Defense in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $49,995. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code AF182-001 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $49,995
- Agency
- Department of Defense · Air Force
- Program / Phase
- SBIR · Phase I
- Topic
- AF182-001
- Solicitation
- 18.2
- NAICS
- —
- Place of performance
- WV
- Period
- 2019-01-07 → 2020-01-07
Description
Soil moisture is an important parameter for AF weather measurements and forecasting. It impacts Army operations (off-road mobility, land operations) and Intelligence Community’s knowledge (agriculture: social unrest). AF has classified soil moisture measurements and data as a DoD space-based environmental monitoring gap. If unmitigated, the lack of soil moisture data will diminish our asymmetric advantage over adversaries. The Weather Company (TWC) ingests and fuses more than 100 terabytes of third-party data daily from more than 800 different data sources like pollen, radar, satellite imagery, traffic, personal weather stations, and agricultural equipment (tractors, sprayers, and soil sensors). NextGen Federal Systems is proposing to leverage TWC’s services including their soil moisture data products, radar, satellite imagery, numerical models, and data fusion techniques combined with IBM’s machine-learning services to construct a global soil moisture data product tailored to meet DoD requirements and mission utility. The effort will establish a supervised machine-learning methodology for imagery classification trained on ground-truth soil moisture measurements from in situ, remote sensed, and model data.