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NOBEL PRIZE 2026

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NSFDirectorate for Technology, Innovation, and Partnerships

SBIR Phase II: Dynamic OneSource Geospatial Information System

Active
Institution
Earth Mapping International, Inc., GA
Principal investigator
Bishwa N Acharya
Funding
$1.3M
Duration
2026–2028

Moonbase score

Still being scored

Abstract

The broader/commercial impact of this Phase II Small Business Innovation Research (SBIR) project lies in reducing technology adoption barriers by transforming historical financial and agricultural records into actionable intelligence. This allows for precision resource management, fostering the resilience and long-term profitability necessary to endure volatile market conditions. Furthermore, the project utilizes a dynamic database-sharing model, ensuring that every data point contributes to continuous algorithmic improvement. Ultimately, this creates a self-supporting cycle of accuracy and value, providing the essential tools for data-driven decision-making. This project integrates terrestrial, airborne, and satellite data to develop a cost-effective, high-fidelity dynamic gridded geospatial database tailored for advanced agricultural modeling. The core objective is to leverage the Online Real-time Global Navigation Satellite System Positioning Service (ORGPS) to engineer a Dynamic OneSource Precise-value-added Geospatial Information System (DOPGIS). By optimizing this system, the research will generate hyper-localized models that empower small-scale producers with unprecedented precision. DOPGIS functions by synthesizing varied static and dynamic geospatial datasets, including high-resolution point and imaging data acquired via airborne and orbital sensors. The system utilizes the ORGPS network for rigorous aerial-satellite triangulation and real-time data quality validation. These inputs are subsequently fused with multi-spectral imaging and meteorological satellite streams to facilitate a near-real-time information service. This project applies advanced geomorphological analysis to capture these nuances at scale. This technological convergence minimizes operational risks and maximizes yields by replacing static assumptions with high-resolution, data-driven intelligence. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Directorate for Technology, Innovation, and Partnerships01002627DB NSF RESEARCH & RELATED ACTIVITSBIR Phase IImarketadvancedsmalltransformingaccuracyengineertechnologyadoptionstaticprofitability

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