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SBIR Phase II: Dynamic OneSource Geospatial Information System

Awarded
220751SBIR / STTR

Contract Overview

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This Phase II Small Business Innovation Research award from the National Science Foundation supports the development of the Dynamic OneSource Precise-value-added Geospatial Information System. The project aims to create a cost-effective, high-fidelity dynamic gridded geospatial database by integrating terrestrial, airborne, and satellite data. By leveraging the Online Real-time Global Navigation Satellite System Positioning Service for precise triangulation and validation, the system synthesizes multi-spectral imaging and meteorological streams to provide near-real-time, hyper-localized agricultural modeling. The primary goal is to reduce technology adoption barriers for small-scale producers by converting historical financial and agricultural records into actionable intelligence. This approach replaces static assumptions with data-driven insights to optimize resource management, minimize operational risks, and increase crop yields. Through a dynamic database-sharing model, the project ensures continuous algorithmic improvement, fostering long-term profitability and resilience against volatile market conditions.

General Info

NSF award for developing a high-fidelity geospatial database for hyper-localized agricultural modeling.

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Organization & Contact Information

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AgencyNational Science Foundation
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OfficeN/A
Organization / AgencyNational Science Foundation
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Full Description

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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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