SBIR Phase II: Dynamic OneSource Geospatial Information System
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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.
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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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