IUCRC Phase I, Saint Louis University: Center for Accurate Georeferencing of the Environment (CAGE)

About This Grant

Geospatial data has become a cornerstone of modern society, enabling critical applications in navigation, national security, transportation, emergency response, infrastructure management, environmental monitoring, precision agriculture, and more. The rapid proliferation of smartphones, drones, satellites, and connected sensors has resulted in an explosion of geospatial data. However, the true value of these datasets depends on one critical factor: accurate and reliable georeferencing. Without precise spatial positioning, data products become misaligned, leading to flawed analysis, compromised decision-making, and even threats to public safety. The Industry–University Cooperative Research Center (IUCRC) for Accurate Georeferencing of the Environment (CAGE) at Saint Louis University (SLU), in partnership with The Ohio State University (OSU) and Purdue University (PU), aims to tackle this foundational challenge. CAGE seeks to accelerate innovation and enhance the economic competitiveness of the U.S. geospatial industry and its users through industry-driven, convergent, precompetitive research in geospatial technologies. The center unites academic researchers, government agencies, and industry stakeholders to advance next-generation methods for georeferencing, spatial data fusion, quality assurance, and decision support. Through a collaborative, industry-focused model, CAGE ensures that innovations are closely aligned with real-world needs while supporting the development of a highly skilled geospatial workforce. CAGE’s proposed research portfolio is organized into three thrust areas: (1) Georeferencing and Navigation, (2) Geoinformation Extraction Assisted by Artificial Intelligence (AI) and Machine Learning (ML), and (3) Rigorous Quality Assurance and Quality Control Processes, which are built upon the outputs of the first two thrusts. The development of quality assurance and control processes is essential due to the vast amounts of data generated by geospatial sensors. These processes will enable greater technological advancement by ensuring compatibility and managing known variables. To achieve these goals, SLU CAGE leverages world-class computing and AI infrastructure, robotics, and various remote sensing platforms to advance research in GeoAI, precision and quantum agriculture, GPS and Positioning, Navigation and Timing (PNT), GPS alternatives, and food security. These innovations will enable scalable, cost-effective deployment across platforms ranging from satellites and UAVs to mobile phones and wearable sensors. As AI and machine learning become standard tools for extracting insights from geospatial big data, CAGE’s work helps ensure that these insights rest on a solid spatial foundation. CAGE directly serves the national interest by strengthening the geospatial innovation ecosystem, supporting national security, enabling data-driven public services, and training the next generation of geospatial professionals through inclusive outreach and education. 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.