As part of a five-person undergraduate team representing the University of South Florida, I participated in the MAJIC (Modeling, Analysis, and Geospatial Intelligence Challenge), a national competition among Intelligence Community Centers for Academic Excellence (IC CAE) schools. Our task was to utilize the National Unclassified Data Library (NUDL), which integrates AWS, NGA, and other open-source geospatial data repositories, to assess indicators of economic growth and decline in China.

My focus within the team was on analyzing satellite imagery and open-source data to identify trends in economic activity. I worked with Landsat 8–9 Collection 2, Level 2 USGS imagery, specifically Band 2, to evaluate infrastructure and environmental changes. Additionally, we utilized AI-assisted tools to identify patterns in maritime traffic. I employed OSINT methods to track potential shifts in commercial vehicle movement, which could indicate industrial relocation or supply chain disruptions.

Our workflow combined geospatial intelligence techniques with OSINT validation, producing layered assessments that highlighted patterns of shipping activity, industrial site changes, and possible indicators of slowed growth. Collaboration was key: our team divided research areas but maintained constant integration of findings to create a cohesive final product.

This project strengthened my experience with geospatial analysis, satellite imagery interpretation, and applied OSINT techniques in an intelligence setting. It also sharpened my ability to work in a competitive, team-based environment under time constraints, while contributing actionable insights to a problem set modeled on real-world intelligence priorities.