Document Type
Poster Session
Publication Date
1-7-2026
Abstract
The Maritime Automatic Identification System (AIS) produces over 100 terabytes of data annually. offering vital insights into globaJ vessel traffic, identity, and navigation patterns. However, the massive volume and inconsislent quality of AIS data introduce challenges for researchers seeking to extract reliable and applicable information. This study investigates the, effectiveness of Julius Al, an off-the-shelf data science platform, for streamlining the analysis of a ilarge AIS dataset, spanning 2015 to 2019 and covering over 42 million messages from Florida's five busiest ports. The results were compared to a previous human analysis. By leveraging only natural language prompts, we explore how AI tools like Julius can automate data ingestion, filtering, and visualization, and assess their capacity to support maritime research while identifying system limitations.
Rights Information
Scholar Commons Citation
Hawkins, Tyler; Meyers, Steven D.; Ingraham, Aishlin; and Luther, Mark E., "AI in Ocean Traffic Monitoring" (2026). Making Waves REU. 7.
https://digitalcommons.usf.edu/making-waves_reu/7
