Port-Agent: Structured Multimodal Reasoning for Port Disruption Analysis Using AIS, Satellite Imagery, and News

Abstract

Textual accounts establish essential context for port disruptions but can bias large language model synthesis before independent observations are examined. We present Port Agent, a remote-sensing agent that aligns Automatic Identification System (AIS) trajectories, Sentinel-2 vessel detections, and documents while retaining acquisition time, footprint, processing, and uncertainty. The evaluation follows the evidence chain from physical observation to agent synthesis across 12 views of 11 U.S. event-or-control groups, 24 quality-screened scenes, and 145,570,911 AIS positions. AIS first resolved distinct operational responses: cargo/tanker occupancy fell by 58.8% during the New York/New Jersey strike, 41.5% during the Baltimore channel restriction, and 44.1% around Hurricane Beryl at Houston, whereas the Seattle cyber case showed no comparable portwide contraction. Sentinel-2 then added spatially explicit evidence through a validated detector, georeferenced AIS–image correspondence, and observations during near-synchronous AIS gaps. Finally, a fixed-backend experiment tested how this evidence affected synthesis. Adding the aligned physical record improved concise review, and applying the evidence gate to that same record produced a further gain, particularly for misleading reports. The complete configuration achieved the highest scores under both report conditions. Relative to concise text-only review, it improved the common-outcome score by 19.24 and 22.16 points and reduced misleading-claim false acceptance from 100.0% to 33.3%. Supporting analyses found no detectable model-interface difference, little change in mean performance across temperatures, and no benefit from a longer prompt. Although the system comparison remains exploratory, the results support an auditable evidence chain in which AIS and satellite observations constrain text-led synthesis.