ARGUS – Automated Recognition of Ghost Ships and Underwater Surveillance

Talk
In session Artificial Intelligence/ Machine Learning methods in Earth System Sciences , Sept. 3, 2025, 13:30 – 15:15
Exact timing: 14:00 – 14:15
Room info: Lecture Hall

The protection of critical underwater infrastructure, such as pipelines, data cables, or offshore energy assets, has become an emerging security challenge. Despite its growing importance, maritime infrastructure monitoring remains limited by high costs, insufficient coverage, and fragmented data processing workflows. The ARGUS project addresses these challenges by developing an AI-driven platform to support risk assessment and surveillance at sea.

At its core, ARGUS integrates satellite-based Synthetic Aperture Radar (SAR) imagery, AIS vessel tracking data, and spatial information on critical assets into a unified data management system. A key functionality is detecting so-called "ghost ships" – vessels that deliberately switch off their AIS transponders – using object detection techniques on SAR imagery.

At the same time, we are currently developing methods for underwater anomaly and change detection based on optical imagery. This work is still ongoing and focuses on identifying relevant structural or environmental changes in submerged infrastructure through automated image comparison and temporal analysis.

In this talk, we present the architecture and workflows of the ARGUS system, including our use of deep learning (YOLO-based object detection) in the maritime context. We share insights into the current capabilities and limitations of AI models for maritime surveillance, especially in the context of underwater imaging, data scarcity, and domain-specific challenges.

ARGUS illustrates how tailored AI applications, grounded in operational needs and real-world sensor data, can contribute to digital infrastructure protection and maritime situational awareness.