DarkVesselNet: Multi-Modal Remote Sensing and Trajectory Reasoning for Dark Vessel Detection
arXiv:2606.00445v1 Announce Type: cross Abstract: Dark vessel detection requires fusing what vessels report through AIS with what satellites observe through radar and optical sensors. DarkVesselNet is a multi-modal remote sensing stack that combines Sentinel-1 SAR, Sentinel-2 optical imagery, geospatial foundation model backbones, AIS trajectory reasoning, TGARD-style gap detection, and a Pi-DPM-inspired anomaly head. The repository exposes the system as a tested Python package and a public Hugging Face Space. The paper presents the sensor stack, backbone abstraction, fusion path, anomaly head
The increasing sophistication of multi-modal AI coupled with the availability of diverse remote sensing data is enabling more effective real-time maritime anomaly detection.
This technology enhances maritime domain awareness, crucial for national security, economic stability, and combating illicit activities at sea by improving the detection of non-cooperative vessels.
The ability to accurately identify 'dark vessels' using a combination of AI, radar, and optical data dramatically reduces blind spots in maritime surveillance, shifting from reactive to more proactive interdiction.
- · Navies
- · Coast Guards
- · Intelligence Agencies
- · Maritime Security Firms
- · Illegal fishing operations
- · Smuggling networks
- · Dark fleet operators
Increased interdiction of vessels involved in illicit maritime activities.
Heightened geopolitical tensions in disputed waters due to improved enforcement capabilities.
The proliferation of counter-detection technologies by actors seeking to evade surveillance.
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Read at arXiv cs.LG