DarkVesselNet: Multi-Modal Remote Sensing and Trajectory Reasoning for Dark Vessel Detection
For maritime surveillance, this work provides a modular, open-source pipeline for dark vessel detection, but it is an incremental integration of existing methods with no demonstrated performance gains.
DarkVesselNet fuses AIS data with SAR and optical satellite imagery to detect dark vessels (non-reporting ships). The system is validated through software tests for SAR speckle filtering, optical band ratios, and anomaly scoring, but no quantitative detection performance numbers are reported.
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, and current validation. The evidence currently available is software-grounded: tests for SAR speckle filtering, optical band ratios, Haversine distance, TGARD gap emission, sensor coregistration, backbone token shapes, and differentiable anomaly scoring.