暗船检测需融合船舶通过AIS上报的信息与卫星通过雷达和光学传感器观测到的信息。DarkVesselNet是一种多模态遥感技术栈，整合了Sentinel-1 SAR数据、Sentinel-2光学影像、地理空间基础模型主干网络、AIS航迹推理、TGARD风格的间隙检测，以及受Pi-DPM启发的异常检测头。该代码库以经过测试的Python软件包及公开的Hugging Face Space形式发布。论文阐述了其传感器堆栈、主干网络抽象、特征融合路径、异常检测头及当前验证结果。现有证据均为软件实现层面：包括SAR斑点滤波、光学波段比值、Haversine距离计算、TGARD间隙生成、传感器共配准、主干网络token形状、以及可微异常评分等测试。

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.