融合卫星与AIS数据，用多模态模型识别未报告的暗船。
DarkVesselNet: Multi-Modal Remote Sensing and Trajectory Reasoning for Dark Vessel Detection
- 结合雷达、光学影像与航迹推理，构建多模态检测框架。
- 通过差异评分与间隙检测，有效识别未报告的船舶活动。
- 适合海洋监测、非法捕捞追踪等需要暗船识别的应用。
暗船检测需融合船舶通过AIS报告的信息与卫星通过雷达和光学传感器观测的数据。DarkVesselNet是一个多模态遥感系统，整合了Sentinel-1 SAR、Sentinel-2光学影像、地理空间基础模型骨干网络、AIS轨迹推理、类似TGARD的间隙检测方法，以及受Pi-DPM启发的异常头。该系统以可测试的Python包和公开Hugging Face Space形式发布。论文介绍了传感器堆栈、骨干抽象、融合路径、异常头及当前验证结果。现有证据为软件验证：包括SAR斑点滤波、光学波段比值、哈弗辛距离、TGARD间隙发射、传感器配准、骨干网络标记形状及可微分异常评分等测试。
原文摘要 · Abstract (English)
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.
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