公开1260张海事图像数据集，支持船舶像素级分割
MariSat: A Maritime Dataset for Instance Segmentation of Objects in Satellite and Aerial Images
- 结合SAM3与后处理滤波实现半自动标注
- 涵盖8类船只，含小型渔船等复杂场景
- 适合做海上实时监测的模型训练与评测
从卫星和航拍图像中实现自动化海事监控需要大规模、精确标注的数据集，但针对实例分割任务的小型船只在复杂港口环境中的数据仍十分稀缺。本文提出MariSat，一个包含1260张覆盖多样港口与沿海场景的航空及卫星图像的新基准数据集，对8类海事物体（帆船、游艇、摩托艇、渔船、邮轮、军舰、拖船、货船）进行像素级标注。数据集通过结合文本提示可调的分割模型SAM 3与一系列几何与色彩后处理滤波器的级联流程生成，并经由CVAT平台完成人工修正与质量控制。文中详述了图像采集方法、标注与校正流程及数据组织方式，并报告了训练、验证与测试集的类别统计。MariSat已用于微调与评估分割与检测模型（SAM 3与YOLO11），实现海上实时监控。本文还提供了两任务的详细定量结果与逐类表现。
原文摘要 · Abstract (English)
Automated maritime surveillance from satellite and aerial imagery requires large, precisely annotated datasets, which remain scarce for the instance-segmentation task, particularly for small vessels in cluttered port environments. We present MariSat, a new benchmark dataset of 1260 aerial and satellite images covering diverse port and coastal scenes, annotated at the pixel level for eight maritime object classes (sailboat, yacht, jet-ski, fishing boat, cruise ship, military vessel, tugboat and cargo ship). The dataset was produced through a semi-automatic annotation pipeline combining the textpromptable segmentation model SAM 3 with a cascade of geometric and colorimetric post-processing filters, followed by a manual correction and quality-control pass performed with the CVAT annotation platform. We describe the image-collection methodology, the annotation and correction process, and the resulting data organization. We also report class-wise statistics for the training, validation, and test splits. MariSat has already been used to fine-tune and benchmark segmentation and detection models (SAM 3 and YOLO11) for real-time maritime monitoring. We report detailed quantitative and per-class results for both tasks. The MariSat dataset is publicly available on GitHub : https://github.com/amirabbes/P2M-Maritime-Segmentation
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