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arXiv · 2608.29852

MariSat: A Maritime Dataset for Instance Segmentation of Objects in Satellite and Aerial Images

Amir Abbes·Ines Harrabi·Lucas Justin Yirepoa Kinda·Rim Trabelsi·Adnane Cabani·Fatma Abdelkefi
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Abstract

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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Amir Abbes, Ines Harrabi, Lucas Justin Yirepoa Kinda, Rim Trabelsi, Adnane Cabani, Fatma Abdelkefi. 2026-08-30. MariSat: A Maritime Dataset for Instance Segmentation of Objects in Satellite and Aerial Images. https://arxiv.org/abs/2608.29852

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