[
    {
        "id": "osp-14984",
        "type": "article-journal",
        "title": "AI-Based On-Board Maritime Object Detection for Earth Observation Payload Data Reduction on Versal Embedded Hardware",
        "author": [
            {
                "family": "Goudemant",
                "given": "Thomas"
            },
            {
                "family": "Bobey",
                "given": "Aurélien"
            },
            {
                "family": "Hlimi",
                "given": "Omar"
            },
            {
                "family": "Bellizzi",
                "given": "Marjorie"
            }
        ],
        "URL": "https://omanscience.com/en/articles/ai-based-on-board-maritime-object-detection-for-earth-observation-payload-data-reduction-on-versal-embedded-hardware",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "DOI": "10.5281/zenodo.23242463",
        "abstract": "Very-high-resolution Earth-observation satellites acquire more data than they can store and downlink, while in maritime surveillance the vessels cover a tiny fraction of each scene. We study onboard vessel detection as a way to select what is downlinked, which reduces the data according to its content rather than coding every pixel; it is complementary to conventional onboard compression. The work follows three axes. (i) Data and algorithm: a controlled dataset is generated from 68 annotated Maxar scenes with 43 vessel classes, and a YOLOX-S detector is trained on it. (ii) Embedded deployment: the detector is quantized and deployed on the DPU of a Versal VC1902, with a limited loss of detection quality and a processing time of a few seconds per scene. (iii) Data reduction: we propose several downlink modes, from metadata only (box, class and score of each detection) to image crops around vessels, tiles holding detections, or the whole scene with a degraded background, and estimate from the measured detection errors the trade-off each offers between the vessels kept and the volume downlinked. On our dense harbor and coastal scenes, tiles keep 98% of the vessels with 29% of the scene volume, and crops 83% with 3%."
    }
]