AI-Based On-Board Maritime Object Detection for Earth Observation Payload Data Reduction on Versal Embedded Hardware

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%.

8 pages. Accepted at the 10th On-Board Payload Data Compression Workshop (OBPDC 2026), Barcelona, Spain, 12-14 October 2026

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

I.4.8; I.2.10; C.3

arXiv:2610.12182 [cs.CV]

(or arXiv:2610.12182v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.12182 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.5281/zenodo.23242463 Focus to learn more DOI(s) linking to related resources

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