Seagrass meadows of Posidonia oceanica are key coastal habitats in the Mediterranean, yet they remain diffcult and costly to survey frequently with traditional field methods. This thesis develops and evaluates a remote sensing and deep learning workflow that enables routine, spatially explicit mapping of P. oceanica from freely available satellite imagery. The work aligns with the broader aims of the POSEIDON initiative to support European directives through reproducible, non-destructive, georeferenced monitoring of priority marine habitats. The study focuses on the Capo Testa Marine Protected Area in northern Sardinia. Level-2A Sentinel-2 multispectral images from 2015 to 2024 were processed and used to train semantic segmentation models.