  Recent studies have shown evidence of a significant decline of the Posidonia
oceanica (P.O.) meadows on a global scale. The monitoring and mapping of these
meadows are fundamental tools for measuring their status. We present an
approach based on a deep neural network to automatically perform a
high-precision semantic segmentation of P.O. meadows in sea-floor images,
offering several improvements over the state of the art techniques. Our network
demonstrates outstanding performance over diverse test sets, reaching a
precision of 96.57% and an accuracy of 96.81%, surpassing the reliability of
labelling the images manually. Also, the network is implemented in an
Autonomous Underwater Vehicle (AUV), performing an online P.O. segmentation,
which will be used to generate real-time semantic coverage maps.
