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This paper presents a methodology for observing and analyzing marine ecosystems using images gathered from autonomous marine vehicles. Visual data is composed in photo-mosaics and classified using machine learning algorithms. The approach expands existing solutions, enabling extended monitoring in time, space, and depth. Imagery was collected during a field campaign in the Spanish marine and terrestrial protected area of Cabrera, Balearic Islands, colonized by the endemic seagrass species Posidonia oceanica (Po). The operations were performed using three distinct platforms, an Autonomous Underwater Vehicle (AUV), an Autonomous Surface Vehicle (ASV) and a Lagrangian Drifter (LD). Results are compared to prior habitat maps to assess seagrass meadow distribution. The proposed solution can be scaled and adapted to other locations and species, considering limitations in data storage and battery endurance. [Display omitted] • Seafloor habitat mapping with autonomous robots, filling gaps missed by divers. • Assessment of marine habitats using convolutional neural networks. • Methods apply to georeferenced imaging (e.g. drones, satellites) beyond subsea mapping. • Field campaign data from a marine area compared to previous habitat map data. [ABSTRACT FROM AUTHOR]