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Seagrass meadows serve as critical carbon sinks, but estimating the amount ofcarbon they store requires knowledge of the seagrass species present.Underwater and surface vehicles equipped with machine learning algorithms canhelp to accurately estimate the composition and extent of seagrass meadows atscale. However, previous approaches for seagrass detection and classificationhave required supervision from patch-level labels. In this paper, we reframeseagrass classification as a weakly supervised coarse segmentation problemwhere image-level labels are used during training (25 times fewer labelscompared to patch-level labeling) and patch-level outputs are obtained atinference time. To this end, we introduce SeaFeats, an architecture that usesunsupervised contrastive pre-training and feature similarity, and SeaCLIP, amodel that showcases the effectiveness of large language models as asupervisory signal in domain-specific applications. We demonstrate that anensemble of SeaFeats and SeaCLIP leads to highly robust performance. Our methodoutperforms previous approaches that require patch-level labels on themulti-species 'DeepSeagrass' dataset by 6.8% (absolute) for the class-weightedF1 score, and by 12.1% (absolute) for the seagrass presence/absence F1 score onthe 'Global Wetlands' dataset. We also present two case studies for real-worlddeployment: outlier detection on the Global Wetlands dataset, and applicationof our method on imagery collected by the FloatyBoat autonomous surfacevehicle.

Image Labels Are All You Need for Coarse Seagrass Segmentation