  Seagrass meadows serve as critical carbon sinks, but estimating the amount of
carbon they store requires knowledge of the seagrass species present.
Underwater and surface vehicles equipped with machine learning algorithms can
help to accurately estimate the composition and extent of seagrass meadows at
scale. However, previous approaches for seagrass detection and classification
have required supervision from patch-level labels. In this paper, we reframe
seagrass classification as a weakly supervised coarse segmentation problem
where image-level labels are used during training (25 times fewer labels
compared to patch-level labeling) and patch-level outputs are obtained at
inference time. To this end, we introduce SeaFeats, an architecture that uses
unsupervised contrastive pre-training and feature similarity, and SeaCLIP, a
model that showcases the effectiveness of large language models as a
supervisory signal in domain-specific applications. We demonstrate that an
ensemble of SeaFeats and SeaCLIP leads to highly robust performance. Our method
outperforms previous approaches that require patch-level labels on the
multi-species 'DeepSeagrass' dataset by 6.8% (absolute) for the class-weighted
F1 score, and by 12.1% (absolute) for the seagrass presence/absence F1 score on
the 'Global Wetlands' dataset. We also present two case studies for real-world
deployment: outlier detection on the Global Wetlands dataset, and application
of our method on imagery collected by the FloatyBoat autonomous surface
vehicle.
