UAV-Based Deep Learning Workflows for High-Resolution Detection and Mapping of Elkhorn Coral (Acropora palmata)

Elkhorn coral (Acropora palmata) is a threatened reef-building species that plays a critical role in Caribbean coastal ecosystems. Efficient, large-scale monitoring of A. palmata is essential for evaluating restoration success, yet traditional in situ surveys remain costly and spatially constrained. In this study, we acquired high-resolution (1.8 cm) uncrewed aerial vehicle (UAV) imagery of a coral reef within the United States Virgin Islands’ (USVI) St. Croix East End Marine Park (STXEEMP) and applied deep learning object detection to identify individual A. palmata colonies. We utilized two convolutional neural network architectures, FasterRCNN and MaskRCNN. FasterRCNN was used as an initial screening tool to identify the optimal imagery dataset from several candidates. After identifying the dataset, we used MaskRCNN with an iterative annotation refinement procedure in which initial model predictions were used to augment the training data and achieved an F1 score of 0.78. Detection accuracy was strongly influenced by colony size and apparent water depth, with markedly high accuracy for corals wider than 0.3 m (F1 = 0.87) and located in shallower waters (F1 = 0.81). Beyond detection, MaskRCNN’s polygon outputs enabled the measurement of the individual colony area and the generation of high-resolution coral density maps. These products complement broader-scale prediction and mapping approaches and provide fine-scale, management-relevant information. Although this study was conducted at a single reef site during one acquisition period, the results suggest that UAV-based deep learning workflows offer a promising approach for coral reef monitoring that could support restoration assessments and conservation decision-making, pending validation across additional sites, seasons, and environmental conditions.