Automated benthic image annotation is crucial to efficiently monitor and protect coral reefs against climate change. Current machine learning approaches fail to capture the hierarchical nature of benthic organisms covering reef substrata, i.e., coral taxonomic levels and health condition. To address this limitation, we propose to annotate benthic images using hierarchical classification. Experiments on a custom dataset from a Northeast Brazilian coral reef show that our approach outperforms flat classifiers, improving both F1 and hierarchical F1 scores by approximately 2\% across varying amounts of training data. In addition, this hierarchical method aligns more closely with ecological objectives.

翻译：自动底栖图像标注对于高效监测和保护珊瑚礁以应对气候变化至关重要。当前的机器学习方法未能捕捉覆盖礁石基质的底栖生物层次结构，即珊瑚分类学层级与健康状况。为克服这一局限，我们提出采用层次分类方法进行底栖图像标注。在巴西东北部珊瑚礁定制数据集上的实验表明，该方法优于平面分类器，在不同训练数据量下将F1分数与层次F1分数均提升约2%。此外，这种层次分类方法更契合生态学研究目标。