Towards Scalable Ecological Monitoring: Assessing AI-Based Annotation of Benthic Images

Abstract

Mediterranean rocky reef habitats are ecologically valuable yet increasingly degraded due to cumulative human pressures, necessitating efficient, large-scale ecological status assessments to inform management. Macroalgal communities are widely used as indicators of rocky reef conditions and are typically assessed via photoquadrat sampling. However, the manual annotation of benthic images remains time-consuming and costly. This study evaluates the performance of CoralNet (version 1.0), an AI-assisted image annotation platform, using a pre-annotated dataset of 2537 photoquadrat images from 89 rocky reef sites in the Aegean Sea, Greece, classified into 23 taxonomic and morphofunctional groups. Half of the dataset was used to iteratively train CoralNet classifiers, while the remainder was used to compute the reef-EBQI index and compare ecological status estimates with those derived from manual annotations. The classifier accuracy improved with training volume, reaching 67% using the entire dataset. Reef-EBQI scores derived from CoralNet showed 87% agreement with the manual classifications. Despite challenges and limitations, AI-assisted annotation proved effective in regional-scale ecological assessments based on broad taxonomic and morphofunctional categories. Automated tools like CoralNet can reduce post-processing bottlenecks and enable scalable, cost-effective monitoring, especially when integrated with standardized protocols and citizen science initiatives.

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