A generalizable deep learning framework for large-scale mapping of seagrass habitats

• DOI: 10.1016/j.ecolind.2025.114349

Seagrass meadows play a vital role in supporting coastal communities by promoting biodiversity, mitigating coastal erosion, and contributing to the local economy. These ecosystems face significant threats, including habitat loss, degradation, and climate change. This has led the United Nations to recognize the urgency of conserving marine ecosystems, highlighting the need for evidence-based conservation strategies and high-quality monitoring methods. However, traditional monitoring approaches are often time-consuming, labor-intensive, and costly, limiting their scalability and effectiveness in large-scale applications. Here, we present a deep learning framework based on convolutional neural networks to identify Posidonia oceanica meadows in the Mediterranean Sea using satellite imagery. We demonstrate the generalization capability and robustness of the model by introducing appropriate metrics that overcome the limitations of current approaches. We show that our model is capable of providing reliable estimates of the distribution of the considered habitats and accurate measures of their extension areas. Our study contributes to the development of a reliable map of the distribution of Posidonia oceanica meadows in the Mediterranean Sea, showcasing the transformative potential of remote sensing and machine learning technologies for marine habitat monitoring.

Available as a bioRxiv preprint: https://doi.org/10.1101/2024.03.21.586047

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