From detection to complexity: AI boulder mapping enables structural analysis of Baltic Sea reefs

Advances in AI-based boulder detection using hydroacoustic data enable detailed characterization of geogenic reefs. As AI-based detection approaches the level of accuracy of human interpretation in small-scale test areas, it opens up the opportunity to efficiently analyze and characterize larger regions. Geogenic hard substrates are a key habitat for diverse benthic communities that provide crucial ecosystem services. Current classification schemes for boulder fields in the German Baltic Sea, using three categories (0 boulders, 1-5 boulders, and &amp;gt;5 boulders) inadequately capture habitat complexity, thus limiting our understanding of these critical geogenic reefs. Convolutional neural networks were used to detect individual boulders on side scan sonar backscatter mosaics with 25 cm resolution across four study sites, covering an area of 306 km 2 in the German Baltic Sea. Region-specific AI models detected about 6.7 times more boulders than previous automated methods. A maximum of 550 boulders per 50 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"> <mml:mrow> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> 50 m grid cell were detected. A novel metric, the Boulder Field Complexity Index (BFCI), was developed to describe the complexity of boulder fields. The BFCI integrates boulder count with the transition of sharpness and the spatial distribution of individual boulders. Compared to conventional approaches, the BFCI enables the characterization of boulder field complexity on a continuous scale and reveals significant complexity differences between study sites. The coastal and shallow study site Plantagenet Ground demonstrates the highest level of complexity, whereas the offshore and deepest study site, Western Rönnebank, exhibits the lowest. The abundance of boulders is negatively correlated with water depth, with the highest densities occurring in shallow waters. The spatial variability in BFCI values reflects the heterogeneous nature of glacial till deposits and differential erosion processes that have shaped boulder field distribution. This approach provides the foundation for linking habitat heterogeneity to benthic community patterns and ecosystem functioning.