Open-source approach for reproducible substrate mapping using semantic segmentation on recreation-grade side scan sonar datasets

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Cameron Scott Bodine , Daniel David Buscombe , Toby D. Hocking


Knowledge of the variation and distribution of substrates at large spatial extents in aquatic systems, particularly rivers, is severely lacking, impeding species conservation and ecosystem restoration efforts. Air and space-borne remote sensing important for terrestrial and atmospheric measurements are limited in benthic environments due to river stage, turbidity, and canopy cover, requiring direct observation of conditions in the field. Recreation-grade side scan sonar (SSS) instruments, or fishfinders, have demonstrated their unparalleled value as a low-cost scientific instrument capable of rapidly imaging benthic environments due to the ease of deploying and operating the instrument. However, existing methods for generating georeferenced datasets from these instruments, including sonar mosaics and substrate maps, remains a barrier of adoption for wider scientific inquiry due to the high degree of human-intervention and expertise required to generate these datasets. To address this short-coming, we introduced PING-Mapper; an open-source and freely available Python-based software for generating geospatial benthic datasets from popular Humminbird® instruments reproducibly, with minimal intervention from the user. The previously released Version 1.0 of the software provided automated workflows for exporting georeferenced sonar imagery. This study extends functionality with Version 2.0 by incorporating semantic segmentation with deep neural network models to reproducibly map substrates at large spatial extents. We present a novel approach for generating label-ready sonar datasets, creating label-image training sets, and model training with transfer learning; all with readily available open-source tools. The substrate models, achieving overall accuracies of 78%, are integrated into PING-Mapper v2.0, providing an automated workflow to generate map substrate distribution anywhere. Additional workflows enable masking sonar shadows, calculating independent bedpicks, and correcting attenuation effects in the imagery to improve interpretability. This software provides an improved mechanism for generating geospatial benthic datasets from recreation-grade SSS systems, thereby lowering the barrier for inclusion in wider aquatic research.



Analysis, Artificial Intelligence and Robotics, Databases and Information Systems, Environmental Monitoring, Hydrology, Natural Resources and Conservation, Numerical Analysis and Computation, Numerical Analysis and Scientific Computing, Programming Languages and Compilers, Software Engineering, Terrestrial and Aquatic Ecology, Water Resource Management


Side-scan sonar, Substrate mapping, Aquatic habitat, Acoustic remote sensing, Deep learning, Neural Networks, semantic segmentation


Published: 2023-12-21 22:44

Last Updated: 2023-12-22 07:30

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CC BY Attribution 4.0 International

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