An open-source, scalable workflow for organizing echosounder data for machine learning applications

Echosounders, or high-frequency active sonar systems, have become standard tools for quantifying and mapping the distribution of marine organisms in fisheries or ecological surveys. Conventional echosounder data analysis often relies on human annotation of echograms, which are sonar imagery formed by echo intensity. Over the past decade, in parallel with the exponentially growing volume of echosounder data, there has been a corresponding increase in the development of machine learning (ML) methods that operate primarily on echograms as images. However, echograms are not simply images: they are associated with specific spatiotemporal coordinates that are essential for alignment with survey events, human annotations, and other oceanographic datasets. We present a generalizable two-stage workflow for constructing analysis-ready datasets for ML development tailored for echograms from transect-based surveys, in which (1) acoustic data are partitioned according to transect designation, and (2) masks are created from annotations referencing user-defined uniform spatiotemporal echo data grid. Importantly, ancillary information, such as geospatial coordinates and oceanographic measurements, is propagated across processing stages to preserve the essential contextual information for downstream analyses. We demonstrate the scalability of our workflow implementation based on two open-source software libraries, Echopype and Echoregions, using two example fisheries survey datasets. We additionally provide an executable tutorial that guides readers through the computational implementation of this workflow. Together, these elements provide a scalable and generalizable framework for creating analysis-ready echosounder datasets for ML applications.