Training a Deep Learning Model for Echogram Semantic Segmentation (oceanstream.io)

This tutorial presents a reproducible deep‑learning pipeline and notebook for semantic segmentation of echograms (sonar backscatter vs. time/depth) to detect fish schools. It stitches together open‑source tools—echopype to convert data to SONAR‑netCDF4/Zarr, xarray+Dask for lazy, out‑of‑core access, echoregions to parse Echoview (.evr) annotations, and a PyTorch stack (segmentation_models_pytorch U‑Net with a ResNet34 ImageNet encoder, PyTorch Lightning, torchmetrics). The workflow loads and interpolates NaN‑filled Sv values, normalizes each frequency channel to [0,1], chunks long echograms into overlapping windows (window size = 1.5× depth) to preserve morphology, and exports image/mask patches as .npy files. Data is split by day (scikit‑learn) to prevent leakage, and a custom SonarDataset applies augmentations and resizes to 512×512. Key technical choices target real deployment issues: U‑Net with skip connections captures elongated, irregular schools; a single‑channel sigmoid output is thresholded for binary masks; Focal Loss (α=0.25, γ=2.0) mitigates extreme background/foreground class imbalance; Adam optimizer (lr=1e‑4), batch_size=4, and Lightning handle training, logging and checkpointing. Evaluation uses precision/recall, F1/Dice, mean IoU and AUROC via torchmetrics. For the AI/ML community this is a practical blueprint showing how vision architectures and loss functions can be adapted to large, noisy geoscience sensor data with scalable I/O and annotation tooling—useful for building robust, production‑ready models on hydroacoustic datasets.