3D photogrammetry and deep-learning deliver accurate estimates of epibenthic biomass

Accurate biomass estimates are key to understanding a wide variety of ecological functions. In marine systems, epibenthic biomass estimates have traditionally relied on either destructive/extractive methods which are limited to horizontal soft-sediment environments, or simplistic geometry-based biomass conversions which are unsuitable for more complex morphologies. Consequently, there is a requirement for non-destructive, higher-accuracy methods that can be used in an array of environments, targeting more morphologically diverse taxa, and at ecological relevant scales. We used a combination of 3D photogrammetry, convolutional-neural-network (CNN) automated taxonomic identification, and taxa-specific biovolume:biomass calibrations to test the viability of estimating biomass of three species of morphologically-complex epibenthic taxa from in situ stereo 2D source imagery. Our trained CNN produced accurate and reliable annotations of our target taxa across a wide range of conditions. When ..., Biomass regressions
Biomass regressions for target taxa were conducted using in-situ and ex-situ photogrammetry to estimate biovolume and subsequent weighing using dry- or wet-weight methods, depending upon the taxa. 
Field Biomass Validation
Five underwater transects were conducted using photogrammetric video surveys. On each transect, three 0.5 quadrats were placed and the biovolume of the target taxa was measured from the photogrammetric 3D model. All target taxa within each of the quadrats were subsequently collected and retained for biomass measurements. Validation of field biomass estimates was achieved by comparing the “true” biomass of the target taxa (measured from weighing subsampled quadrats) with that predicted from our biovolume conversions. 

Machine-learning 3D annotation validation
To test the accuracy of the automated model annotation, we compared the biovolume from manually annotated meshes with the biovolume from meshes created using machine-learning annotated dense c..., , # 3D photogrammetry and deep-learning deliver accurate estimates of epibenthic biomass

[https://doi.org/10.5061/dryad.1rn8pk11z](https://doi.org/10.5061/dryad.1rn8pk11z)

A combination of biomass and biovolume data for NE Atlantic Epibenthic Species and a machine-learning code for automated identification of these species.

## Description of the data and file structure

Biomass data is collected in kg or g, dry mass or wet mass. Biovolume is collected using photogrammetric approaches measured in cm^3 or m^3. These data are in Excel format. Data can be used to generate density regressions.

# Sharing/Access information

This data will also be made available as part of a submission to the NERC British Oceanographic Data Centre.

## Code/Software

All training data/code for the machine learning semantic segmentation is contained in a folder called SemanticSegmentation. The training image set is in JPEG format partitioned into train/validate/test sets, and contained in folders with associa...