An open-source, citizen science and machine learning approach to analyse subsea movies

By

Anton, Victor
Germishuys, Jannes
Bergström, Per
Lindegarth, Mats
Obst, Matthias

Type

Article

Date of Publication

2021-02-24

Original Publication

Biodiversity data journal

Volume

9

Pages

1--14

Subjects

Artificial intelligence , autonomous underwater vehicles , Big Data , biodiversity monitoring , Essential Biodiversity Variables , image analysis , marine biodiversity , participatory science , remotely-operated vehicles , research infrastructure

Contributed by

Pensoft Publishers

Language

English

Published by

Sofia Pensoft Publishers [2013]-

Identifiers

DOI: https://doi.org/10.3897/BDJ.9.e60548
eLocator: e60548

 

Abstract

The increasing access to autonomously-operated technologies offer vast opportunities to sample large volumes of biological data. However, these technologies also impose novel demands on ecologists who need to apply tools for data management and processing that are efficient, publicly available and easy to use. Such tools are starting to be developed for a wider community and here we present an approach to combine essential analytical functions for analysing large volumes of image data in marine ecological research.<br> This paper describes the Koster Seafloor Observatory, an open-source approach to analysing large amounts of subsea movie data for marine ecological research. The approach incorporates three distinct modules to: manage and archive the subsea movies, involve citizen scientists to accurately classify the footage and, finally, train and test machine learning algorithms for detection of biological objects. This modular approach is based on open-source code and allows researchers to customise and further develop the presented functionalities to various types of data and questions related to analysis of marine imagery. We tested our approach for monitoring cold water corals in a Marine Protected Area in Sweden using videos from remotely-operated vehicles (ROVs). Our study resulted in a machine learning model with an adequate performance, which was entirely trained with classifications provided by citizen scientists. We illustrate the application of machine learning models for automated inventories and monitoring of cold water corals. Our approach shows how citizen science can be used to effectively extract occurrence and abundance data for key ecological species and habitats from underwater footage. We conclude that the combination of open-source tools, citizen science systems, machine learning and high performance computational resources are key to successfully analyse large amounts of underwater imagery in the future.