Anton Victor, Germishuys Jannes, Bergström Per, Lindegarth Mats, Obst Matthias
Wildlife.ai, New Plymouth, New Zealand Wildlife.ai New Plymouth New Zealand.
Combine AB, Gothenburg, Sweden Combine AB Gothenburg Sweden.
Biodivers Data J. 2021 Feb 24;9:e60548. doi: 10.3897/BDJ.9.e60548. eCollection 2021.
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.
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.
自主运行技术的日益普及为采集大量生物数据提供了巨大机遇。然而，这些技术也对生态学家提出了新的要求，他们需要应用高效、公开可用且易于使用的数据管理和处理工具。此类工具正开始为更广泛的群体开发，在此我们提出一种方法，用于整合海洋生态研究中分析大量图像数据的基本分析功能。
本文描述了科斯特海底观测站，这是一种用于海洋生态研究的分析大量海底视频数据的开源方法。该方法包含三个不同模块，用于：管理和存档海底视频，让公民科学家参与准确分类视频片段，最后训练和测试用于检测生物物体的机器学习算法。这种模块化方法基于开源代码，允许研究人员针对与海洋图像分析相关的各种类型的数据和问题定制并进一步开发所展示的功能。我们使用来自遥控潜水器（ROV）的视频，在瑞典的一个海洋保护区测试了我们监测冷水珊瑚的方法。我们的研究得出了一个性能良好的机器学习模型，该模型完全由公民科学家提供的分类数据进行训练。我们展示了机器学习模型在冷水珊瑚自动清查和监测中的应用。我们的方法展示了如何利用公民科学从水下视频中有效提取关键生态物种和栖息地的出现情况及丰度数据。我们得出结论，开源工具、公民科学系统、机器学习和高性能计算资源的结合是未来成功分析大量水下图像的关键。
