Background 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. New information 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.

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一种分析海底电影的开源、公民科学和机器学习方法

背景 越来越多地使用自主操作的技术为大量生物数据的采样提供了巨大的机会。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、公开且易于使用的数据管理和处理工具。此类工具已开始为更广泛的社区开发，在这里我们提出了一种结合基本分析功能的方法，用于分析海洋生态研究中的大量图像数据。新信息 本文介绍了科斯特海底天文台，这是一种分析大量海底电影数据以进行海洋生态研究的开源方法。该方法包含三个不同的模块：管理和存档海底电影，让公民科学家对镜头进行准确分类，最后训练和测试机器学习算法以检测生物物体。这种模块化方法基于开源代码，允许研究人员针对各种类型的数据和与海洋图像分析相关的问题定制和进一步开发所呈现的功能。我们使用远程操作车辆 (ROV) 的视频测试了我们在瑞典海洋保护区监测冷水珊瑚的方法。我们的研究产生了具有足够性能的机器学习模型，该模型完全接受了公民科学家提供的分类训练。我们说明了机器学习模型在冷水珊瑚自动清点和监测中的应用。我们的方法展示了如何利用公民科学有效地从水下镜头中提取关键生态物种和栖息地的发生和丰度数据。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。