Deep sea spy: An online citizen science annotation platform for science and ocean literacy

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2025-02-10 DOI:10.1016/j.ecoinf.2025.103065

Marjolaine Matabos , Pierre Cottais , Riwan Leroux , Yannick Cenatiempo , Charlotte Gasne-Destaville , Nicolas Roullet , Jozée Sarrazin , Julie Tourolle , Catherine Borremans

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Abstract

The recent development of deep-sea observatories has enabled the acquisition of high temporal resolution imagery for studying the dynamics of deep-sea communities on hourly to multi-decadal scales. These unprecedented datasets offer valuable insight into the variation of species abundance and biology in relation to changes in environmental conditions. Since 2010, camera systems deployed at hydrothermal vents have acquired over 11 terabytes (TB) of data that cannot be processed by research labs only. Although deep learning offers an alternative to human processing, training algorithms requires large annotated reference datasets. The Deep Sea Spy project allows citizens to contribute to the annotation of pictures acquired with underwater platforms. Based on approximately 4000 photos, each annotated 10 times by independent participants, we were able to develop a data validation workflow that can be applied to similar databases. We compared these annotations with expert-annotated data and analysed the agreement rate among participants for each of the 15,000 annotated individual organisms to optimise the robustness and confidence level in non-expert citizen science. The optimal number of repeat annotations per photo was also analysed to guide the definition of a trade-off between the accuracy and amount of data. An agreement rate of 0.4 (i.e., 4 out of 10 participants detecting one given individual) was established as an efficient threshold to reach counts similar to that obtained from an expert. One important result lies in the robustness of the temporal trends of species abundance as revealed by time-series analyses. Regarding the number of times a photo needs to be annotated, results varied greatly depending on the target species and the difficulty of the associated task. Finally, we present the communication tools and actions deployed during the project and how the platform can serve educational and decision-making purposes. Deep Sea Spy and the proposed workflow have a strong potential to enhance marine environmental observation and monitoring.

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深海间谍：促进科学和海洋知识普及的在线公民科学注释平台

深海观测站的最新发展使我们能够获得高时间分辨率的图像，用于研究深海群落在小时到几十年尺度上的动态。这些前所未有的数据集为了解与环境条件变化有关的物种丰度和生物学变化提供了宝贵的见解。自2010年以来，部署在热液喷口的摄像系统已经获得了超过11tb （TB）的数据，这些数据仅靠研究实验室无法处理。尽管深度学习提供了人类处理的替代方案，但训练算法需要大量带注释的参考数据集。“深海间谍”项目允许公民对水下平台获取的图片进行注释。基于大约4000张照片，每张照片由独立参与者注释10次，我们能够开发一个数据验证工作流，可以应用于类似的数据库。我们将这些注释与专家注释的数据进行了比较，并分析了15,000个注释个体生物中每个参与者的同意率，以优化非专家公民科学的鲁棒性和置信度。还分析了每张照片重复注释的最佳数量，以指导在准确性和数据量之间权衡的定义。一致性率为0.4（即，10个参与者中有4个发现了一个给定的个体）被建立为达到与专家获得的计数相似的有效阈值。一个重要的结果是，通过时间序列分析揭示了物种丰度的时间趋势的稳健性。关于一张照片需要注释的次数，结果因目标物种和相关任务的难度而有很大差异。最后，我们介绍了项目期间部署的通信工具和行动，以及该平台如何服务于教育和决策目的。深海间谍及其工作流程在加强海洋环境观测和监测方面具有很大的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.