Data from: Expert, crowd, students or algorithm: who holds the key to deep-sea imagery ‘big data’ processing?

1. Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, has acquired nearly 65 TB and over 90,000 hours of video data from seafloor cameras and Remotely Operated Vehicles (ROVs). Manual processing of these data is time-consuming and highly labour-intensive, and cannot be comprehensively undertaken by individual researchers. These videos contain valuable information for faunal and environmental monitoring, and are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea. 2. In this study, we compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) Anoplopoma fimbria, in recorded video from a cabled camera platform at 900 m depth in a submarine canyon in the Northeast Pacific. The first group of human observers were untrained volunteers recruited via a crowdsourcing platform and the second were experienced university students, who performed the task in the context of an ichthyology class. Results were validated against counts obtained from a scientific expert. 3. All groups produced relatively accurate results in comparison to the expert and all succeeded in detecting patterns and periodicities in fish abundance data. Trained volunteers displayed the highest accuracy and the algorithm the lowest. 4. As seafloor observatories increase in number around the world, this study demonstrates the value of a hybrid combination of crowdsourcing and computer vision techniques, as a tool to help process large volumes of imagery to support basic research and environmental monitoring. Reciprocally, by engaging large numbers of online participants in deep-sea research, this approach can contribute significantly to ocean literacy and informed citizen input to policy development.1. 近年来的技术进步大幅提升了深海与海洋底栖生境的研究能力，其中多学科海底观测站的发展尤为关键。自2006年起，加拿大海洋网络（Ocean Networks Canada）的缆式海底观测站已通过海底相机与遥控无人潜水器（Remotely Operated Vehicles, ROVs）采集了近65 TB、累计时长超9万小时的视频数据。人工处理此类数据不仅耗时耗力，且单个研究者无法完成全面的标注与分析工作。这些视频蕴含着动物群落与环境监测的宝贵信息，亦是评估深海自然变异性及生态系统对人类活动加剧响应的核心数据来源。2. 本研究针对东北太平洋一处900米水深海底峡谷的缆式相机平台录制视频，对比了三组人类标注者与一种计算机视觉算法对具有商业价值的裸盖鱼（Anoplopoma fimbria，俗称黑鳕鱼、sablefish）的个体计数表现。第一组为通过众包平台招募的未经培训志愿者，第二组为参与鱼类学课程实践的经验丰富大学生；所有标注结果均以一位科学专家的计数结果作为验证基准。3. 相较于专家基准，所有组别均取得了相对准确的计数结果，且均成功识别出鱼类丰度数据中的分布模式与周期性特征。其中经培训的志愿者标注精度最高，计算机视觉算法的精度最低。4. 随着全球海底观测站数量持续增长，本研究证实了众包与计算机视觉技术混合方案的应用价值：该方案可作为处理海量影像数据的工具，为基础研究与环境监测提供支撑。与此同时，通过组织大量线上参与者参与深海研究，该方法还可有效提升公众海洋素养，并为政策制定提供具备科学依据的公民参与意见。