The deep learning revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. These new methods provide analysis of data from sensors, cameras, and acoustic recorders, even in real time, in ways that are reproducible and rapid. Off-the-shelf algorithms can find, count, and classify species from digital images or video and detect cryptic patterns in noisy data. Using these opportunities requires collaboration across ecological and data science disciplines, which can be challenging to initiate. To facilitate these collaborations and promote the use of deep learning towards ecosystem-based management of the sea, this paper aims to bridge the gap between marine ecologists and computer scientists. We provide insight into popular deep learning approaches for ecological data analysis in plain language, focusing on the techniques of supervised learning with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of deep learning to marine ecology. We use established and future-looking case studies on plankton, fishes, marine mammals, pollution, and nutrient cycling that involve object detection, classification, tracking, and segmentation of visualized data. We conclude with a broad outlook of the field's opportunities and challenges, including potential technological advances and issues with managing complex data sets.
翻译:深海学习革命正在触及我们生活中的所有科学学科和角落,以此作为利用海量数据的力量的手段。海洋生态学也不例外。这些新方法以可复制和快速的方式,对传感器、照相机和声学记录器的数据进行分析,甚至实时地以可复制和快速的方式进行分析。现成算法可以从数字图像或视频中找到、计数和分类物种,并探测噪音数据中的隐秘模式。利用这些机会需要跨生态和数据科学学科的合作,而开展这些研究可能具有挑战性。为了促进这些合作,促进利用深层次的学习来进行基于生态系统的海洋管理,本文旨在弥合海洋生态学家和计算机科学家之间的差距。我们深入了解普通语言的生态数据分析的流行深层学习方法,侧重于与深层神经网络的监督下学习技术,通过对海洋生态的深层学习的既定和新兴应用来说明挑战和机遇和机会。我们利用关于浮游生物、鱼类、海洋哺乳动物、污染和营养学的既定和前瞻性案例研究,这涉及对物体的探测、分类、追踪和分解的复杂技术进展的挑战。我们以广泛的领域、包括各种数据管理机会结束。