Biological Evaluations at Scale: The Weddell Sea, Antarctica

大规模生物学评估：南极洲威德尔海

• 批准号: 2115978
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2115978
• 关键词: Biological; Evaluations; Scale; Weddell; Sea

The deep sea is the largest ecosystem on Earth. Nevertheless its marine fauna and communities are at best sparsely explored, surveying considered a major challenge due to accessibility and scale. However such barriers are now beginning to fall as advances in submarine drones widen our research capabilities and bring deep-sea exploration and large-scale marine-life appraisal much closer.Submersible drones, for example autonomous underwater vehicles (AUVs), when fitted with video capture technology are increasingly making possible new studies of both fish and benthic (seabed) communities. They can record life in great depths, in extreme environments and move through vast areas, and offer potential to generate extensive fresh data on marine-dwelling species and their habitats. Such surveying is important to help map biodiversity, communities and establish new ecological baselines. It can also evaluate community change. Such analyses can support oceanic modelling and endorse environmental frameworks, as appropriate. However, although we can now envision extensive image capture, a major "choke" is the current process used to analyse image-data. To date, major delays are common in marine image analysis. This is mainly because contemporary methods are reliant on manual Video Annotation Systems (VASs). To alleviate this bottleneck research is proposed herein to ultimately develop automated approaches to analysis. It is considered that automated approaches to image classification based on machine learning could revolutionise the use of deep-submergence platforms for surveying biodiversity, offering an increase in speed of video / photo analysis of orders of magnitude, massively accelerating deep-sea science. Such approaches would also dramatically improve the prospects of monitoring impacts of human activities such as deep-sea mining over extended time periods.Initially the project intends to focus on seafloor communities with deployment of remotely operated vehicles (ROVs) and AUVs in benthic environs of the "pristine" waters of Antarctica. Study will form part of the Weddell Sea Expedition 2019 investigating the Larsen C ice shelf and iceberg A-68. Megafaunal imagery captured will be used in machine learning exercises harnessing Citizen Science (CS) platform Zooniverse. Over the research period, under project name "Poseidon's Eye", a global forum will be encouraged to identify and characterise marine assemblages according to taxonomic category, anomalous, and additionally (visible) anthropogenic debris. Respective CS is expected to provide crowd-sourcing of image-characterised data-sets for leverage in algorithm generation and synthesis of artificial intelligence (AI) pathways for animal classification. Intention is machine learning will encompass characterised imagery from diverse geographies and research would ultimately provide new "tools of choice" for large-scale and expedient seabed imagery appraisals, with product extensions being envisaged. Please note that sensitivities lie within the project, each step of the proposal being demanding in respect of delivery and potential hurdles arising.

深海是地球上最大的生态系统。尽管如此，其海洋动物和群落充其量也只是很少被探索，由于可及性和规模，调查被认为是一个重大挑战。然而，随着无人潜航器的进步，我们的研究能力得到了拓展，深海探测和大规模海洋生物评估也越来越近，这些障碍正在逐渐消失。无人潜航器，例如自动水下航行器（AUV），在配备视频捕捉技术后，越来越多地使鱼类和底栖生物（海床）群落的新研究成为可能。它们可以记录极深处、极端环境中的生命，并在广阔的区域中移动，并有可能产生关于海洋栖息物种及其栖息地的大量新数据。这种调查对于帮助绘制生物多样性、社区和建立新的生态基线十分重要。它还可以评估社区的变化。这种分析可以支持海洋建模，并酌情认可环境框架。然而，尽管我们现在可以设想广泛的图像捕获，一个主要的“瓶颈”是目前用于分析图像数据的过程。迄今为止，在海洋图像分析中，重大延迟是常见的。这主要是因为当代方法依赖于手动视频注释系统（VAS）。为了缓解这一瓶颈，本文提出了研究，以最终开发自动化的分析方法。据认为，基于机器学习的自动图像分类方法可以彻底改变深潜平台在生物多样性调查中的使用，使视频/照片分析速度提高几个数量级，大大加快深海科学。这种方法还将大大改善长期监测深海采矿等人类活动影响的前景，该项目最初打算侧重于海底群落，在南极洲“原始”水域沃茨的海底环境部署遥控潜水器和自动潜航器。这项研究将成为2019年威德尔海洋探险队的一部分，调查拉森C冰架和冰山A-68。捕获的巨型动物图像将用于利用公民科学（CS）平台Zooniverse的机器学习练习。在研究期间，将鼓励在项目名称“海神之眼”下举办一个全球论坛，根据分类类别、异常碎片和其他（可见）人为碎片，查明和鉴定海洋生物组合。相应的CS预计将提供图像特征数据集的众包，用于动物分类的人工智能（AI）路径的算法生成和合成。机器学习将涵盖来自不同地理位置的特征图像，研究最终将为大规模和方便的海底图像评估提供新的“首选工具”，并设想产品扩展。请注意，项目中存在敏感性，提案的每一步都要求交付和潜在的障碍。