Biological Evaluations at Scale: The Weddell Sea, Antarctica

深海是地球上最大的生态系统。然而，其海洋动物和群落充其量只是被稀少地探索，由于可及性和规模的原因，调查被认为是一项重大挑战。然而，随着潜艇无人机的进步扩大了我们的研究能力，并使深海勘探和大规模海洋生物评估变得更加接近，这些障碍现在正开始下降。潜水器，例如自主水下机器人(AUV)，当配备视频捕获技术时，越来越多地使对鱼类和海底(海底)群落的新研究成为可能。它们可以记录深海、极端环境中的生命，并在广阔的区域中移动，并提供关于海洋生物物种及其栖息地的大量最新数据。这种调查对帮助绘制生物多样性、社区图和建立新的生态基线很重要。它还可以评估社区变化。这种分析可酌情支持海洋建模和认可环境框架。然而，尽管我们现在可以设想广泛的图像捕获，但一个主要的“瓶颈”是当前用于分析图像数据的过程。到目前为止，在海洋图像分析中，主要的延迟是常见的。这主要是因为当代的方法依赖于手动视频注释系统(VASS)。为了缓解这一瓶颈，本文提出了最终开发自动化分析方法的研究。据认为，基于机器学习的图像自动分类方法可以彻底改变深海平台对生物多样性调查的使用，提高视频/照片数量级分析的速度，大大加速深海科学的发展。这种方法还将极大地改善在较长时间内监测深海采矿等人类活动影响的前景。该项目最初打算以海底社区为重点，在南极洲“原始”水域的海底环境部署遥控潜水器(ROV)和自动潜航器。这项研究将成为2019年韦德尔海探险的一部分，调查拉森C冰架和A-68冰山。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，将鼓励一个名为“海神之眼”项目的全球论坛，根据分类分类、异常和另外(可见的)人为碎片来确定和描述海洋组合。预计各自的CS将提供图像特征数据集的众包，以便在动物分类的人工智能(AI)路径的算法生成和合成中发挥作用。目的是机器学习将涵盖来自不同地理位置的特色化图像，研究最终将为大规模和便利的海底图像评估提供新的“首选工具”，并正在设想产品扩展。请注意，项目内部存在敏感性，提案的每个步骤都对交付和出现的潜在障碍提出了要求。

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.