Towards net zero: development of AI enabled biological observing

迈向净零：人工智能的发展支持生物观测

• 批准号: NE/X010937/1
• 负责人: Kerry Howell
• 金额: $ 10.18万
• 依托单位: Plymouth University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FX010937%2F1
• 关键词: Towards; net; zero; development; AI

Sustainable management of the marine environment is a global concern, perhaps best highlighted by the UN declaration of 2021-2030 as the Decade of Ocean Science for Sustainable Development. Ecological understanding of the non-coastal marine environment has lagged behind the physical, chemical and geological understanding due to challenges in observing and monitoring marine life at depth and in open ocean environments. Modern seafloor survey and monitoring platforms, including Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), and Autonomous Landers, are able to collect an array of spatially and temporally explicit, multi-sensor data, including vast video and / or image datasets, offering either high or large spatially and temporally resolved datasets. While use of these platforms, and their ability to make concurrent visual and environmental observation have already transformed our understanding of marine ecosystems, particularly hard substrate systems like seamounts and hydrothermal vents, the full potential of these autonomous and robotic systems has not yet been realised. One of the greatest challenges to realising that potential lies in overcoming the bottleneck created by the need for manual (human) interpretation of images and video in order to extract quantitative biological data. Recently developments in artificial intelligence and computer vision have offered a potential mechanism to overcome that bottleneck, offering a faster, more consistent, cost effective and shareable alternative to manual annotation. We have established that deep learning (a branch of artificial intelligence) can be used to reliably and quickly count specific species in the right conditions. This capacity needs to be expanded to a wider selection of taxa and pipelines developed that can be applied in-situ, moving us toward a future of AI enabled biological observing. Realising this future is important to reducing the carbon footprint of marine biological research, and helping us achieve our climate change targets. In this project we will investigate the best methods to translate the large volume of data collected by autonomous and robotic systems, into ecological knowledge to then feed into models enabling us to make predictions on how biodiversity is distributed and may change over time. This will drastically improve our perception of the oceans ecology and better inform conservation and management measures.

海洋环境的可持续管理是全球关注的问题，联合国宣布2021-2030年为海洋科学促进可持续发展十年也许是最好的强调。对非沿海海洋环境的生态认识落后于对物理、化学和地质的认识，原因是在观察和监测深海和开阔海洋环境中的海洋生物方面存在挑战。现代海底调查和监测平台，包括自主水下航行器（AUV），遥控潜水器（ROV）和自主登陆器，能够收集一系列空间和时间明确的多传感器数据，包括大量的视频和/或图像数据集，提供高或大的空间和时间分辨率数据集。虽然这些平台的使用及其同时进行视觉和环境观测的能力已经改变了我们对海洋生态系统的理解，特别是像海山和热液喷口这样的硬基底系统，但这些自主和机器人系统的全部潜力尚未实现。实现这一潜力的最大挑战之一在于克服手动（人类）解释图像和视频以提取定量生物数据的需求所造成的瓶颈。最近人工智能和计算机视觉的发展为克服这一瓶颈提供了一种潜在的机制，为手动注释提供了更快、更一致、更具成本效益和可共享的替代方案。我们已经确定，深度学习（人工智能的一个分支）可以在适当的条件下可靠、快速地对特定物种进行计数。这种能力需要扩展到更广泛的分类群和管道，这些分类群和管道可以在原位应用，使我们走向人工智能支持的生物观察的未来。实现这一未来对于减少海洋生物研究的碳足迹和帮助我们实现气候变化目标至关重要。在这个项目中，我们将研究将自主和机器人系统收集的大量数据转化为生态知识的最佳方法，然后将其输入模型，使我们能够预测生物多样性如何分布，并可能随着时间的推移而变化。这将大大改善我们对海洋生态的认识，并更好地为养护和管理措施提供信息。