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.

对海洋环境的可持续管理是一个全球关注的问题，也许最好是联合国2021 - 2030年作为可持续发展的海洋科学十年来强调的。由于观察和监测深度和开放海洋环境中海洋生物的挑战，对非沿海海洋环境的生态理解落后于物理，化学和地质理解。现代海底调查和监测平台，包括自动驾驶水下车辆（AUV），远程操作的车辆（ROV）和自主登陆器，能够收集一系列在空间和时间上显式的多传感器数据，包括大量的和 /或图像数据集，包括高空或大型的空间质量质量质量质量质量质量分辨率。尽管这些平台的使用及其进行同时进行视觉和环境观察的能力已经改变了我们对海洋生态系统的理解，尤其是诸如海拔和水热通风孔（例如，这些自主和机器人系统）的全部潜力，但尚未实现这些全部潜力。意识到潜在的最大挑战之一在于克服需要手动（人类）对图像和视频解释以提取定量生物学数据而产生的瓶颈。最近，人工智能和计算机视觉的发展提供了一种潜在的机制来克服这种瓶颈，提供了一种更快，更一致，更一致，更具成本效益和可共享的手动注释替代方案。我们已经确定，深度学习（人工智能的一个分支）可用于在正确条件下可靠，迅速地计算特定物种。这种能力需要扩展到可以在原位应用的更广泛的分类单元和管道，使我们朝着实现AI的生物学观察的未来发展。意识到这一未来对于减少海洋生物学研究的碳足迹很重要，并帮助我们实现气候变化目标。在该项目中，我们将研究最佳方法，以将自动源和机器人系统收集的大量数据转化为生态知识，然后将其进食，使我们能够对生物多样性的分布方式进行预测，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学的看法，并更好地为保护和管理措施提供信息。