Self-Learning Digital Twins for Sustainable Land Management

英国农业和土地利用产生的温室气体排放加剧了全球气候变化。英国致力于到2050年实现温室气体净零排放。自1990年以来，农业和土地利用的温室气体排放量有所下降，但在2020年和2021年，它们又开始上升。英国11%的温室气体排放来自放牧牛羊（7%）和退化的泥炭地（4%）。该研究项目正在开发一种名为“自我学习数字双胞胎”的人工智能算法，用于可持续土地管理。数字孪生应用计算建模、环境测量和人工智能算法，为系统的功能提供新的环境见解。农民和土地管理者可以提出问题，数字孪生兄弟可以回答。简而言之，它是物理环境的数字模型，并根据实时数据进行更新，因此它可以随时反映环境。数字孪生可以帮助农民和环境管理者在减少温室气体排放方面取得更好的成果，最终节省时间和资源。这个自我学习的数字双胞胎从实时卫星图像、现场仪器的温室气体测量和其他数据中学习。随着新数据的出现，它的底层模型会随着时间的推移而改进。该项目将促进可持续的牛羊养殖方式和泥炭地恢复。我们将为人工智能的道德和社会责任应用做好准备，以实现温室气体净零排放。我们工作的一个重要部分是建立一个“净零人工智能实践社区”，将计算机科学家与环境、行为和社会科学研究人员聚集在一起，制定一种共同的方法。我们将纳入数字孪生的社会和伦理维度，包括它们可能对谁有利或对谁不利。

Greenhouse gas emissions from agriculture and land use in the UK contribute to global climate change. The UK is committed to achieving net zero greenhouse gas emissions by 2050. Since 1990, greenhouse gas emissions from agriculture and land use have fallen, but in 2020 and 2021 they started rising again. 11% of UK GHG emissions stem from cattle and sheep grazing (7%) and degraded peatlands (4%).This research project is developing an Artificial Intelligence algorithm called a 'Self-Learning Digital Twin' for sustainable land management. A Digital Twin applies computational modelling, environmental measurements and an Artificial Intelligence algorithm to provide new environmental insights into the functioning of a system. Farmers and land managers can ask questions that the Digital Twin can answer. In a nutshell, it is a digital model of the physical environment and is updated from real-time data, so that it mirrors the environment at all times. Digital Twins can support farmers and environmental managers to achieve better outcomes for their greenhouse gas emission reductions, ultimately saving time and resources.The self-learning digital twin learns from real-time satellite images, greenhouse gas measurements from field instruments and other data. Its underlying model improves over time as new data are becoming available. The project will promote sustainable cattle and sheep farming practices and peatland restoration. We will prepare the ground for an ethical and socially responsible application of artificial intelligence for achieving net zero greenhouse gas emissions. An important part of our work is to build a 'Community of Practice in AI for Net Zero' that brings together computer scientists with environmental, behavioural and social science researchers to develop a common approach. We will incorporate the social and ethical dimensions of digital twins, including who they may benefit or disadvantage.