A national UK programme in AI and digital twins to address the biodiversity and climate crisis
Lead Research Organisation:
The Alan Turing Institute
Department Name: Research
Abstract
The Alan Turing Institute (Turing), in collaboration with national labs, will lead a nationwide effort to develop and launch novel methods in AI and digital twinning technologies that improve the UK's effectiveness in addressing environment and sustainability (E&S) concerns. Turing has identified four long-term E&S missions: 1) automate biodiversity monitoring to enable nature recovery; 2) deliver localised environmental predictions to mitigate the impacts of climate change; 3) optimise infrastructure for sustainable use of natural resources; and 4) model interventions to achieve sustainable cities and regions for a net zero world. The proposed programme will bring together a diverse group of UK leaders and experts in AI, environment, sustainability and policy to refine these missions, develop an ambitious national 5-year roadmap and undertake transformative environmental research and innovation to avert climate and biodiversity catastrophe.
Such complex challenges necessitate a blend of advanced analytical skills working in conjunction with environmental scientists across various sub-disciplines. Here, AI and data science act as the binding agent that unifies these diverse teams, and Turing serves as the central hub for UK-wide inclusive activities, leveraging its past ASG seeded research activities with partners. Specifically, this will involve collaboration with national laboratories to establish and augment our collective understanding, which will include: British Antarctic Survey (BAS), UK Centre for Ecology & Hydrology (UK CEH), Centre for Environment, Fisheries and Aquaculture Science (Cefas), National Oceanography Centre (NOC), UK Met Office, and Rothamsted Research.
The work is separated into the following workstreams (WS):
WS1: E&S Mission Scoping with UK research and practitioner communities
- WS1.1: Deep stakeholder mapping and engagement to develop, refine and launch the E&S Missions.
- WS1.2: Capacity-building to support E&S delivery
- WS1.3: Nurturing a national community of research and best practice
WS2: Scaling-up game-changing environmental research from the Turing's AI for Science and Government programme
- WS2.1: Intelligent fusion of sensor data bridging global and local scales
- WS2.2: Digital Twins of Controlled Environment Agriculture
- WS2.3: Automated shape analysis and tracking within imagery
- WS2.4: Reproducible environmental pipelines for digital twins
- WS2.5: Foster an open international environmental data science community
WS3: Establishing physics informed data-driven national capability in weather prediction
Such complex challenges necessitate a blend of advanced analytical skills working in conjunction with environmental scientists across various sub-disciplines. Here, AI and data science act as the binding agent that unifies these diverse teams, and Turing serves as the central hub for UK-wide inclusive activities, leveraging its past ASG seeded research activities with partners. Specifically, this will involve collaboration with national laboratories to establish and augment our collective understanding, which will include: British Antarctic Survey (BAS), UK Centre for Ecology & Hydrology (UK CEH), Centre for Environment, Fisheries and Aquaculture Science (Cefas), National Oceanography Centre (NOC), UK Met Office, and Rothamsted Research.
The work is separated into the following workstreams (WS):
WS1: E&S Mission Scoping with UK research and practitioner communities
- WS1.1: Deep stakeholder mapping and engagement to develop, refine and launch the E&S Missions.
- WS1.2: Capacity-building to support E&S delivery
- WS1.3: Nurturing a national community of research and best practice
WS2: Scaling-up game-changing environmental research from the Turing's AI for Science and Government programme
- WS2.1: Intelligent fusion of sensor data bridging global and local scales
- WS2.2: Digital Twins of Controlled Environment Agriculture
- WS2.3: Automated shape analysis and tracking within imagery
- WS2.4: Reproducible environmental pipelines for digital twins
- WS2.5: Foster an open international environmental data science community
WS3: Establishing physics informed data-driven national capability in weather prediction
