Artificial Intelligence Lab

Artificial Intelligence Lab

The BAS AI Lab is a cross-disciplinary group of researchers leading in the application of AI and Data Science methods to advance environmental science discovery and lead the development of Digital Twins of the natural environment.

AI methods are now embedded across many areas of BAS science and engineering, including: oceanography; climate science and weather extremes; glacial change and water security; space weather monitoring; and for tracking icebergs and wildlife from space. The BAS AI Lab also lead projects under The Alan Turing Institute’s £40m AI for Science and Government research programme, including Understanding Arctic sea ice loss and Improving tracking of iceberg populations in the Southern Ocean. Through our PhD student projects we also collaborate closely with Google DeepMind, Microsoft, World Wildlife Fund, Max Fordham, Mott MacDonald and the UK Met Office.

We are currently advertising for a Principal Researcher in Environmental AI to lead activities in the BAS AI Lab and support digital innovation at NERC.

Current research activities at the British Antarctic Survey’s AI Lab

PhD opportunities 

Current PhD Student Projects

Machine Learning Workshop 2019
Machine Learning for Environmental Sciences Workshop and Hackathon, BAS, June 2019 (blog post)

Featured reports

  • Developing a Digital Twin Earth, Digital Twinning for the natural environment (e.g., Ocean, Cryosphere, Forests)
  • Application of AI for climate risks, and the prediction of high-impact weather events
  • Intelligent post-processing of climate data, including bias correction and downscaling
  • Simplifying data analysis pipelines for the application of AI, including (a cloud platform for Big Data geoscience)
  • Probabilistic machine learning, providing robust uncertainty estimates for business and environmental policy decision making
  • Combining Little Data and Big Data
    • Scalable data science methods for large N-dimensional spatiotemporal datasets
    • Flexible approaches for irregularly sampled and fragmented datasets,
  • Causal inference: understanding the drivers of environmental change
  • Identification and tracking from Earth Observational data, from icebergs to wildlife


  • Head of the Lab: Scott Hosking
  • Project Management: Elaina Ford, Sarah Berry
  • Steering Group: Rachel Cavanagh, Andrew Fleming, Mervyn Freeman, Scott Hosking, Amie Jackson, Dan Jones, Tracy Moffat-Griffin, Ana Pereira-O’Callaghan, Alex Tate

Co-Supervised and Affiliated Students

Michelle Wan (AI for Environmental Risks, CDT) | Fruzsina Agocs (Kavli Institute for Cosmology, Univ. Cambridge) | Matt Amos (Univ. Lancaster) | Robert Rouse (Future Infrastructure and Built Environment CDT, Univ. Cambridge) | Will Tebbutt (Machine Learning Group, Univ. Cambridge) | Simon Thomas (Univ. Cambridge)

Former Members

Tudor Suciu and Mala Virdee (AI for Environmental Risks, CDT), Shahel Khan (Part III Project, Univ. Cambridge, 2017-2018), Stratis Markou (Internship, Machine Learning Group, Cambridge, 2017), Lille Borresen (Internship, 2018), Daniel Popa-Christobal (Internship, 2018), Harry Holt (paper, Internship, 2017)

AI for Environmental Sciences

21 July, 2019 by Rachel Furner

Rachel Furner is a PhD student at British Antarctic Survey, which has recently opened up its new AI Lab, that aims to foster the application of various machine learning (and …

BAS participates in EGU General Assembly 2020

4 May, 2020

As the world continues to work around lockdown, the EGU General Assembly 2020 will take place this week (4-8 May) online. The annual EGU (European Geosciences Union) meeting, which usually …

PhD centre will nurture new leaders in Earth observation

9 January, 2020

A new centre will enable 50 fully-funded PhD researchers to harness satellite data to tackle global environmental challenges. The Centre for Satellite Data in Environmental Science (SENSE) will bring together expertise in …

Using AI to help tackle global environmental challenges

26 February, 2019

A new Centre for Doctoral Training, involving researchers from British Antarctic Survey, will develop Artificial Intelligence (AI) techniques to address critical environmental challenges. Climate change and environmental hazards pose some …

Watching whales from space

1 November, 2018

Scientists have used detailed high-resolution satellite images provided by Maxar Technologies’ DigitalGlobe, to detect, count and describe four different species of whales. Reported this week in the journal Marine Mammal …

FEATURED PAPER: Temperature change in Sichuan

13 November, 2017

The Sichuan basin is one of the most densely populated regions of China. Along with insufficient arable land and economic underdevelopments, this region is particularly vulnerable to climate-related stresses. Improving …