Artificial Intelligence Lab

The BAS AI Lab is a cross-disciplinary group of scientists and engineers leading in the development of AI and digital twin technologies to tackle our greatest polar research challenges.

These 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; monitoring and tracking icebergs and wildlife from satellites; and automation for decarbonisation.

The Lab has two overarching objectives:

  • the development of machine learning and data pipelines for understanding and predicting environmental change
  • developing AI algorithms and digital infrastructure for optimising the use of our polar research vessels, our fleet of underwater and aerial vehicles, and Antarctic research bases

The BAS AI Lab leads mulitiple research programmes include some under The Alan Turing Institute’s AI for Science and Government research programme.

BAS AI Lab Themes

PhD opportunities

 

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

Data Scientist

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Ellen Bowler

Researcher in Machine Learning

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James Byrne

IT Research Software Engineer

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George Coombs

Data Analyst

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Penny Clarke

PhD Student Pelagic Ecosystems

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Andrew Fleming

Head of MAGIC

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Maria Fox

Principal Researcher in Environmental AI

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Peter Fretwell

Geographic Information Officer

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Rachel Furner

PhD Student

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Markus Kaiser

Digital Twin Feasibility Visitor

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Samuel Hall

SDA Digital Twin Manager

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Scott Hosking

Environmental Data Scientist

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Dan(i) Jones

Physical Oceanographer (Adjoint Modelling)

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Martin Rogers

Researcher in Machine Learning

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Jonathan Rosser

PhD Student

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Jonathan Smith

Data Analyst

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Kenza Tazi

PhD Student

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Michael Thorne

Computation Bioinformatics

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Risa Ueno

PhD Student

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Rosie Williams

Numerical Modeller for L Sheet

IceNet

IceNet is a probabilistic, deep learning sea ice forecasting system developed by an international team and led by British Antarctic Survey and The Alan Turing Institute [Andersson et al., 2021]. …


AI4EOAccelerator

The AI4EO Accelerator is a collaboration between Φ-Lab of the European Space Agency (ESA) and the UKRI Centre for Doctoral Training (CDT) in the Application of Artificial Intelligence to the …

Wildlife from Space

Many populations of wildlife are remote, inaccessible or difficult to monitor. The advent of sub-metre, Very-High-Resolution (VHR) satellite imagery may enable us study these animals in a much more efficient …



Using AI to track whales from space

4 February, 2021

British Antarctic Survey (BAS) scientists will work with an Artificial Intelligence company after being awarded a contract from the Canadian Space Agency (CSA) to support the protection of an endangered …


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 …







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 …




Quantifying the causes and consequences of variation in satellite‐derived population indices: a case study of emperor penguins

19 April, 2022 by Phil Trathan, Peter Fretwell

Very high-resolution satellite (VHR) imagery is a promising tool for estimating the abundance of wildlife populations, especially in remote regions where traditional surveys are limited by logistical challenges. Emperor penguins…

Read more on Quantifying the causes and consequences of variation in satellite‐derived population indices: a case study of emperor penguins

Untangling local and remote influences in two major petrel habitats in the oligotrophic Southern Ocean

1 November, 2021 by Dan(i) Jones, Eugene Murphy, Richard Phillips

Ocean circulation connects geographically distinct ecosystems across a wide range of spatial and temporal scales via exchanges of physical and biogeochemical properties. Remote oceanographic processes can be especially important for…

Read more on Untangling local and remote influences in two major petrel habitats in the oligotrophic Southern Ocean

Seasonal Arctic sea ice forecasting with probabilistic deep learning

26 August, 2021 by Dan(i) Jones, Emily Shuckburgh, James Byrne, Scott Hosking, Jeremy Wilkinson, Tony Phillips, Tom Andersson

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the…

Read more on Seasonal Arctic sea ice forecasting with probabilistic deep learning

Experimental determination of reflectance spectra of Antarctic krill (Euphausia superba) in the Scotia Sea

1 August, 2021 by Anna Belcher, Geraint Tarling, Gabriele Stowasser, Louise Ireland, Peter Fretwell, Sophie Fielding

Antarctic krill are the dominant metazoan in the Southern Ocean in terms of biomass; however, their wide and patchy distribution means that estimates of their biomass are still uncertain. Most…

Read more on Experimental determination of reflectance spectra of Antarctic krill (Euphausia superba) in the Scotia Sea