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.
PhD opportunities
- Centre for Doctoral Training (CDT) in the Application of Artificial Intelligence to the study of Environmental Risks, University of Cambridge and British Antarctic Survey
AI and Digital Twinning for Decarbonisation
AI systems will provide automated decision support to SDA masters in delivering optimal fuel economy and minimising carbon emissions.
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 …
Space technology and artificial intelligence to monitor whale mass stranding events
18 November, 2021
An international team of scientists led by British Antarctic Survey have published research today on using new technology to study mass stranding of whales from space and how the technology …
Artificial intelligence to help predict Arctic sea ice loss
26 August, 2021
A new AI (artificial intelligence) tool is set to enable scientists to more accurately forecast Arctic sea ice conditions months into the future. The improved predictions could underpin new early-warning …
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 …
BLOG: Predicting September 2021 Arctic sea ice using artificial intelligence
24 September, 2021 by Tom Andersson
Tom Andersson, a data scientist at British Antarctic Survey Artificial Intelligence (AI) Lab, shares the latest predictions from a new Arctic sea ice forecasting AI tool as this year’s Arctic …
Innovative Research Sprints Tackle Challenges in Biodiversity and Exposure
11 May, 2021 by Anita Faul
Over the last decade, digital technologies, including Artificial Intelligence, developed rapidly as did our capability to monitor our home planet from space with Earth Observation satellites. How can we most …
Earth Day 2020: A new age of Arctic science discovery – the AI way
24 April, 2020 by Scott Hosking
When we see news reports on climate change on our TV, they are often accompanied by footage of a polar bear walking over the icy Arctic landscape. But the Arctic …
Official launch of the BAS AI Lab!
4 December, 2019 by Risa Ueno
Today we officially launched the BAS AI Lab – thank you everyone for coming!
AI Lab presents at the First Artificial Intelligence for Copernicus Workshop
22 November, 2019 by Anita Faul
Andrew Fleming and Anita Faul present their research at the First Artificial Intelligence for Copernicus Workshop
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 …
Data Study Group: Automated monitoring of seals via high-resolution satellite imagery
8 April, 2019 by Premdeep Gill
Prem hosted at Data Study Group at the Alan Turing Institute Seals from space: automated Antarctic ecosystem monitoring via high-resolution satellite imagery Antarctic seal populations are potential indicators for the …
Automated clustering of storm tracks for interpreting ice core records
22 August, 2017 by Scott Hosking
Accumulation in coastal West Antarctic ice core records and the role of cyclone activity Cyclones are an important component of Antarctic climate variability, yet quantifying their impact on the polar …
Localized impacts and economic implications from high temperature disruption days under climate change
20 May, 2022 by Charles Simpson, Erik Mackie, Scott Hosking, Risa Ueno
Most studies into the effects of climate change have headline results in the form of a global change in mean temperature. More useful for businesses and governments, however, are measures…
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…Tipping cycles
25 March, 2022 by Michael Thorne
Instability in Jacobians is determined by the presence of an eigenvalue lying in the right half plane. The coefficients of the characteristic polynomial contain information related to the specific matrix…
Attention-based machine vision models and techniques for solar wind speed forecasting using solar EUV images
17 March, 2022 by Edward Brown, Nigel Meredith, Richard Horne
Extreme ultraviolet images taken by the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory make it possible to use deep vision techniques to forecast solar wind speed - a…Asymptotic analysis of subglacial plumes in stratified environments
9 March, 2022 by Alexander Bradley, Rosie Williams, Robert Arthern
Accurate predictions of basal melt rates on ice shelves are necessary for precise projections of the future behaviour of ice sheets. The computational expense associated with completely resolving the cavity…Read more on Asymptotic analysis of subglacial plumes in stratified environments
Vegetation interactions with geotechnical properties and erodibility of salt marsh sediments
5 February, 2022 by Ben Evans
Salt marshes provide diverse ecosystem services including coastal protection, habitat provision and carbon sequestration. The loss of salt marshes is a global scale phenomenon, of great socio-economic concern due to…The Time Machine framework: monitoring and prediction of biodiversity loss
1 February, 2022 by Scott Hosking
Transdisciplinary solutions are needed to achieve the sustainability of ecosystem services for future generations. We propose a framework to identify the causes of ecosystem function loss and to forecast the…Read more on The Time Machine framework: monitoring and prediction of biodiversity loss
Ventilation of the Southern Ocean pycnocline
1 January, 2022 by Dan(i) Jones
Ocean ventilation is the transfer of tracers and young water from the surface down into the ocean interior. The tracers that can be transported to depth include anthropogenic heat and…Convolutional conditional neural processes for local climate downscaling
1 January, 2022 by Scott Hosking
A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning…Read more on Convolutional conditional neural processes for local climate downscaling
Cetacean strandings from space: Challenges and opportunities of very high resolution satellites for the remote monitoring of cetacean mass strandings
18 November, 2021 by Hannah Cubaynes, Jennifer Jackson, Peter Fretwell
The study of cetacean strandings was globally recognised as a priority topic at the 2019 World Marine Mammal Conference, in recognition of its importance for understanding the threats to cetacean…Regional disparities and seasonal differences in climate risk to rice labour
15 November, 2021 by Charles Simpson, Scott Hosking
The 880 million agricultural workers of the world are especially vulnerable to increasing heat stress due to climate change, affecting the health of individuals and reducing labour productivity. In this…Read more on Regional disparities and seasonal differences in climate risk to rice labour
Defining Southern Ocean fronts using unsupervised classification
2 November, 2021 by Anita Faul, Dan(i) Jones, Erik Mackie
Oceanographic fronts are transitions between thermohaline structures with different characteristics. Such transitions are ubiquitous, and their locations and properties affect how the ocean operates as part of the global climate…Read more on Defining Southern Ocean fronts using unsupervised classification
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…
The call of the emperor penguin: Legal responses to species threatened by climate change
1 October, 2021 by Phil Trathan, Peter Fretwell
Species extinction risk is accelerating due to anthropogenic climate change, making it urgent to protect vulnerable species through legal frameworks in order to facilitate conservation actions that help mitigate risk.… 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
ExtremeEarth meets satellite data from space
26 August, 2021 by Andrew Fleming, Andreas Cziferszky
Bringing together a number of cutting-edge technologies that range from storing extremely large volumesof data all the way to developing scalable machine learning and deep learning algorithms in a distributed…
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…Bridging observations, theory and numerical simulation of the ocean using machine learning
22 July, 2021 by Dan(i) Jones
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the…
Matrix scaling and tipping points
16 June, 2021 by Anje-Margriet Neutel, Michael Thorne
To assess which ecosystems are most vulnerable it is necessary to compare the resilience of complex interaction networks in a meaningful way. A fundamental problem for the comparative analysis of…Remote sensing phenology of Antarctic green and red snow algae using WorldView satellites.
16 June, 2021 by Lloyd Peck, Peter Convey, Peter Fretwell
Snow algae are an important group of terrestrial photosynthetic organisms in Antarctica, where they mostly grow in low lying coastal snow fields. Reliable observations of Antarctic snow algae are difficult…
AI Lab Leadership
- Scott Hosking (Leader)
- Maria Fox (Deputy)
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AI and Digital Twinning for Decarbonisation
