Inspiration for environmental AI projects
24 April, 2020 by Scott Hosking
Here are a number of polar AI topics we are seeking students and collaborators for. This is not an exhaustive list, but instead aims to highlight the potential for the …
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 to tackle our greatest environmental challenges.
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.
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
- Centre for Doctoral Training (CDT) in Satellite Data in Environmental Science, Universities of Edinburgh and Leeds, National Oceanography Centre and British Antarctic Survey
Current PhD Student Projects
- Edward Brown, BAS/Univ. Cambridge (2020-)
- Deep Learning for monitoring space weather and predicting hazards
- Marc Girona-Mata, BAS/Univ. Cambridge, Industry partner – Mott MacDonald (2020-)
- AI for hydrological processes and water security
- Raghul Parthipan, BAS/Univ. Cambridge (2020-)
- Deep Learning for improving cloud microphysical processes in climate models
- Kenza Tazi, BAS/Univ. Cambridge, Industry partner – Microsoft (2020-)
- Multifidelity deep learning for downscaling and Himalayan water security
- Rachel Furner, BAS/Univ. Cambridge, Industry partner – Google DeepMind
- Prem Gill, BAS/Univ. Cambridge, Industry partner – World Wildlife Fund (WWF)
- Jon Rosser, BAS/Univ. Cambridge, Industry partner – UK Met Office
- Risa Ueno, BAS/Univ. Cambridge, Industry partner – Max Fordham
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 Pangeo.io (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
Management
- Co-Leaders: Maria Fox and 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)
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 …
Unsupervised clustering of Southern Ocean Argo float temperature profiles
7 June, 2018 by Dan Jones
What can machine learning tell us about the structure of the Southern Ocean? In this preprint*, we apply unsupervised clustering, a machine learning method, to Southern Ocean temperature data. * …
Policy workshop summary: The impacts of machine learning on climate change modelling
7 June, 2018 by Dan Jones
Summary available here: http://www.csap.cam.ac.uk/news/article-climate-change-modelling/
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 …
How can machine learning techniques improve our understanding of climate change risks in the future?
22 February, 2017 by Emily Shuckburgh
Dr Emily Shuckburgh – Data Anaytics for Climate Decision Making – CSaP Lecture
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 …
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 …
Climate scientists help businesses tackle climate change
26 February, 2020
Climate change could add around 20% to the global cost of extreme weather events by 2040, according to early findings from Cambridge researchers. The findings come from the Cambridge Climate …
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 …
Funding to assess shipping emissions and climate hazards
13 December, 2018
British Antarctic Survey (BAS) scientists have been awarded funding to enable them to assess the impact of emissions from shipping and to quantify and manage the risk of climate hazards. …
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 …
European wind energy generation potential in a 1.5˚C world
17 May, 2018
The UK and large parts of northern Europe could become windier if global temperatures reach 1.5˚C above pre-industrial levels, according to a new study. This has implications for wind energy …
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 …
Climate Scientist awarded Prestigious Mathematical prize
18 July, 2017
Last week (12th July) Dr Emily Shuckburgh travelled to Pittsburgh, USA, and received the prestigious 2017 I. E. Block Community Lecture prize from SIAM – the Society for Industrial and …
