News & Events

NCEO Researchers' Forum: Machine Learning and AI

In March 2022, NCEO launched a new and exciting headquarters, the Leicester Space Park. After the launch, researchers from various institutions with affiliations to NCEO were invited to the Annual Researchers’ Forum at the new HQ.

After a break in 2021 due to the COVID pandemic, the NCEO Researchers’ Forum was hosted by the University of Leicester from 16-17 March 2022.

Over 30 early career researchers from throughout NCEO gathered for two days at Space Park Leicester to learn about the use of machine learning in Earth Observation.

Here, Laura Risley and Ieuan Higgs share their experience at the Researchers’ Forum, from machine learning to shuffleboard.

Machine Learning and Artificial Intelligence be used for Earth Observation (EO)

Machine Learning (ML) and Artificial Intelligence (AI) are often thought of as intimidating and amorphous topics. This fog of misconceptions was quickly cleared up, however, as the workshop provided a brilliant, fascinating and well-structured introduction into how these fields can be leveraged in the context of earth observation.

The forum began bright and (very) early on Wednesday morning at the Leicester Space Park. Our first day of ML training began with an introduction to NCEO by director – John Remedios, and training lead – Amos Lawless. We each had the opportunity to introduce ourselves and our research in a quick two-minute presentation. This highlighted the variety in both background and experience in an entirely positive way! As well as benefiting directly from the training itself, we enjoyed being in a room full of enthusiastic people with knowledge and niches aplenty.

Laura Risley introducing herself

Researchers introduced themselves and their research by giving a two-minute presentation.

Examples of how ML can be used in EO

Next, we had a talk from Sebastian Hickman, a PhD student at the University of Cambridge, who introduced his work on using ML with satellite imagery to detect tall-tree-mortalities in the Amazonian rainforest. A second talk was given by Duncan Watson-Parris from the University of Oxford on using ML to identify ship tracks from satellite imagery. These initial talks immediately got us thinking about the different ways in which ML could be used within the realm of earth science.

Machine Learning for Earth Observation training

The course was delivered by NCEO colleagues from NEODAAS, who provided an introduction to the basic ideas of machine learning and the opportunity to work through exercises on their MAGEO cluster, applying machine learning to different examples. This was supplemented by research talks on different applications, by scientists from NEODAAS and from the ESA Phi-lab.

David Moffat Machine Learning Presentation

Introduction to Machine Learning for Earth Observation was presented by David Moffat and Katie Awty-Carroll from Plymouth Marine Laboratory.

To begin our ML training, members of NEODAAS David Moffat and Katie Awty-Carroll, led us through an introduction into ML and AI, its uses and importance in modern scientific context. The graphic below – presented by David and Katie – makes a digestible distinction between some commonly conflated terms in the subject area:

AI vs ML vs DL*

The discussion on the limitations and ‘bottlenecks’ of ML was of particular interest, it highlighted the numerous considerations to be made when developing an ML solution. For example, the subset of data used to train a model should ideally be representative of the entire system, avoiding or at least acknowledging the potential biases introduced by: human-preferences in selecting and filtering training data; the method of data collection method; the design of the ML techniques used; and how we interpret the outputs. While this may seem obvious at first, it is certainly not trivial. There are high-profile and hotly-debated examples of AI being used in the real-world where biases have led to significant human-affecting consequences.

For example:

Racial Discrimination in Face Recognition Technology and Artificial intelligence and algorithmic bias: implications for health systems

We were prompted to consider these ethical questions and the efficacy of ML in the context of earth science: Which problems does ML help us solve and, perhaps more importantly, which problems are we willing to entrust it with?


A fun exercise you can try for yourself: search for images of a given profession in your search engine of choice. See if you can identify any patterns or biases in what may have been included or even excluded from the selected results!


We then began the practical sessions which all fell within the broad umbrella of ML. This required a slight mindset shift from traditional programming as, even from a top-down perspective, the way we approached problems was completely different:

Traditional Programming vs Machine Learning

Applying Machine Learning to our work

We were given Jupyter notebooks to work on three separate practicals; random forest classification, a neural network for regression, and convolutional neural networks. Each showed a different application and use-case of ML, giving us more ideas on how it could potentially be implemented into our own research. Adjacent to this, we were given a workflow task to think about over the two days: how could we use ML in our own projects? At the end of the second day we each presented our ideas and were given feedback. This helped ground the talks with an ongoing focus to relate new knowledge back to our own varied fields; allowing the workshop to elegantly handle the variety and promote the actual use of the skills in our own work.

Networking opportunities

The forum was academically challenging but it was also great fun! Surrounding the concentrated days of learning, the forum offered us plenty of chances to connect with others. We were given a tour of Space Park, an impressive space you could say was out of this world. The evening activities, bowling and shuffleboard, had a great atmosphere too!

Researchers were also given a tour of the laboratory and computing facilities at Space Park Leicester to demonstrate how SPL is becoming a collaborative community of industry, academics and students working together to drive growth in space and space-enabled sectors.


NCEO Training Lead, Amos Lawless, commented

“It was really great to be able to bring people together again, both to provide some training in an emerging topic of Earth Observation science and to give NCEO early career researchers the possibility to get to know each other and share their experiences.”

By the end of the event, the interest and enthusiasm of the attendees had been effectively transformed into new understanding and conversation – which is unsurprising considering the increasing relevance ML is gaining in the field of earth science. Further to this, making connections over the pandemic has been difficult, so we felt extremely fortunate that we were able to meet in person.


Laura – The forum was an exciting insight into a field I had no experience in. Although my  immediate work is focused on the application of data assimilation to ocean measurements, which does not directly relate to ML at the moment, data assimilation has high potential to overlap with ML .The forum has furthered my understanding of fields that surround the focal point of my research. In turn, this has helped me gain a more well-rounded knowledge base, opening doors to new directions my research could take.


Ieuan – The forum has certainly given me many new avenues to explore when approaching the intended application of ML in my work, perhaps starting simple with a neural network for multivariate regression and expanding from there. The hands-on practicals were a valuable opportunity for practice and a great chance for some informal discussion on the details of ML implementation with my peers. Moreover, the event has equipped us with the skills to effectively engage with other academics when they present ML-based work – which is something I would love to do in future events! As a member and fundee of NCEO, I was excited to see a training event on offer that was highly relevant to my project.


We both hope there will be more NCEO workshops like this in the future, perhaps an event or meetup that focuses on the intersection of ML and data assimilation, as these topics resonate with us both. We’d like to thank the NEODAAS staff from PML for leading the training and Uzma Saeed for organising the forum. It was a fun and engaging experience that we are grateful to have taken part in and we would encourage anyone with the opportunity to learn about ML to do so!


NCEO Researchers Forum delegates 2022 outside Space Park Leicester

* Graphics were provided by the NEODAAS slides used at the NCEO forum


About the Authors

Laura Risley 

Laura is a Scenario student based at the University of Reading, Department of Mathematics  and  Statistics; supervised by Amos Lawless, NCEO Head of Data Assimilation Methodology & Training Lead. Her project is ‘Assimilation of future ocean-current measurements from satellites’. This will involve applying data assimilation to assimilate ocean-current velocities in preparation for data from future satellites.

Ieuan Higgs

Ieuan is a Scenario Associate based at the University of Reading, Meteorology Department. His project is titled ‘Complex network approach to improve marine ecosystem modelling and data assimilation’.  Ieuan applies complex-network-informed machine learning techniques to predict concentrations of the less well observable nutrients in the ocean, from the well observable quantities – such as phytoplankton.