Dave Moffat

Dave Moffat

AI and ML Research Scientist

Talks and presentations

Machine Learning and Remote Sensing: An Overview of ongoing work

April 20, 2023

Workshop Talk, Machine Learning for Earth Observation Remote Sensing with the Environment Intelligence Network, Exeter, UK

In this talk, we will provide an overview of Machine Learning (ML) and it’s uses in Earth Observation (EO). This talk will highlight a range of different ML Methodologies and how they can be applied to different remote sensing and environmental monitoring problems, demonstrating where ML can be applied to remote sensing with positive impacts - with a focus on where the benefits of using ML are.

Panel Discussion: Can ML4EO save the world?

April 20, 2023

Panel Member, Machine Learning for Earth Observation Remote Sensing with the Environment Intelligence Network, Exeter, UK

I was a panel member at the opening of the inaugaral Machine Learning for Earth Observation Remote Sensing with the Environment Intelligence Network workshop at University of Exeter. The panel discussed example of how machine learning can enhance Earth observation science, emerging trend in AI or machine learning that are particularly exciting, the greatest challenges of using machine learning for Earth Observation studies, ethical and societal considerations associated with using AI and machine learning for Earth observation and future research priorities in using AI and machine learning for remote sensing in Earth observation science.

Plymouth Marine Laboratory, Recent developments in Machine Learning and Remote Sensing

April 18, 2023

Working Group Meeting Invited Presentation, The 55th Meeting of the Working Group on Information Systems and Services, Committee on Earth Observation Satellites, Córdoba, Argentina (Remote Talk)

In this talk, we will provide an overview of recent ML developments from PML, discussing how ML has allowe us to extract further insight and extract meaningful information from earth observation data.

An Introduction to Machine Learning for Earth Observation

March 27, 2023

Guest Lecture, Open Network for Water-Related Diseases, Online Webinar

In this talk, we will provide an overview of Machine Learning (ML) and it’s uses in Earth Observation (EO). This talk will introduce the concepts of AI and ML, with the aim of explaining the differences and the fundamental approaches that can be taken in the field, with some simple examples. We will then go on to discuss a variety of different potential approaches than can be taken, and how ML can be used to overcome some consistent challenges in EO, and where it is not necessarily appropriate to use.

Computer Science: Connecting Music and Environment Science

March 16, 2023

Guest Lecture, Outside The Box Course, Computer Science Dept., University of Exeter, Exeter, UK

Connecting Music and Environment Science: from philosophical approaches to fluid mechanics, and how computer science research can cover a range of domains. In this talk we will discuss a range of different computer science domains and approaches, and demonstrate how non-traditional career paths can be both more interesting but also more advantageous than traditional software development houses.

Deep-learning detection of harmful algal blooms

October 12, 2022

Talk, University of Exeter, Institute for Data Science and Artificial Intelligence, Exeter, UK

The detrimental effects of harmful algal blooms (HABs) on the marine ecosystem, human health, and shellfish and aquaculture industry are well known. Anthropogenic activities have led to an increase in frequency, extent and magnitude of HAB activity. As a result, the detection, monitoring and forecasting of HABs are key to agencies and marine managers, allowing them to implement prevention and remediation strategies. However, HAB events are relatively rare events, that can be challenging to detect. HAB detection with satellite image data improves the coverage and efficiency of tracking HABs. Existing remote sensing-based methods frequently rely on statistical classification algorithms. While comparison with cell concentration in situ data has identified two issues: reduced accuracy for the detection of certain species, and accuracy dependency with satellite training data availability. This talk will present a deep-learning technique to improve the performance of the existing models for HAB detection from ocean colour. To this end, we developed a Machine Learning (ML) system using a few-shot learning approach for the detection of Phaeocystis and Pseudo-nitzschia HABs across the French-English channel. We assessed the performance of the ML model in comparison to in situ cell abundance data. The ML system showed better performance than the S-3 EUROHAB model, with results for the detection of Phaeocystis blooms being particularly promising.

Challenges and Solutions for using Machine Learning with Earth Observation Data

September 08, 2022

Conference proceedings talk, UK National Earth Observation Conference 2022,, Leicester, UK

The application of artificial intelligence (AI) and machine learning (ML) is rapidly growing across Earth Observation (EO). State-of-the-art ML techniques can be used to analyse and exploit vast quantities of data, to produce greater insight into data, and enable better understanding of environmental issues. Over the past year, NEODAAS have been utilising their MAGEO GPU cluster to work with users on a range of projects including harmful algal bloom detection, tree monitoring, monitoring global mangroves, ocean oil-spill detection, road vehicle and ship exhaust tracking and underwater image maerl detection. This range of projects has provided opportunities and insights into common challenges in ML with EO data, and how best to overcome them.

Deep-learning detection of Harmful Algal Blooms

September 08, 2022

Conference proceedings talk, UK National Earth Observation Conference 2022,, Leicester, UK

The detrimental effects of harmful algal blooms (HABs) on the marine ecosystem, human health, and shellfish and aquaculture industry are well known. Anthropogenic activities have led to an increase in frequency, extent and magnitude of HAB activity. As a result, the detection, monitoring and forecasting of HABs are key to agencies and marine managers, allowing them to implement prevention and remediation strategies.

Supporting Environmental Research with Artificial Intelligence at the NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS)

July 06, 2022

Conference proceedings talk, 2022 UK Conference on Environmental Data Science, Lancaster, UK

The application of AI and machine learning is rapidly growing across the environmental research field. State-of-the-art machine learning techniques can be used to analyse and exploit environmental data, to produce greater insight into the current data captured, and enable better understanding of the environment.