Dave Moffat

Dave Moffat

AI Data Science Lead

Posts by Collection

chapter

conference

Automatic Subgrouping of Multitrack Audio

Published:

Use Google Scholar for full citation

David Ronan, David Moffat, Hatice Gunes, Joshua Reiss, "Automatic Subgrouping of Multitrack Audio." In the proceedings of Proceedings of the 18th International Conference on Digital Audio Effects (DAFx-15), 2015.

Modal Synthesis of Weapon Sounds

Published:

Use Google Scholar for full citation

Lucas Mengual, David Moffat, Joshua Reiss, "Modal Synthesis of Weapon Sounds." In the proceedings of Proceedings of the Audio Engineering Society Conference: 61st International Conference: Audio for Games, 2016.

Real-Time Physical Model for an Aeolian Harp

Published:

Use Google Scholar for full citation

Rod Selfridge, David Moffat, Joshua Reiss, Eldad Avital, "Real-Time Physical Model for an Aeolian Harp." In the proceedings of Proceedings of the 24th International Congress on Sound and Vibration, 2017.

Unsupervised Taxonomy of Sound Effects

Published:

Use Google Scholar for full citation

David Moffat, David Ronan, Joshusa Reiss, "Unsupervised Taxonomy of Sound Effects." In the proceedings of Proc. 20th International Conference on Digital Audio Effects (DAFx-17), 2017.

Artificially synthesising data for audio classification and segmentation to improve speech and music detection in radio broadcast

Published:

Use Google Scholar for full citation

Satvik Venkatesh, David Moffat, Alexis Kirke, Gözel Shakeri, Stephen Brewster, Jörg Fachner, Helen Odell-Miller, Alex Street, Nicolas Farina, Sube Banerjee, Eduardo Miranda, "Artificially synthesising data for audio classification and segmentation to improve speech and music detection in radio broadcast." In the proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.

RadioMe: Supporting Individuals with Dementia in Their Own Home… and Beyond

Published:

Di Campli San Vito, P., Brewster, S., Venkatesh, S., Miranda, E., Kirke, A., Moffat, D., Banerjee, S., Street, A., Fachner, J. and Odell-Miller "RadioMe: Supporting Individuals with Dementia in Their Own Home... and Beyond?" In the proceedings of 2022 CHI Conference on Human Factors in Computing Systems (CHI 22) Workshop 32, New Orleans, LA, USA, 30 Apr 2022.

journal

TLS2trees: a scalable tree segmentation pipeline for TLS data Permalink

Published:

Phil Wilkes, Mathias Disney, John Armston, Harm Bartholomeus, Lisa Bentley, Benjamin Brede, Andrew Burt, Kim Calders, Cecilia Chavana-Bryant, Daniel Clewley, Laura Duncanson, Brieanne Forbes, Sean Krisanski, Yadvinder Malhi, David Moffat, Niall Origo, Alexander Shenkin, Wanxin Yang, "TLS2trees: a scalable tree segmentation pipeline for TLS data." Methods in Ecology and Evolution. Wiley. October 2023. https://doi.org/10.1111/2041-210X.14233
Download here

Drone imagery and deep learning for mapping the density of wild Pacific oysters to manage their expansion into protected areas Permalink

Published:

Access paper here

Aser Mata, David Moffat, Sílvia Almeida, Marko Radeta, William Jay, Nigel Mortimer, Katie Awty-Carroll, Oliver R. Thomas, Vanda Brotas, Steve Groom "Drone imagery and deep learning for mapping the density of wild Pacific oysters to manage their expansion into protected areas." Ecological Informatics. 102708, July 2024 https://doi.org/10.1016/j.ecoinf.2024.102708
Download here

Sensing human health from space: An assessment of applications and big data platforms

Published:

Access paper here

D. Sengupta, F. G. Bandao, S. Sathyendranath, G. Kulk, A. Conte, C. Ippolitti, L. Candeloro, D. Moffat, W. Wint, M. Maranesi, R. Scarano, J. Vitorino, G. Brandt and T. M Harish "Sensing human health from space: An assessment of applications and big data platforms." Remote Sensing Applications: Society and Environment 2025 https://doi.org/10.1016/j.rsase.2025.101701
Download here

ForestScan: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data Permalink

Published:

Chavana-Bryant, C., Wilkes, P., Yang, W., Burt, A., Vines, P., Bennett, A. C., Pickavance, G. C., Cooper, D. L. M., Lewis, S. L., Phillips, O. L., Brede, B., Lau, A., Herold, M., McNicol, I. M., Mitchard, E. T. A., Coomes, D. A., Jackson, T. D., Makaga, L., Milamizokou Napo, H. O., Ngomanda, A., Ntie, S., Medjibe, V., Dimbonda, P., Soenens, L., Daelemans, V., Proux, L., Nilus, R., Labrière, N., Jeffery, K., Burslem, D. F. R. P., Clewley, D., Moffat, D., Qie, L., Bartholomeus, H., Vincent, G., Barbier, N., Derroire, G., Abernethy, K., Scipal, K., and Disney, M. "ForestScan: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data." Earth System Science Data, 18, 1243–1274 February 2026. https://doi.org/10.5194/essd-18-1243-2026
Download here

Modelling primary production: multitude of theories, or multitude of languages? Permalink

Published:

Skákala, J., Sathyendranath, S., Artioli, Y., Banerjee, D. S., Bouman, H., Brewin, R. J. W., Butenschön, M., Ciavatta, S., Dutkiewicz, S., Fidai, Y., Ford, D., George, G., Guihou, K., Jönsson, B., Bačeković Koloper, M., Kovač, Ž., Krishnakumary, L., Kulk, G., Laufkötter, C., Lessin, G., Mattern, J. P., Melet, A., Mignot, A., Moffat, D., Monteiro, F., Rodriguez Bennadji, M., Rousseaux, C., Swaminathan, R., Ulloa, O., and Tjiputra, J. "Modelling primary production: multitude of theories, or multitude of languages? ", Ocean Science, 3.22; EGUsphere, 2026. 1-39 https://doi.org/10.5194/os-22-1457-2026
Download here

portfolio

talks

Deep-learning detection of Harmful Algal Blooms

Published:

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.

Challenges and Solutions for using Machine Learning with Earth Observation Data

Published:

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

Published:

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.

Computer Science: Connecting Music and Environment Science

Published:

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.

An Introduction to Machine Learning for Earth Observation

Published:

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.

Panel Discussion: Can ML4EO save the world?

Published:

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.

Machine Learning and Remote Sensing: An Overview of ongoing work

Published:

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.

teaching

Other Teaching

Teaching Associate, Queen Mary University of London, Teaching, 2018

I was involved in a range of teaching course, including

PhD Supervision

Workshop, University of Plymouth, PhD Student Supervision, 2019

I supervised three PhD students through their studies, as their PhD supervisor.

ResM Supervision

Workshop, University of Plymouth, Research Masters Student Supervison, 2019

I supervised Research Masters students through their studies.

Plymouth University Teaching 2019-20

Workshop, University of Plymouth, Computing Audio and Music Technology BSc., 2019

As a lecturer at the University of Plymouth, I was the Programme Leader for Computing Audio and Music Technology BSc. teaching on the following modules

EI CDT - ML for EO

One day, 23 participants Hybrid. Co-lead training course and all delivery, , 2022

EI CDT - ML for EO

Two day, 37 participants In Person. Co-lead training course and all delivery, , 2022

SENSE CDT - ML4EO

Two day, 16 participants In Person. Supported additional training on atmospheric correction and EO, , 2022

SENSE CDT - ML4EO

Two day, 28 participants In Person. Supported additional training on research techniques, atmospheric correction and EO, , 2023