I am a machine learning researcher in the Artificial Intelligence (AI) Lab at the British Antarctic Survey (BAS). My research primarily focusses on the application of machine learning techniques to detect features in satellite imagery, including multispectral visible and Synthetic Aperture Radar (SAR) datasets. I have recently trained a convolutional neural network, ViSual_IceD for the detection of sea ice in concurrent mutlispectral and SAR imagery. I am passionate about potential use cases for this work, including as an data source for Autonomous Marine Operations and Planning and for improving information on sea ice conditions for habitat monitoring.

I hold a PhD from the University of Cambridge, where I applied AI techniques, including convolutional neural networks, to automatically detect coastal features and land covers in satellite imagery.

I am also working on the DeepSensor project, using neural processes for cloud infilling of fine resolution multispectral imagery of sea ice conditions.

Rogers, M.S.J, Fox, M., Fleming, A., van Zeeland, L., Wilkinson, J. and Hosking, J.S., 2024. Sea ice detection using concurrent multispectral and synthetic aperture radar imagery. arXiv preprint: https://doi.org/10.48550/arXiv.2401.06009

Bojko, J., Reinke, A.W., Stentiford, G.D., Williams, B., Rogers, M.S.J and Bass, D., 2022. Microsporidia: a new taxonomic, evolutionary, and ecological synthesis. Trends in Parasitology. doi: https://doi.org/10.1016/j.pt.2022.05.007

Rogers, M. S. J., Bithell, M., Brooks, S. M and Spencer, T. 2021. VEdge_Detector: automated coastal vegetation edge detection using a convolutional neural network. International Journal of remote sensing. http://dx.doi.org/10.1080/01431161.2021.1897185

Arundell, K.L., Dubuffet, A., Wedell, N., Bojko, J., Rogers, M.S.J and Dunn, A.M., 2019. Podocotyle atomon (Trematoda: Digenea) impacts reproductive behaviour, survival and physiology in Gammarus zaddachi (Amphipoda). Diseases of aquatic organisms, 136(1), pp.51-62. https://doi.org/10.3354/dao03416

Rogers, M. 2016. Can Structure from Motion determine the efficacy of river re-meandering as a natural flood management measure? Available online: https://www.researchgate.net/profile/Martin_Rogers7

Publications from NERC Open Research Archive

2025

Bowler, Ellen ORCID record for Bowler, Ellen. Byrne, James ORCID record for Byrne, James. Leclerc, Lisa-Marie. Roberto-Charron, Amélie. Rogers, Martin S.J. ORCID record for Rogers, Martin S.J.. Cavanagh, Rachel D. ORCID record for Cavanagh, Rachel D.. Harasimo, Jason. Lancaster, Melanie L. Chan, Ryan S.Y. Strickson, Oliver. Wilkinson, Jeremy ORCID record for Wilkinson, Jeremy. Downie, Rod. Hosking, J. Scott ORCID record for Hosking, J. Scott. Andersson, Tom R. ORCID record for Andersson, Tom R.. (2025) AI sea ice forecasts for Arctic conservation: A case study predicting the timing of caribou sea ice migrations.

2024

Stokholm, Andreas. Buus-Hinkler, Jørgen. Wulf, Tore. Korosov, Anton. Saldo, Roberto. Pedersen, Leif Toudal. Arthurs, David. Dragan, Ionut. Modica, Iacopo. Pedro, Juan. Debien, Annekatrien. Chen, Xinwei. Patel, Muhammed. Cantu, Fernando Jose Pena. Turnes, Javier Noa. Park, Jinman. Xu, Linlin. Scott, Katharine Andrea. Clausi, David Anthony. Fang, Yuan. Jiang, Mingzhe. Taleghanidoozdoozan, Saeid. Brubacher, Neil Curtis. Soleymani, Armina. Gousseau, Zacharie. Smaczny, Michał. Kowalski, Patryk. Komorowski, Jacek. Rijlaarsdam, David. van Rijn, Jan Nicolaas. Jakobsen, Jens. Rogers, Martin Samuel James ORCID record for Rogers, Martin Samuel James. Hughes, Nick. Zagon, Tom. Solberg, Rune. Longépé, Nicolas. Kreiner, Matilde Brandt. (2024) The AutoICE Challenge.

Rogers, Martin S.J. ORCID record for Rogers, Martin S.J.. Fox, Maria ORCID record for Fox, Maria. Fleming, Andrew ORCID record for Fleming, Andrew. van Zeeland, Louisa. Wilkinson, Jeremy ORCID record for Wilkinson, Jeremy. Hosking, J. Scott ORCID record for Hosking, J. Scott. (2024) Sea ice detection using concurrent multispectral and synthetic aperture radar imagery.

2023

Warren, Daniel A. Burgess, Amy L. Prati, Sebastian. Bacela-Spychalska, Karolina. Rogers, Martin S.J. ORCID record for Rogers, Martin S.J.. Bojko, Jamie. (2023) Histopathological screening of Pontogammarus robustoides (Amphipoda), an invader on route to the United Kingdom.

2022

Bojko, Jamie. Reinke, Aaron W. Stentiford, Grant D. Williams, Bryony. Rogers, Martin S.J. ORCID record for Rogers, Martin S.J.. Bass, David. (2022) Microsporidia: a new taxonomic, evolutionary, and ecological synthesis.

• Bowler, E., Byrne, J., Leclerc, L., Roberto-Charron, A., Rogers, M., Cavanagh, R., Harasimo, J., Lancaster, M., Chan, R., Strickson, O., Wilkinson, J., Downie, R., Hosking, J., & Andersson, T. (2025). Pan-Arctic 93-day sea ice concentration forecasts from the IceNet model and mappings between sea ice concentration and Dolphin and Union caribou sea ice crossing-start times (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/8738b3cb-52c7-4b36-aa6d-6e15c0b46ba4

1. Improving how we measure Antarctic sea ice

   QUASAR uses AI to improve Antarctic sea ice measurements from satellites, making climate data more reliable for scientists tracking changes in polar regions.

   Read more of: Improving how we measure Antarctic sea ice

   

4. AI for Earth Observation

   The AI for Earth Observation project develops machine learning tools to detect icebergs, sea ice, and wildlife from satellite imagery.

   Read more of: AI for Earth Observation

   

7. AI for smart conservation

   The AI for smart conservation project combines sea ice forecasts, satellite data, and GPS tracking to create early-warning systems for Arctic wildlife.

   Read more of: AI for smart conservation

   

9. Digital Twins of the Polar Regions

   This project is developing digital twins of Antarctic and Arctic environments and resources. A digital twin makes it possible to test “what if” questions far more quickly than traditional computer models.

   Read more of: Digital Twins of the Polar Regions

   

11. Machine Learning for Sea Ice Forecasting

    IceNet is a deep learning system that forecasts Arctic sea ice. The system is trained on climate simulations and observational data. It outperforms state-of-the-art dynamical models, especially for extreme sea ice events.

    Read more of: Machine Learning for Sea Ice Forecasting

    

12. AI4EOAccelerator

    AI4EO Accelerator applies artificial intelligence to the large amounts of Earth Observation satellite data now available,

    Read more of: AI4EOAccelerator

    

• Artificial Intelligence Lab
• AI4ER CDT
• DeepSensor
• Environment and Sustainability Grand Challenge.