Ben joined the BAS AI lab in 2021 from the University of Cambridge where he received his BA, MA, MPhil and PhD and was involved as a Research Assistant/Associate in three large consortium projects. He is interested in using data-driven methods to understand and predict process interactions in biophysical sytstems. He has worked extensively with spatial data and Earth Observation as well as geospatial modelling. During his PhD he developed machine learning models to predict morphodynamic change in coastal ecosystems and at BAS is working primarily on detection and tracking of icebergs in the Southern Ocean while contributing to a range of other science projects across the organisation.

B Evans, A Faul, A Fleming, DG Vaughan, JS Hosking, (2023). Unsupervised machine learning detection of iceberg populations within sea ice from dual-polarisation SAR imagery. Remote Sensing of Environment 297, 113780. https://doi.org/10.1016/j.rse.2023.113780

Möller, I., Ionescu, M. S., Constantinescu, A. M., Evans, B. R., Scrieciu, A., Stanica, A., & Grosu, D. (2022). Bio-Physical Controls on Wave Transformation in Coastal Reed Beds: Insights From the Razelm-Sinoe Lagoon System, Romania. Frontiers in Marine Science, 0, 924. https://doi.org/10.3389/FMARS.2022.813474

Evans, B.R., Brooks, H., Chirol, C., Kirkham, M. K., Möller, I., Royse, K., et al. (2022). Vegetation interactions with geotechnical properties and erodibility of salt marsh sediments. Estuarine, Coastal and Shelf Science, 265, 107713. https://doi.org/10.1016/J.ECSS.2021.107713

Bamber, J., Bates, P., Brindley, H., Evans, B., Jackson, T., Merchant, C., Davey, M.P., Palmer, P., Scott, M., Spencer, T. (2021). Space-based Earth Observations for climate security – briefing to policymakers, COP26.

Evans, Ben R., Möller, I., & Spencer, T. (2021). Topological and Morphological Controls on Morphodynamics of Salt Marsh Interiors. Journal of Marine Science and Engineering 2021, Vol. 9, Page 311, 9(3), 311. https://doi.org/10.3390/JMSE9030311

Hu, Z., Willemsen, P. W. J. M., Borsje, B. W., Wang, C., Wang, H., Van Der Wal, D., et al. (2021). Synchronized high-resolution bed-level change and biophysical data from 10 marsh-mudflat sites in northwestern Europe. Earth System Science Data, 13(2), 405–416. https://doi.org/10.5194/ESSD-13-405-2021

Schoutens, K., Reents, S., Nolte, S., Evans, B., Paul, M., Kudella, M., et al. (2021). Survival of the thickest? Impacts of extreme wave‐forcing on marsh seedlings are mediated by species morphology. Limnology and Oceanography, lno.11850. https://doi.org/10.1002/lno.11850

Evans, Ben R., Möller, I., Spencer, T., & Smith, G. (2019). Dynamics of salt marsh margins are related to their three-dimensional functional form. Earth Surface Processes and Landforms, 44(9), 1816–1827. https://doi.org/10.1002/esp.4614

Brooks, H., Möller, I., Carr, S., Chirol, C., Christie, E., Evans, B., et al. (2021). Resistance of salt marsh substrates to near-instantaneous hydrodynamic forcing. Earth Surface Processes and Landforms, 46(1), 67–88. https://doi.org/10.1002/esp.4912

Chirol, C., Spencer, K. L., Carr, S. J., Möller, I., Evans, B., Lynch, J., et al. (2021). Effect of vegetation cover and sediment type on 3D subsurface structure and shear strength in saltmarshes. Earth Surface Processes and Landforms, 46(11), 2279–2297. https://doi.org/10.1002/esp.5174

Brooks, H., Möller, I., Carr, S., Chirol, C., Christie, E., Evans, B., et al. (2020). Resistance of salt marsh substrates to near-instantaneous hydrodynamic forcing. Earth Surface Processes and Landforms, 46(1), 67–88. https://doi.org/10.1002/esp.4912

Schuerch, M., Spencer, T., & Evans, B. (2019). Coupling between tidal mudflats and salt marshes affects marsh morphology. Marine Geology, 412, 95–106. https://doi.org/10.1016/j.margeo.2019.03.008

Evans, B. R. (2018). Data-driven prediction of saltmarsh morphodynamics. PhD Thesis – University of Cambridge

Willemsen, P. W. J. M., Borsje, B. W., Hulscher, S. J. M. H., Van der Wal, D., Zhu, Z., Oteman, B., et al. (2018). Quantifying bed level change at the transition of tidal flat and salt marsh: can we understand the lateral location of the marsh edge? Journal of Geophysical Research: Earth Surface. https://doi.org/10.1029/2018JF004742

Ford, H., Evans, B., Van Klink, R., Skov, M. W. M. W., & Garbutt, A. (2017). The importance of canopy complexity in shaping seasonal spider and beetle assemblages in saltmarsh habitats. Ecological Entomology, 42(2), 145–155. https://doi.org/10.1111/een.12373

Reef, R., Spencer, T., Möller, I., Lovelock, C. E., Christie, E. K., Mcivor, A. L., et al. (2016). The effects of elevated CO2 and eutrophication on surface elevation gain in a European salt marsh. Global Change Biology. https://doi.org/10.1111/gcb.13396

Rupprecht, F., Möller, I., Evans, B., Spencer, T., & Jensen, K. (2015). Biophysical properties of salt marsh canopies – Quantifying plant stem flexibility and above ground biomass. Coastal Engineering, 100, 48–57. https://doi.org/10.1016/j.coastaleng.2015.03.009

Spencer, T., Brooks, S. M., Evans, B. R., Tempest, J. A., & Möller, I. (2015). Southern North Sea storm surge event of 5 December 2013: Water levels, waves and coastal impacts. Earth-Science Reviews, 146(December 2013), 120–145. https://doi.org/10.1016/j.earscirev.2015.04.002

Spencer, T., Brooks, S. M., Möller, I., & Evans, B. R. (2014). Where Local Matters: Impacts of a Major North Sea Storm Surge. Eos, Transactions American Geophysical Union, 95(30), 269–270. https://doi.org/10.1002/2014EO300002

Publications from NERC Open Research Archive

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

   

2. 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

   

3. 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

   

1. Using AI to track icebergs

   Researchers are using a new AI tool to detect icebergs in the Southern Ocean – the first step towards tracking the complete lifecycle of Antarctic icebergs from satellite data.

   Read more of: Using AI to track icebergs