Recent Machine Learning Publications

Our staff have published a range of machine learning journal articles, reports and web articles.

Journal Articles:

  • Acharya, D., Farazi, M., Rolland, V., Petersson, L., Rosebrock, U., Smith, D., Ford, J., Wang, D., Tuck, G. N., Little, L. R., & Wilcox, C. (2024). Towards automatic anomaly detection in fisheries using electronic monitoring and Automatic Identification System. Fisheries Research, 272, 106939.

  • Jackett, C., Althaus, F., Maguire, K., Farazi, M., Scoulding, B., Untiedt, C., Ryan, T., Shanks, P., Brodie, P. and Williams, A. A benthic substrate classification method for seabed images using deep learning: application to management of deep-sea coral reefs. J Appl Ecol. 2023.
  • Blondeau-Patissier D, Schroeder T, Suresh G, Li Z, Diakogiannis FI, Irving P, Witte C, Steven AD. Detection of marine oil-like features in Sentinel-1 SAR images by supplementary use of deep learning and empirical methods: Performance assessment for the Great Barrier Reef marine park. Marine Pollution Bulletin. 2023 Mar 1;188:114598.
  • Taylor J, Feng M. A deep learning model for forecasting global monthly mean sea surface temperature anomalies. Frontiers in Climate. 2022;4.
  • Schroeder T, Schaale M, Lovell J, Blondeau-Patissier D. An ensemble neural network atmospheric correction for Sentinel-3 OLCI over coastal waters providing inherent model uncertainty estimation and sensor noise propagation. Remote Sens Environ. 2022;270:112848. 
  • Patricio-Valerio L, Schroeder T, Devlin M, Qin Y, Smithers S. A Machine Learning Algorithm for Himawari-8 Total Suspended Solids Retrievals in the Great Barrier Reef. Remote Sens (Basel). 2022;14(14):3503. 
  • Andreotta M, Boschetti F, Farrell S, Paris C, Walker I, Hurlstone M. Evidence for three distinct climate change audience segments with varying belief updating tendencies: Implications for climate change communication. 2022. 
  • Boschetti F, Feng M, Hartog J, Hobday A, Zhang X. Sea Surface Temperature Predictability at the interface between oceanographic modelling and machine learning. 2022. 
  • Drayson N, Anstee J, Botha H et al. Australian aquatic bio-optical dataset with applications for satellite calibration, algorithm development and validation. Data Brief. 2022;44:108489. 
  • Han J, Shoeiby M, Malthus T et al. Underwater Image Restoration via Contrastive Learning and a Real-World Dataset. Remote Sens (Basel). 2022;14(17):4297. 
  • Khokher M, Little L, Tuck G et al. Early lessons in deploying cameras and artificial intelligence technology for fisheries catch monitoring: where machine learning meets commercial fishing. Canadian Journal of Fisheries and Aquatic Sciences. 2022;79(2):257-266. 
  • Fan Y, Li W, Chen N,.., Schroeder T et al. OC-SMART: A machine learning based data analysis platform for satellite ocean color sensors. Remote Sens Environ. 2021;253:112236.
  • Qiao M, Wang D, Tuck G, Little L, Punt A, Gerner M. Deep learning methods applied to electronic monitoring data: automated catch event detection for longline fishing. ICES Journal of Marine Science. 2020;78(1):25-35. 
  • Malthus T, Lehmann E, Ho X, Botha E, Anstee J. Implementation of a Satellite Based Inland Water Algal Bloom Alerting System Using Analysis Ready Data. Remote Sens (Basel). 2019;11(24):2954.
  • Andreotta M, Nugroho R, Hurlstone M et al. Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis. Behav Res Methods. 2019;51(4):1766-1781.
  • Ford J, Peel D, Kroodsma D, Hardesty B, Rosebrock U, Wilcox C. Detecting suspicious activities at sea based on anomalies in Automatic Identification Systems transmissions. PLoS One. 2018;13(8):e0201640.
  • Fan Y, Li W, Gatebe CK, .. Schroeder T al. Atmospheric correction over coastal waters using multilayer neural networks. Remote Sensing of Environment. 2017;199:218-240.

Web Articles: