Recent Machine Learning Publications
Our staff have published a range of machine learning journal articles, reports and web articles.
Journal Articles:
- Spillias, S., Tuohy, P., Andreotta, M., Annand-Jones, R., Boschetti, F., Cvitanovic, C., Duggan, J., Fulton, E., Karcher, D., Paris, C., Shellock, R., Trebilco, R. (2023). Human-AI Collaboration to Identify Literature for Evidence Synthesis. https://doi.org/10.1016/j.crsus.2024.100132
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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. https://doi.org/10.1016/j.fishres.2024.106939
- 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. https://doi.org/10.1111/1365-2664.14408
- 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. https://doi.org/10.1016/j.marpolbul.2023.114598
- Taylor J, Feng M. A deep learning model for forecasting global monthly mean sea surface temperature anomalies. Frontiers in Climate. 2022;4. https://doi.org/10.3389/fclim.2022.932932
- 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. https://doi.org//10.1016/j.rse.2021.112848
- 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. https://doi.org/10.3390/rs14143503
- 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. https://doi.org/10.31234/osf.io/64j7g
- Boschetti F, Feng M, Hartog J, Hobday A, Zhang X. Sea Surface Temperature Predictability at the interface between oceanographic modelling and machine learning. 2022. https://doi.org/10.21203/rs.3.rs-1721732/v1
- 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. https://doi.org/10.1016/j.dib.2022.108489
- 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. https://doi.org/10.3390/rs14174297
- 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. https://doi.org/10.1139/cjfas-2020-0446
- 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. https://doi.org/10.1016/j.rse.2020.112236
- 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. https://doi.org/10.1093/icesjms/fsaa158
- 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. https://doi.org/10.3390/rs11242954
- 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. https://doi.org/10.3758/s13428-019-01202-8
- 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. https://doi.org/10.1371/journal.pone.0201640
- Fan Y, Li W, Gatebe CK, .. Schroeder T ..et al. Atmospheric correction over coastal waters using multilayer neural networks. Remote Sensing of Environment. 2017;199:218-240. https://doi.org/10.1016/j.rse.2017.07.016
Web Articles:
- “Deep-learning AI: fast and accurate systems offer huge ocean potential” – Highlighting the wide range of machine learning applications in the marine science space at CSIRO. https://impact.economist.com/ocean/ocean-health/deep-learning-ai-fast-and-accurate-systems-offer-huge-ocean-potential
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“Making artificial intelligence count for science” – building high quality annotated algae image datasets and training machine learning models to automatically detect harmful algae in images. https://www.csiro.au/en/news/All/Articles/2023/September/artificial-intelligence-counts-science
- “AI to the rescue protecting deep-sea coral reefs” – new deep learning system that uses AI to identify deep-sea coral quickly and accurately. https://www.csiro.au/en/news/all/articles/2023/may/ai-to-the-rescue-protecting-deep-sea-coral
- “Reducing litter in Sydney Harbour” – Using cameras and machine learning to identify and count floating plastic along Sydney’s waterways – https://blog.csiro.au/reducing-litter-sydney-harbour/
- “Using satellite data to unlock water quality knowledge” – https://ecos.csiro.au/using-satellite-data-to-monitor-water-quality/
- “How we’re using machine learning to detect coral-eating COTS” – https://algorithm.data61.csiro.au/how-were-using-machine-learning-to-detect-coral-eating-cots/
- “CSIRO wields data and AI as sword and shield to protect the planet” – https://news.microsoft.com/en-au/features/csiro-wields-data-and-ai-as-sword-and-shield-to-protect-the-planet/
- “CSIRO and Microsoft partner to tackle plastic waste, illegal fishing, and efficient farming” – https://www.csiro.au/en/news/news-releases/2020/csiro-and-microsoft-partner-to-tackle-plastic-waste-illegal-fishing-and-efficient-farming
- “Utilising Artificial Intelligence to detect illegal fishing” – We are working with Microsoft to enhance our Artificial Intelligence (AI) capabilities using high-resolution cameras to detect illegal fishing and help manage marine reserves. https://www.csiro.au/en/research/technology-space/it/AI-technologies-IUU
- Olivelli A, Rosebrock U. “From Hobart, to London, to Dhaka: using cameras and AI to build an automatic litter detection system” – https://theconversation.com/from-hobart-to-london-to-dhaka-using-cameras-and-ai-to-build-an-automatic-litter-detection-system-150950
- “Using artificial intelligence to detect harmful algae” – AI to the rescue protecting deep-sea coral reefs – https://blog.csiro.au/using-artificial-intelligence-to-detect-harmful-algae/