7acd7b26-f459-4643-b5d7-91e9a5e05180 urn:uuid eng UTF-8 nonGeographicDataset nonGeographicDataset 2025-10-17T01:55:34 creation ISO 19115 2016 Uncover-ML: a machine learning pipeline for geoscience data analysis. 7acd7b26-f459-4643-b5d7-91e9a5e05180 The geosciences are a data-rich domain where Earth materials and processes are analysed from local to global scales. However, often we only have discrete measurements at specific locations, and a limited understanding of how these features vary across the landscape. Earth system processes are inherently complex, and trans-disciplinary science will likely become increasingly important in finding solutions to future challenges associated with the environment, mineral/petroleum resources and food security. Machine learning is an important approach to synthesise the increasing complexity and sheer volume of Earth science data, and is now widely used in prediction across many scientific disciplines. In this context, we have built a machine learning pipeline, called Uncover-ML, for both supervised and unsupervised learning, prediction and classification. The Uncover-ML pipeline was developed from a partnership between CSIRO and Geoscience Australia, and is largely built around the Python scikit-learn machine learning libraries. In this paper, we briefly describe the architecture and components of Uncover-ML for feature extraction, data scaling, sample selection, predictive mapping, estimating model performance, model optimisation and estimating model uncertainties. Links to download the source code and information on how to implement the algorithms are also provided. Citation: Wilford, J., Basak, S., Hassan, R., Moushall, B., McCalman, L., Steinberg, D. and Zhang, F, 2020. Uncover-ML: a machine learning pipeline for geoscience data analysis. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4. {"type": "Polygon", "coordinates": [[[112.0, -44.0], [154.0, -44.0], [154.0, -9.0], [112.0, -9.0], [112.0, -44.0]]]} 112.0 154.0 -44.0 -9.0 EARTH SCIENCES EFTF Exploring for the Future INFORMATION AND COMPUTING SCIENCES Published_External Toolbox data analytics information and computer sciences machine learning Extended Abstract for download (pdf) [2.5MB] documentDigital Extended Abstract for download (pdf) [2.5MB] PDF