Albrecht, T.; González-Álvarez, I.; Klump, J. Using Machine Learning to Map Western Australian Landscapes for Mineral Exploration. ISPRS Int. J. Geo-Inf.2021, 10, 459.
Albrecht, T.; González-Álvarez, I.; Klump, J. Using Machine Learning to Map Western Australian Landscapes for Mineral Exploration. ISPRS Int. J. Geo-Inf. 2021, 10, 459.
Albrecht, T.; González-Álvarez, I.; Klump, J. Using Machine Learning to Map Western Australian Landscapes for Mineral Exploration. ISPRS Int. J. Geo-Inf.2021, 10, 459.
Albrecht, T.; González-Álvarez, I.; Klump, J. Using Machine Learning to Map Western Australian Landscapes for Mineral Exploration. ISPRS Int. J. Geo-Inf. 2021, 10, 459.
Abstract
Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. These processes link geological processes at depth to surface features. Consequently, the study of landscapes can reveal essential information about the geochemical footprint of ore deposits at depth. Advances in satellite imaging and computing power have enabled the creation of large geospatial datasets, the sheer size of which necessitates automated processing. We describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available Digital Elevation Model, derived data, and sample landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve about 98\% classification accuracy with reasonable runtime of 48 minutes on a single core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, while smaller tiles result in a more detailed, and at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region.
Keywords
supervised machine learning; automated landscape mapping; digital elevation model
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
