Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit…

By Matthew J. Cracknell, Anya M. Reading. Computational Geosciences, 2014

603 citations recorded by the source corpus.

Machine learning algorithms (MLAs) are a powerful group of data-driven inference tools that offer an automated means of recognizing patterns in high-dimensional data. Hence, there is much scope for the application of MLAs to the rapidly increasing volumes of remotely sensed geophysical data for geological mapping problems. We carry out a rigorous comparison of five MLAs: Naive Bayes, k-Nearest Neighbors, Random Forests, Support Vector Machines, and Artificial Neural Networks, in the context of a supervised lithology classification task using widely available and spatially constrained remotely sensed geophysical data. We make a further comparison of MLAs based on their sensitivity to variations in the degree of spatial clustering of training data, and their response to the inclusion of explicit spatial information (spatial coordinates). Our work identifies Random Forests as a good first choice algorithm for the supervised classification of lithology using remotely sensed geophysical data. Random Forests is straightforward to train, computationally efficient, highly stable with respect to variations in classification model parameter values, and as accurate as, or substantially more accurate than the other MLAs trialed. The results of our study indicate that as training data becomes increasingly dispersed across the region under investigation, MLA predictive accuracy improves dramatically. The use of explicit spatial information generates accurate lithology predictions but should be used in conjunction with geophysical data in order to generate geologically plausible predictions. MLAs, such as Random Forests, are valuable tools for generating reliable first-pass predictions for practical geological mapping applications that combine widely available geophysical data.

Subfields: Artificial Intelligence, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/765b78ae73f69f55d1800e21f61df21b52a1badc

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Citations in the corpus

Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction (2019, result): “This ﬁnding contrasts with recommendations from previous studies (Mascaro et al., 2013; Cracknell & Reading, 2014).”
Monitoring Land-Cover Changes: A Machine-Learning Perspective (2016, background): “A comparative analysis of different supervised learning approaches for land-cover classification has been presented in [7]–[9].”
Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) (2015, background): “Meanwhile, the application of RF to spatial data integration for geological mapping is also growing (e.g., Waske et al., 2009; Cracknell and Reading, 2013, 2014; Cracknell et al., 2013).”
A review of machine learning in processing remote sensing data for mineral exploration (2021, methodology): “In another study (Cracknell and Reading, 2014), five machine learning algorithms (naive-Bayes, K-nearest neighbor, random forest, SVM, and simple neural networks) were compared in terms of the efficiency in extracting different lithological units using Landsat 7, and spatially controlled remotely”