# Convolutional neural networks for mineral prospecting through alteration mapping with remote sensing data

arXiv: https://arxiv.org/abs/2502.18533 · HTML: https://mlsanity.com/paper/2502.18533/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 91 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation](https://mlsanity.com/paper/2304.02858/) (2023)
- [Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks](https://mlsanity.com/paper/1704.04055/) (2017)
- [A review of machine learning in processing remote sensing data for mineral exploration](https://mlsanity.com/paper/2103.07678/) (2021)
- [Revisiting Bayesian Autoencoders with MCMC](https://mlsanity.com/paper/2104.05915/) (2021)
- [Learning Translation Invariance in CNNs](https://mlsanity.com/paper/2011.11757/) (2020)
- [Bayesian neural networks via MCMC: a Python-based tutorial](https://mlsanity.com/paper/2304.02595/) (2023)
- [Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 Classification](https://mlsanity.com/paper/1901.10112/) (2019)

## Abstract (arXiv)

Traditional geological mapping, based on field observations and rock sample analysis, is inefficient for continuous spatial mapping of features like alteration zones. Deep learning models, such as convolutional neural networks (CNNs), have revolutionised remote sensing data analysis by automatically extracting features for classification and regression tasks. CNNs can detect specific mineralogical changes linked to mineralisation by identifying subtle features in remote sensing data. This study uses CNNs with Landsat 8, Landsat 9, and ASTER data to map alteration zones north of Broken Hill, New South Wales, Australia. The model is trained using ground truth data and an automated approach with selective principal component analysis (PCA). We compare CNNs with traditional machine learning models, including k-nearest neighbours, support vector machines, and multilayer perceptron. Results show that ground truth-based training yields more reliable maps, with CNNs slightly outperforming conventional models in capturing spatial patterns. Landsat 9 outperforms Landsat 8 in mapping iron oxide areas using ground truth-trained CNNs, while ASTER data provides the most accurate argillic and propylitic alteration maps. This highlights CNNs' effectiveness in improving geological mapping precision, especially for identifying subtle mineralisation-related alterations.