# A review of machine learning in processing remote sensing data for mineral exploration

arXiv: https://arxiv.org/abs/2103.07678 · HTML: https://mlsanity.com/paper/2103.07678/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 241 references that are in the map.
- First posted on arXiv 13 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches](https://mlsanity.com/paper/1803.01164/) (2018)
- [A Novel CNN-LSTM-based Approach to Predict Urban Expansion](https://mlsanity.com/paper/2103.01695/) (2021)
- [Machine learning based hyperspectral image analysis: A survey](https://mlsanity.com/paper/1802.08701/) (2018)
- [Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks](https://mlsanity.com/paper/2004.02182/) (2020)
- [ZhuSuan: A Library for Bayesian Deep Learning](https://mlsanity.com/paper/1709.05870/) (2017)
- [Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands](https://mlsanity.com/paper/1805.03696/) (2018)
- [Computer vision-based framework for extracting geological lineaments from optical remote sensing data](https://mlsanity.com/paper/1810.02320/) (2018)

## Built on by (most cited first, 16 of 16)

- [HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models](https://mlsanity.com/paper/2402.15865/) (2024)
- [Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification](https://mlsanity.com/paper/2207.07189/) (2022)
- [HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification](https://mlsanity.com/paper/2404.00272/) (2024)
- [HyperspectralViTs: General Hyperspectral Models for On-board Remote Sensing](https://mlsanity.com/paper/2410.17248/) (2024)
- [Multi-encoder ConvNeXt Network with Smooth Attentional Feature Fusion for Multispectral Semantic Segmentation](https://mlsanity.com/paper/2602.10137/) (2026)
- [Reef-insight: A framework for reef habitat mapping with clustering methods via remote sensing](https://mlsanity.com/paper/2301.10876/) (2023)
- [AnyBand-Diff: A Unified Remote Sensing Image Generation and Band Repair Framework with Spectral Priors](https://mlsanity.com/paper/2605.14341/) (2026)
- [Semi-Automated Segmentation of Geoscientific Data Using Superpixels](https://mlsanity.com/paper/2303.11404/) (2023)
- [Leveraging Internal Representations of Model for Magnetic Image Classification](https://mlsanity.com/paper/2403.06797/) (2024)
- [Remote sensing framework for geological mapping via stacked autoencoders and clustering](https://mlsanity.com/paper/2404.02180/) (2024)
- [Convolutional neural networks for mineral prospecting through alteration mapping with remote sensing data](https://mlsanity.com/paper/2502.18533/) (2025)
- [TTRD3: Texture Transfer Residual Denoising Dual Diffusion Model for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2504.13026/) (2025)
- [A Diff-Attention Aware State Space Fusion Model for Remote Sensing Classification](https://mlsanity.com/paper/2504.16665/) (2025)
- [A generalised pre-training strategy for deep learning networks in semantic segmentation of remotely sensed images](https://mlsanity.com/paper/2604.27704/) (2026)
- [D2-CDIG: Controlled Diffusion Remote Sensing Image Generation with Dual Priors of DEM and Cloud-Fog](https://mlsanity.com/paper/2605.14326/) (2026)
- [Remote sensing data imputation using deep learning for multispectral imagery](https://mlsanity.com/paper/2605.24003/) (2026)

## Abstract (arXiv)

The decline of the number of newly discovered mineral deposits and increase in demand for different minerals in recent years has led exploration geologists to look for more efficient and innovative methods for processing different data types at each stage of mineral exploration. As a primary step, various features, such as lithological units, alteration types, structures, and indicator minerals, are mapped to aid decision-making in targeting ore deposits. Different types of remote sensing datasets, such as satellite and airborne data, make it possible to overcome common problems associated with mapping geological features. The rapid increase in the volume of remote sensing data obtained from different platforms has encouraged scientists to develop advanced, innovative, and robust data processing methodologies. Machine learning methods can help process a wide range of remote sensing datasets and determine the relationship between components such as the reflectance continuum and features of interest. These methods are robust in processing spectral and ground truth measurements against noise and uncertainties. In recent years, many studies have been carried out by supplementing geological surveys with remote sensing datasets, which is now prominent in geoscience research. This paper provides a comprehensive review of the implementation and adaptation of some popular and recently established machine learning methods for processing different types of remote sensing data and investigates their applications for detecting various ore deposit types. We demonstrate the high capability of combining remote sensing data and machine learning methods for mapping different geological features that are critical for providing potential maps. Moreover, we find there is scope for advanced methods to process the new generation of remote sensing data for creating improved mineral prospectivity maps.