Heliyon (Sep 2023)
A review on advancements in lithological mapping utilizing machine learning algorithms and remote sensing data
Abstract
Lithological mapping is a fundamental undertaking in geological research, mineral resource exploration, and environmental management. However, conventional methods for lithological mapping are often laborious and challenging, particularly in remote or inaccessible areas. Fortunately, a transformative solution has emerged through the integration of remote sensing and machine learning algorithms, providing an efficient and accurate means of deciphering the geological features of the Earth's crust. Remote sensing offers vast and comprehensive data across extensive geographical regions, while machine learning algorithms excel at recognizing intricate patterns and features in the data, enabling the classification of different lithological units. Compared to traditional methods, this approach is faster, more efficient, and remarkably accurate. The combination of remote sensing and machine learning presents numerous advantages, including the ability to amalgamate multiple data sources, provide up-to-date information on rapidly changing regions, and manage vast volumes of data. This review article delves into the invaluable contributions of remote sensing and machine learning algorithms to lithological mapping. It extensively explores diverse remote sensing datasets, such as Landsat, Sentinel-2, ASTER, and Hyperion data, which can be effectively harnessed for this purpose. Additionally, the study investigates a range of machine learning algorithms, including SVM, RF, and ANN, specifically tailored for lithological mapping. By scrutinizing practical use cases, the review underscores the strengths, limitations, and potential future developments of remote sensing and machine learning algorithms in the context of lithological mapping. Practical use cases have demonstrated the immense potential of machine learning algorithms, with the SVM classifier emerging as a reliable and accurate option for lithological mapping. Moreover, the choice of the most appropriate data source depends on the specific objectives of the application.Overall, the transformative potential of remote sensing and machine learning in lithological mapping cannot be overstated. This integrated approach not only enhances our understanding of geological features but also enables diverse applications in geological research and environmental management. With the promise of a more informed and sustainable future, the utilization of remote sensing and machine learning in lithological mapping represents a pivotal advancement in the field of geological sciences.
