Fast production of large lithologic maps using lithologic sample generation strategies based on 3D spatial positioning

Abstract

The acquisition of spatiotemporal information for lithological mapping with timeliness, accuracy, and high precision is crucial for mineral resource exploration and geological hazard prevention. However, large-scale lithological mapping remains severely constrained by the limitations of visual interpretation in obtaining representative samples from remote sensing data and error propagation during sample collection based on existing geological maps. To address this, we propose a three-dimensional spatial dual-positioning sample generation methodology (SG-3DSD) using Sentinel-2 (S2) and Landsat 8 (L8) data on the Google Earth Engine (GEE) platform, enabling automated generation of 11 lithological class samples across the Beishan region of Gansu Province, China (covering approximately 6,000 km2). First, boundary association rules were established to reconstruct 1:200,000-scale geological maps, mitigating data acquisition biases and cartographic compilation errors. Second, principal component analysis (PCA) was performed on seven S2 spectral bands, with the first three principal components (capturing > 98% information variance) constituting a 3D feature space for localized clustering. Concurrently, four L8 bands were selected through lithological spectral curve analysis to implement band ratio (BR) transformations for secondary positioning. Finally, a two-step refinement strategy was implemented to filter high-confidence samples across 11 lithological classes, balancing intraclass feature consistency and sample purity. Applying SG-3DSD-derived samples to multiple machine learning models revealed that (1) the Stacking ensemble model demonstrated superior lithological discrimination capability compared to conventional algorithms, achieving peak accuracy of 94.15% and mean F1-score of 93.87%; (2) integrating topographic data (especially Elevation) enhanced lithological positioning accuracy by 4.43% ± 1.13%; (3) PCA and BR transformations effectively enhanced lithological separability, particularly at lithological boundary zones; (4) while SG-3DSD enables efficient large-scale sample generation, it is advisable to avoid using excessively large training samples for regional-scale mapping. This methodology mitigates the weighting dependence on geological maps during sample selection and dilutes inherent cartographic error propagation, providing a novel paradigm for large-scale lithological mapping with broad application potential.

Keywords

Lithology mapping / Three-dimensional spatial positioning / Automatic sample generation / Google Earth Engine / Stacking ensemble learning

Data availability

The geological map data used in this study can be accessed and downloaded from the following link: https://www.ngac.cn/. We provide automatic lithology sample generation code on GitHub: https://github.com/zhangtao24/LSG.

CRediT authorship contribution statement

Tao Zhang: Writing - original draft, Methodology, Formal analysis, Conceptualization. Zhifang Zhao: Funding acquisition, Formal analysis, Conceptualization. Min Zeng: Writing - review & editing. Haiying Yang: Formal analysis, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors would like to express their gratitude to the anonymous reviewers for their valuable feedback on the manuscript. The research was supported by the National Natural Science Foundation of China (Grant No. 42161067).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gsf.2025.102217.

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