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Abstract

The green energy transition has intensified global demand for critical minerals, driving the expansion of mining activities with significant environmental consequences. In response, we present a globally consistent dataset of land use and land cover classification within mining areas, providing detailed information for over 80,000 recognised mining extents across 150 countries, spanning 95,644 km² and offering global-scale insights into the mining footprint. Developed through the integration of Sentinel-2 imagery and TanDEM-X elevation change data with a Random Forest classifier, this dual-source integration supports the differentiation of functionally different but spectrally similar land use types, such as open pits and waste dumps. This distinction is critical because different land uses pose varying environmental risks. By accurately identifying specific land use types, rather than treating all disturbed or adjacent areas as equally impacted, the dataset avoids overestimating mining-affected land. Ultimately, it provides a more accurate depiction of land use within mining areas and significantly improves the reliability of environmental impact assessments in the mining sector.

Data availability

The full dataset55 is openly accessible and archived under the https://doi.org/10.5281/zenodo.15726306.

Code availability

The codes for data acquisition and preprocessing, the Random Forest (RF) classification model, and the Majority Filter (MF) smoothing procedure are available at https://github.com/yutongcheng/Classifying-land-use-gee-rf-mf-example. By running the corresponding sections of the scripts with the provided example datasets, users can reproduce example outputs using the same workflow as in this study.

References

International Energy Agency (IEA). The role of critical minerals in clean energy transitions. International Energy Agency, https://www.iea.org/reports/the-role-of-critical-minerals-in-clean-energy-transitions (2021). 1.

Arrobas, D. L. P., Hund, K. L., McCormick, M. S., Ningthoujam, J. & Drexhage, J. R. The growing role of minerals and metals for a low carbon future. World Bank, https://documents.worldbank.org/en/publication/documents-reports/documentdetail/207371500386458722 (2017). 1.

Sovacool, B. K. et al. Sustainable minerals and metals for a low-carbon future. Science 367, 30–33, https://doi.org/10.1126/science.aaz6003 (2020).

Google Scholar 1.

Hund, K. L., Arrobas, D. L. P., Fabregas Masllovet, T. P., Laing, T. J. & Drexhage, J. R. Minerals for climate action: The mineral intensity of the clean energy transition. World Bank, https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099052423172525564 (2023). 1.

Sonter, L. J., Ali, S. H. & Watson, J. E. M. Mining and biodiversity: Key issues and research needs in conservation science. Proc. R. Soc. B Biol. Sci. 285(1892), 20181926, https://doi.org/10.1098/rspb.2018.1926 (2018).

Google Scholar 1.

Luckeneder, S., Giljum, S., Schaffartzik, A., Maus, V. & Tost, M. Surge in global metal mining threatens vulnerable ecosystems. Glob. Environ. Change 69, 102303, https://doi.org/10.1016/j.gloenvcha.2021.102303 (2021).

Google Scholar 1.

Whieldon, E. et al. Rocks and hard places: The ecosystem risks of mining for energy transition minerals. S&P Global Sustainable1, https://www.spglobal.com/esg/insights/featured/special-editorial/rocks-and-hard-places-the-ecosystem-risks-of-mining-for-energy-transition-minerals (2024). 1.

Weston, P. Boom in mining for renewable energy minerals threatens Africa’s great apes. The Guardian, https://www.theguardian.com/environment/2024/apr/03/boom-in-mining-for-renewable-energy-minerals-threatens-africas-great-apes-aoe (2024). 1.

Sonter, L. J. et al. Mining drives extensive deforestation in the Brazilian Amazon. Nat. Commun. 8, 1013, https://doi.org/10.1038/s41467-017-00557-w (2017).

Google Scholar 1.

Uribe-Sierra, S. E., Carte, L., Mansilla-Quiñones, P. & Moreira-Muñoz, A. The impact of open-pit mining in mountainous areas on eco-anxiety and future images of the place. Emot. Space Soc. 53, 101045, https://doi.org/10.1016/j.emospa.2024.101045 (2024).

Google Scholar 1.

Hu, X., Oommen, T., Lu, Z., Wang, T. & Kim, J.-W. Consolidation settlement of Salt Lake County tailings impoundment revealed by time-series InSAR observations from multiple radar satellites. Remote Sens. Environ. 202, 199–209, https://doi.org/10.1016/j.rse.2017.05.023 (2017).

Google Scholar 1.

Islam, K. & Murakami, S. Global-scale impact analysis of mine tailings dam failures: 1915–2020. Glob. Environ. Change 70, 102361, https://doi.org/10.1016/j.gloenvcha.2021.102361 (2021).

Google Scholar 1.

Su, C., Rana, N. M., Zhang, S. & Wang, B. Environmental pollution and human health risk due to tailings storage facilities in China. Sci. Total Environ. 928, 172437, https://doi.org/10.1016/j.scitotenv.2024.172437 (2024).

Google Scholar 1.

Kusi, J. K., Foli, G., Peasah, M. Y. & Akoto, O. Geochemical characterization of waste rock dump and its potential to predict acid mine drainage. Sci. Afr. 26, e02350, https://doi.org/10.1016/j.sciaf.2024.e02350 (2024).

Google Scholar 1.

Chen, H. B., Chen, H. M., Chen, Z. B. & Chen, Z. B. The ecological impacts of residues from the heap leaching of ion-adsorption rare earth clays. Int. J. Environ. Sci. Technol. 20, 13267–13276, https://doi.org/10.1007/s13762-023-04873-1 (2023).

Google Scholar 1.

Werner, T. T. et al. Global-scale remote sensing of mine areas and analysis of factors explaining their extent. Glob. Environ. Change 60, 102007, https://doi.org/10.1016/j.gloenvcha.2019.102007 (2020).

Google Scholar 1.

Maus, V. & Werner, T. T. Impacts for half of the world’s mining areas are undocumented. Nature 625(7993), 26–29, https://doi.org/10.1038/d41586-023-04090-3 (2024).

Google Scholar 1.

OECD. Environment at a Glance Indicators. OECD Publishing, Paris, https://doi.org/10.1787/ac4b8b89-en (2024). 1.

S&P Global Market Intelligence. SNL Metals and Mining Database (2023). 1.

Jasansky, S., Lieber, M., Giljum, S. & Maus, V. An open database on global coal and metal mine production. Sci. Data 10, 52, https://doi.org/10.1038/s41597-023-01965-y (2023).

Google Scholar 1.

U.S. Geological Survey (USGS). Mineral resources online spatial data, https://mrdata.usgs.gov/ (2024). 1.

Maus, V. et al. An update on global mining land use. Sci. Data 9, 433, https://doi.org/10.1038/s41597-022-01547-4 (2022).

Google Scholar 1.

Tang, L. & Werner, T. T. Global mining footprint mapped from high-resolution satellite imagery. Commun. Earth Environ. 4, 1–12, https://doi.org/10.1038/s43247-023-00805-6 (2023).

Google Scholar 1.

Lechner, A. M. et al. Historical socio-environmental assessment of resource development footprints using remote sensing. Remote Sens. Appl.: Soc. Environ. 15, 100236, https://doi.org/10.1016/j.rsase.2019.100236 (2019).

Google Scholar 1.

Maxwell, A. E. et al. Semantic segmentation deep learning for extracting surface mine extents from historic topographic maps. Remote Sens 12(24), 4145, https://doi.org/10.3390/rs12244145 (2020).

Google Scholar 1.

Wang, C., Chang, L., Zhao, L. & Niu, R. Automatic identification and dynamic monitoring of open-pit mines based on improved Mask R-CNN and transfer learning. Remote Sens. 12(21), 3474, https://doi.org/10.3390/rs12213474 (2020).

Google Scholar 1.

Camalan, S. et al. Change detection of Amazonian alluvial gold mining using deep learning and Sentinel-2 imagery. Remote Sens. 14(7), 1746, https://doi.org/10.3390/rs14071746 (2022).

Google Scholar 1.

Silva, M., Hermosilla, G., Villavicencio, G. & Breul, P. Automated detection and analysis of massive mining waste deposits using Sentinel-2 satellite imagery and artificial intelligence. Remote Sens. 15(20), 4949, https://doi.org/10.3390/rs15204949 (2023).

Google Scholar 1.

Jabłońska, K. et al. MineCam: Application of combined remote sensing and machine learning for segmentation and change detection of mining areas enabling multi-purpose monitoring. Remote Sens. 16(6), 955, https://doi.org/10.3390/rs16060955 (2024).

Google Scholar 1.

Xing, J. et al. CTMNet: Enhanced open-pit mine extraction and change detection with a hybrid CNN-Transformer multitask network. IEEE Trans. Geosci. Remote Sens. 62, https://doi.org/10.1109/TGRS.2024.3492715 (2024). 1.

Xiao, J., Werner, T. T., Komai, T. & Matsubae, K. Assessing the relationship between production and land transformation for Chilean copper mines using satellite and operational data. Resour. 14(2), 25, https://doi.org/10.3390/resources14020025 (2025).

Google Scholar 1.

Saputra, M. R. U. et al. Multi-modal deep learning approaches to semantic segmentation of mining footprints with multispectral satellite imagery. Remote Sens. Environ. 318, 114584, https://doi.org/10.1016/j.rse.2024.114584 (2025).

Google Scholar 1.

Chen, W., Li, X., He, H. & Wang, L. A review of fine-scale land use and land cover classification in open-pit mining areas by remote sensing techniques. Remote Sens. 10(1), 15, https://doi.org/10.3390/rs10010015 (2018).

Google Scholar 1.

Hoang, N. T. & Koike, K. Comparison of hyperspectral transformation accuracies of multispectral Landsat TM, ETM+, OLI and EO-1 ALI images for detecting minerals in a geothermal prospect area. ISPRS J. Photogramm. Remote Sens. 137, 15–28, https://doi.org/10.1016/j.isprsjprs.2018.01.007 (2018).

Google Scholar 1.

Maus, V. et al. Global-scale mining polygons (Version 2). PANGAEA https://doi.org/10.1594/PANGAEA.942325 (2022). 1.

Tang, L. & Werner, T. T. Global mining footprint mapped from high-resolution satellite imagery. Zenodo https://doi.org/10.5281/zenodo.7894216 (2023). 1.

Lachaise, M., González, C., Rizzoli, P., Schweißhelm, B. & Zink, M. The new TanDEM-X DEM change maps product. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 5432–5435. IEEE, https://doi.org/10.1109/IGARSS46834.2022.9883612 (2022). 1.

Lachaise, M. & Schweißhelm, B. TanDEM-X 30m DEM change maps product description (Issue Public Document TD-GS-PS-0216 Issue 1.0, 12 October 2023). German Aerospace Center (DLR), https://geoservice.dlr.de (2023). 1.

Martone, M. et al. The global forest/non-forest map from TanDEM-X interferometric SAR data. Remote Sens. Environ. 205, 352–373, https://doi.org/10.1016/j.rse.2017.12.002 (2018).

Google Scholar 1.

González, C., Bachmann, M., Bueso-Bello, J.-L., Rizzoli, P. & Zink, M. A fully automatic algorithm for editing the TanDEM-X global DEM. Remote Sens. 12(23), 3961, https://doi.org/10.3390/rs12233961 (2020).

Google Scholar 1.

Bueso-Bello, J.-L. et al. The global water body layer from TanDEM-X interferometric SAR data. Remote Sens. 13(24), 5069, https://doi.org/10.3390/rs13245069 (2021).

Google Scholar 1.

Drusch, M. et al. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens. Environ. 120, 25–36, https://doi.org/10.1016/j.rse.2011.11.026 (2012).

Google Scholar 1.

Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031 (2017).

Google Scholar 1.

Rouse, J. W., Haas, R. H., Schell, J. A. & Deering, D. W. Monitoring vegetation systems in the Great Plains with ERTS. NASA Goddard Space Flight Center 3rd ERTS-1 Symposium, Sect. A (1974). 1.

Zha, Y., Gao, J. & Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 24, 583–594, https://doi.org/10.1080/01431160304987 (2003).

Google Scholar 1.

Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27, 3025–3033, https://doi.org/10.1080/01431160600589179 (2006).

Google Scholar 1.

Mzid, N., Pignatti, S., Huang, W. & Casa, R. An analysis of bare soil occurrence in arable croplands for remote sensing topsoil applications. Remote Sens. 13, 474, https://doi.org/10.3390/rs13030474 (2021).

Google Scholar 1.

Google Earth Engine. COPERNICUS/S2_SR_HARMONIZED: Harmonized Sentinel-2 MSI, Level-2A SR, https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED (2025). 1.

Google Earth Engine. COPERNICUS/S2_HARMONIZED: Harmonized Sentinel-2 MSI, Level-1C (TOA), https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED (2025). 1.

Belgiu, M. & Drăguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011 (2016).

Google Scholar 1.

Jensen, J. R. Remote Sensing of the Environment: An Earth Resource Perspective 2nd edn. Pearson Prentice Hall, Upper Saddle River, NJ (2007). 1.

Lillesand, T. M., Kiefer, R. W. & Chipman, J. W. Remote Sensing and Image Interpretation 7th edn. Wiley, Hoboken, NJ (2015). 1.

Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2(3), 18–22, https://journal.r-project.org/articles/RN-2002-022/ (2002).

Google Scholar 1.

Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28(5), 1–26, https://doi.org/10.18637/jss.v028.i05 (2008).

Google Scholar 1.

Cheng, Y-T., Hoang, NT., Maupu, L., & Kanemoto, K. Global dataset mapping land use and land cover across more than 80,000 mining areas, Zenodo, https://doi.org/10.5281/zenodo.15726306 (2025). 1.

Esri. World Continents. ArcGIS Data and Maps, Feature Layer. Last updated: 3 Jan 2024. Available at https://hub.arcgis.com (accessed 5 Jul 2025) (2024). 1.

Werner, T. T. et al. Data for: Global-scale mapping and prediction of mine areas. Mendeley Data https://doi.org/10.17632/hbmmzjn3xn.1 (2019).

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Acknowledgements

This work was supported by the Environment Research and Technology Development Fund (JPMEERF20234004) and Frontier Research in Duo, Tohoku University.

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Authors and Affiliations

Graduate School of Environmental Studies, Tohoku University, 468-1 Aramaki Aza Aoba, Aoba-ku, Sendai, Miyagi, 980-8572, Japan

Yu-Tong Cheng, Nguyen Tien Hoang, Lou Maupu & Keiichiro Kanemoto

Authors

1. Yu-Tong Cheng
2. Nguyen Tien Hoang
3. Lou Maupu
4. Keiichiro Kanemoto

Contributions

Y.-T.C.: Conceptualization, Methodology, Data curation, Investigation, Formal analysis, Software, Validation, Visualization, Writing (original draft preparation), Writing (review and editing). N.T.H.: Conceptualization, Methodology, Validation, Resources, Writing (review and editing). L.M.: Data curation, Investigation, Methodology, Formal analysis, Software, Visualization. K.K.: Conceptualization, Methodology, Supervision, Funding acquisition, Validation, Resources, Writing (review and editing).

Corresponding author

Correspondence to Keiichiro Kanemoto.

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Cheng, YT., Hoang, N.T., Maupu, L. et al. Classifying land use within 80,000 mining sites on a global scale. Sci Data (2026). https://doi.org/10.1038/s41597-026-06681-x

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Received: 13 August 2025

Accepted: 22 January 2026

Published: 03 February 2026

DOI: https://doi.org/10.1038/s41597-026-06681-x