Machine learning-enhanced monitoring of global copper mining areas
Abstract Copper is one of the most critical minerals for the global transition to low-carbon energy. However, as copper mining activities expand worldwide, they often result in significant environmental impacts, yet the monitoring approaches and up-to-date databases remain limited. In this study, we...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05296-y |
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| author | Houxuan Li Peng Wang Tim T. Werner Bin Chen Wei-Qiang Chen |
| author_facet | Houxuan Li Peng Wang Tim T. Werner Bin Chen Wei-Qiang Chen |
| author_sort | Houxuan Li |
| collection | DOAJ |
| description | Abstract Copper is one of the most critical minerals for the global transition to low-carbon energy. However, as copper mining activities expand worldwide, they often result in significant environmental impacts, yet the monitoring approaches and up-to-date databases remain limited. In this study, we present a high-resolution, site-specific database of global copper mining activities, developed using a machine learning approach that leverages Earth observation images and various dispersed data sources. Our database encompasses approximately 1,313 copper mines, covering an area of 7,267 km2, and includes detailed monitoring of operational land use categories such as open pits, waste rock dumps, and tailings storage facilities as of 2022. Additionally, we analyse land use intensity at each mine site based on inferences of copper production levels to facilitate comprehensive comparisons and improved management strategies. This database can help to reveal the adverse impacts of copper mining behind the energy transition. The dataset is available for download from https://doi.org/10.6084/m9.figshare.28680863.v1 . |
| format | Article |
| id | doaj-art-96b5ebcf70f94656b98313081a441e33 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-96b5ebcf70f94656b98313081a441e332025-08-20T03:45:45ZengNature PortfolioScientific Data2052-44632025-07-0112111410.1038/s41597-025-05296-yMachine learning-enhanced monitoring of global copper mining areasHouxuan Li0Peng Wang1Tim T. Werner2Bin Chen3Wei-Qiang Chen4Institute of Urban Environment, Chinese Academy of SciencesInstitute of Urban Environment, Chinese Academy of SciencesSchool of Geography, University of MelbourneDepartment of Environment Science and Technology, Fudan UniversityInstitute of Urban Environment, Chinese Academy of SciencesAbstract Copper is one of the most critical minerals for the global transition to low-carbon energy. However, as copper mining activities expand worldwide, they often result in significant environmental impacts, yet the monitoring approaches and up-to-date databases remain limited. In this study, we present a high-resolution, site-specific database of global copper mining activities, developed using a machine learning approach that leverages Earth observation images and various dispersed data sources. Our database encompasses approximately 1,313 copper mines, covering an area of 7,267 km2, and includes detailed monitoring of operational land use categories such as open pits, waste rock dumps, and tailings storage facilities as of 2022. Additionally, we analyse land use intensity at each mine site based on inferences of copper production levels to facilitate comprehensive comparisons and improved management strategies. This database can help to reveal the adverse impacts of copper mining behind the energy transition. The dataset is available for download from https://doi.org/10.6084/m9.figshare.28680863.v1 .https://doi.org/10.1038/s41597-025-05296-y |
| spellingShingle | Houxuan Li Peng Wang Tim T. Werner Bin Chen Wei-Qiang Chen Machine learning-enhanced monitoring of global copper mining areas Scientific Data |
| title | Machine learning-enhanced monitoring of global copper mining areas |
| title_full | Machine learning-enhanced monitoring of global copper mining areas |
| title_fullStr | Machine learning-enhanced monitoring of global copper mining areas |
| title_full_unstemmed | Machine learning-enhanced monitoring of global copper mining areas |
| title_short | Machine learning-enhanced monitoring of global copper mining areas |
| title_sort | machine learning enhanced monitoring of global copper mining areas |
| url | https://doi.org/10.1038/s41597-025-05296-y |
| work_keys_str_mv | AT houxuanli machinelearningenhancedmonitoringofglobalcopperminingareas AT pengwang machinelearningenhancedmonitoringofglobalcopperminingareas AT timtwerner machinelearningenhancedmonitoringofglobalcopperminingareas AT binchen machinelearningenhancedmonitoringofglobalcopperminingareas AT weiqiangchen machinelearningenhancedmonitoringofglobalcopperminingareas |