Li Houxuan, Wang Peng, Werner Tim T, Chen Bin, Chen Wei-Qiang
Institute of Urban Environment, Chinese Academy of Sciences, 1799 Jimei Road, Xiamen, 361021, China.
School of Geography, University of Melbourne, 221 Bouverie Street, Carlton, VIC, 3010, Australia.
Sci Data. 2025 Jul 1;12(1):1120. doi: 10.1038/s41597-025-05296-y.
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 km, 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 .
铜是全球向低碳能源转型过程中最重要的矿物之一。然而，随着全球铜矿开采活动的扩大，这些活动往往会造成重大环境影响，而监测方法和最新数据库仍然有限。在本研究中，我们展示了一个高分辨率、特定地点的全球铜矿开采活动数据库，该数据库是使用机器学习方法开发的，该方法利用了地球观测图像和各种分散的数据源。我们的数据库涵盖了大约1313座铜矿，面积达7267平方公里，并且包括截至2022年对露天矿、废石堆和尾矿储存设施等运营土地使用类别的详细监测。此外，我们根据铜产量水平的推断分析每个矿场的土地使用强度，以促进全面比较并改进管理策略。该数据库有助于揭示能源转型背后铜矿开采的不利影响。数据集可从https://doi.org/10.6084/m9.figshare.28680863.v1下载。
