In the following sections, we explain our approach to updating global mine area data and provide a series of spatial analyses. It therefore offers an advanced spatial dataset that progresses toward comprehensive and refined mapping of global mining land use. Below, we outline an update that builds on these efforts to provide a mine area polygon dataset significantly larger (by the number of polygons) than any previous study. 18 contributed around 44,929 polygons of global mining areas. ![]() The most recent of these efforts by Maus et al. These studies have employed slightly different approaches to classifying mine areas, yet collectively have improved the global coverage of sites mapped and sought to increase the precision of estimates for already mapped areas. 7 have recently employed manual visual inspection methods to delineate mines as polygons. On a global scale, studies like Tang et al. However, such studies typically focus on single regions, as the environments in which mines operate can be so variable that applying a singular visual approach or training dataset entails considerable uncertainty between different mining regions. 17 have sought to use image processing algorithms to automatically identify and delineate mine areas, typically based on an already known dataset of mine areas. Typically, it requires visual inspection or machine learning analysis of satellite imagery, followed by validation of mapped areas using alternative satellite imagery, corporate data, and/or field investigations. However, delineating the complete area occupied by mines at each coordinate is a comparatively more complex endeavor. Global-scale studies have often relied on global corporate mining databases such as the Standard & Poor’s SNL metals and mining database 13 and review studies in economic geology (e.g., 14, 15) that provide data on the coordinates of larger-scale mines. Research on the spatial patterns of mining can be mine- or region-specific 6, 10, or may examine mining as a global geographic phenomenon 11, 12. They can also permit more nuanced planning of future developments in mining regions, for example, by informing the likely scale of future mine developments when mineral discoveries are made. These mapping exercises foster a more sophisticated understanding of the scale and location of risks posed by mining activity from local to global scales. A subset of this research uses GIS and remote sensing methods to map the extent of land transformation due to mining activity 3, 4, 5, 6, 7, 8, 9. As mine areas continue their expansion across the globe, there is an increasing need for research that identifies impacts on surrounding landscapes. They simultaneously promise economic and social development and are essential to many key supply chains 1, 2. A series of spatial analyses are also presented that highlight global mine distribution patterns and broader environmental risks.Įxtractive industries can dramatically alter landscapes and cause irreversible damage to surrounding environments and communities. ![]() Our database is made freely available to support future studies of global mining impacts. Hence, despite our database being the largest to date by number of polygons, comparisons show relatively lower global land use. This distinguishes our dataset from others that employ broader definitions of mining lands. Our polygons finely contour the edges of mine features and do not include the space between them. The dataset comprises 74,548 polygons, covering ~66,000 km 2 of features like waste rock dumps, pits, water ponds, tailings dams, heap leach pads and processing/milling infrastructure. Here, we produce a global mining land use dataset via remote sensing analysis of high-resolution, publicly available satellite imagery. Mining is of major economic, environmental and societal consequence, yet knowledge and understanding of its global footprint is still limited.
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