Not logged in
Data Publisher for Earth & Environmental Science

Maus, Victor; da Silva, Dieison M; Gutschlhofer, Jakob; da Rosa, Robson; Giljum, Stefan; Gass, Sidnei L B; Luckeneder, Sebastian; Lieber, Mirko; McCallum, Ian (2022): Global-scale mining polygons (Version 2). PANGAEA,

Always quote citation above when using data! You can download the citation in several formats below.

RIS CitationBibTeX Citation

This dataset updates the global-scale mining polygons (Version 1) available from It contains 44,929 polygon features, covering 101,583 km² of land used by the global mining industry, including large-scale and artisanal and small-scale mining. The polygons cover all ground features related to mining, .e.g open cuts, tailing dams, waste rock dumps, water ponds, processing infrastructure, and other land cover types related to the mining activities. The data was derived using a similar methodology as the first version by visual interpretation of satellite images. The study area was limited to a 10 km buffer around the 34,820 mining coordinates reported in the S&P metals and mining database. We digitalized the mining areas using the 2019 Sentinel-2 cloudless mosaic with 10 m spatial resolution ( by EOX IT Services GmbH - Contains modified Copernicus Sentinel data 2019). We also consulted Google Satellite and Microsoft Bing Imagery, but only as additional information to help identify land cover types linked to the mining activities. The main data set consists of a GeoPackage (GPKG) file, including the following variables: ISO3_CODE<string>, COUNTRY_NAME<string>, AREA<double> in squared kilometres, FID<integer> with the feature ID, and geom<polygon> in geographical coordinates WGS84. The summary of the mining area per country is available in comma-separated values (CSV) file, including the following variables: ISO3_CODE<string>, COUNTRY_NAME<string>, AREA<double> in squared kilometres, and N_FEATURES<integer> number of mapped features. Grid data sets with the mining area per cell were derived from the polygons. The grid data is available at 30 arc-second resolution (approximately 1x1 km at the equator), 5 arc-minute (approximately 10x10 km at the equator), and 30 arc-minute resolution (approximately 55x55 km at the equator). We performed an independent validation of the mining data set using control points. For that, we draw 1,000 random samples stratified between two classes: mine and no-mine. The control points are also available as a GPKG file, including the variables: MAPPED<string>, REFERENCE<string>, FID<integer> with the feature ID, and geom<point> in geographical coordinates WGS84. The overall accuracy calculated from the control points was 88.3%, Kappa 0.77, F1 score 0.87, producer's accuracy of class mine 78.9 % and user's accuracy of class mine 97.2 %.
coal; Land-cover; land-use; metal ores; minerals; Mining; raw material extraction
Horizon 2020 (H2020), grant/award no. 725525: Spatially explicit material footprints: fine-scale assessment of Europe's global environmental and social impacts
#NameShort NameUnitPrincipal InvestigatorMethod/DeviceComment
1File contentContentMaus, Victor
2Binary ObjectBinaryMaus, Victor
3Binary Object (Media Type)Binary (Type)Maus, Victor
4Binary Object (File Size)Binary (Size)BytesMaus, Victor
Curation Level: Basic curation (CurationLevelB)
12 data points

Download Data

Download dataset as tab-delimited text — use the following character encoding:

View dataset as HTML