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      A global-scale data set of mining areas

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          Abstract

          The area used for mineral extraction is a key indicator for understanding and mitigating the environmental impacts caused by the extractive sector. To date, worldwide data products on mineral extraction do not report the area used by mining activities. In this paper, we contribute to filling this gap by presenting a new data set of mining extents derived by visual interpretation of satellite images. We delineated mining areas within a 10  km buffer from the approximate geographical coordinates of more than six thousand active mining sites across the globe. The result is a global-scale data set consisting of 21,060 polygons that add up to 57,277  km 2. The polygons cover all mining above-ground features that could be identified from the satellite images, including open cuts, tailings dams, waste rock dumps, water ponds, and processing infrastructure. The data set is available for download from 10.1594/PANGAEA.910894 and visualization at www.fineprint.global/viewer.

          Abstract

          Measurement(s) terrestrial mining
          Technology Type(s) satellite imaging
          Sample Characteristic - Environment land
          Sample Characteristic - Location Earth (planet)

          Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12594248

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          • Record: found
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          Simple Features for R: Standardized Support for Spatial Vector Data

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            • Record: found
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            • Article: not found

            Random forest in remote sensing: A review of applications and future directions

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              • Record: found
              • Abstract: not found
              • Article: not found

              Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity

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                Author and article information

                Contributors
                victor.maus@wu.ac.at
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                8 September 2020
                8 September 2020
                2020
                : 7
                : 289
                Affiliations
                [1 ]GRID grid.15788.33, ISNI 0000 0001 1177 4763, Institute for Ecological Economics, , Vienna University of Economics and Business (WU), ; Vienna, Austria
                [2 ]GRID grid.75276.31, ISNI 0000 0001 1955 9478, Ecosystems Services and Management, , International Institute for Applied Systems Analysis (IIASA), ; Laxenburg, Austria
                [3 ]GRID grid.412376.5, ISNI 0000 0004 0387 9962, Federal University of Pampa (UNIPAMPA), ; Itaqui, Brazil
                Author information
                http://orcid.org/0000-0002-7385-4723
                http://orcid.org/0000-0002-4719-5867
                http://orcid.org/0000-0002-5370-8358
                http://orcid.org/0000-0001-5281-8427
                http://orcid.org/0000-0001-7166-9260
                http://orcid.org/0000-0001-5197-7506
                http://orcid.org/0000-0001-6735-301X
                Article
                624
                10.1038/s41597-020-00624-w
                7478970
                31896794
                a0b40ecd-2cbd-48e4-a85d-f61785e33312
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.

                History
                : 11 March 2020
                : 6 August 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 725525
                Award ID: 725525
                Award ID: 725525
                Award ID: 725525
                Award ID: 725525
                Award Recipient :
                Categories
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                © The Author(s) 2020

                sustainability,environmental economics,environmental impact

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