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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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A global-scale data set of mining areas.

Victor Maus1,2, Stefan Giljum3, Jakob Gutschlhofer3

  • 1Institute for Ecological Economics, Vienna University of Economics and Business (WU), Vienna, Austria. victor.maus@wu.ac.at.

Scientific Data
|September 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new global dataset of mining extents, mapping over 21,000 mining areas. This crucial data helps assess environmental impacts from mineral extraction activities.

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Mineral extraction's environmental impact necessitates accurate land use data.
  • Existing global datasets lack comprehensive information on mining-affected areas.
  • Understanding mining extents is vital for environmental mitigation strategies.

Purpose of the Study:

  • To create a novel global dataset of mining extents.
  • To address the gap in worldwide data products on areas used by mining activities.
  • To provide a resource for environmental impact assessment of the extractive sector.

Main Methods:

  • Visual interpretation of satellite imagery.
  • Delineation of mining areas within a 10 km buffer around 6,000+ active mining sites globally.
  • Creation of 21,060 polygons representing mining features.

Main Results:

  • A global dataset of 21,060 mining polygons covering 57,277 km².
  • Inclusion of diverse above-ground mining features (open cuts, tailings dams, etc.).
  • Data available for download and online visualization.

Conclusions:

  • The new dataset significantly enhances the understanding of mining's spatial footprint.
  • This resource is critical for environmental monitoring and sustainable resource management.
  • Facilitates improved assessment and mitigation of the extractive sector's environmental consequences.