Classifying land use within 80,000 mining sites on a global scale
Yu-Tong Cheng1, Nguyen Tien Hoang1, Lou Maupu1
1Graduate School of Environmental Studies, Tohoku University, 468-1 Aramaki Aza Aoba, Aoba-ku, Sendai, Miyagi, 980-8572, Japan.
Scientific Data
|February 3, 2026
Summary
A new global dataset maps mining land use and cover, crucial for understanding the environmental impact of critical mineral extraction during the green energy transition. This detailed mapping improves environmental risk assessments.
Area of Science:
- Environmental Science
- Remote Sensing
- Geospatial Analysis
Background:
- The green energy transition increases demand for critical minerals, leading to expanded mining activities and environmental concerns.
- Accurate assessment of the mining footprint is essential for understanding and mitigating environmental consequences.
Purpose of the Study:
- To develop a globally consistent dataset for land use and land cover classification within mining areas.
- To provide detailed insights into the global mining footprint and improve environmental impact assessments.
Main Methods:
- Integration of Sentinel-2 satellite imagery and TanDEM-X elevation change data.
- Utilisation of a Random Forest classifier for accurate land use classification.
- Differentiating spectrally similar but functionally different land use types (e.g., open pits, waste dumps).
Main Results:
- A comprehensive dataset covering over 80,000 mining extents across 150 countries (95,644 km²).
- Accurate classification distinguishing specific mining land uses, avoiding overestimation of impacted areas.
- Enabled global-scale insights into the mining footprint.
Conclusions:
- The dataset offers a more accurate depiction of land use within mining areas.
- Improved reliability of environmental impact assessments in the mining sector.
- Supports informed decision-making for sustainable resource management and environmental protection.
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