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Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning.

Remis Balaniuk1, Olga Isupova2, Steven Reece3

  • 1Graduate Program in Governance, Technology and Innovation, Universidade Católica de Brasília, Brasília 71966-700, Brazil.

Sensors (Basel, Switzerland)
|December 9, 2020
PubMed
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This study used free cloud computing and deep learning to automatically identify surface mines and tailings dams across Brazil. The method successfully discovered 263 unregistered mines, highlighting potential for low-cost, high-impact data science tools.

Area of Science:

  • Earth Observation
  • Artificial Intelligence
  • Data Science

Background:

  • Illegal mining and proliferation of tailings dams pose significant environmental and social risks, particularly in developing nations.
  • Accurate, large-scale monitoring of mining activities is crucial for environmental protection and public safety.

Purpose of the Study:

  • To develop and demonstrate a cost-effective, automated system for identifying and classifying surface mines and tailings dams nationwide in Brazil.
  • To leverage free cloud computing, open-source software, and deep learning for a scalable solution to monitor mining infrastructure.
  • To identify unregistered mining operations and assess the prevalence of potential environmental hazards.

Main Methods:

  • Utilized Google Earth Engine for processing Sentinel-2 multispectral satellite imagery.
Keywords:
cloud computingdeep learningenvironmental impact of miningmachine learningremote sensingsurface mines detectiontailings dam detection

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  • Employed deep learning models, specifically fully convolutional neural networks, trained using TensorFlow 2 on the Google Colaboratory platform.
  • Integrated publicly available geospatial data from the Brazilian government for registered mine and dam locations.

    • Main Results:

    • Successfully identified and classified surface mines and mining tailings dams across Brazil.
    • Discovered 263 previously unregistered mines, demonstrating the system's efficacy in detecting undeclared mining concessions.
    • Validated the potential of freely available technologies for creating impactful data science tools.

    Conclusions:

    • The combination of free cloud computing, open-source software, and deep learning offers a powerful, low-cost approach for environmental monitoring.
    • This methodology provides a scalable solution for identifying illegal mining and managing risks associated with tailings dams.
    • The study underscores the potential for technology to address critical environmental and social issues in developing countries.