Mining and tailings dam detection in satellite imagery using deep learning

Remis Balaniuk, Olga Isupova, Steven Reece

Research output: Contribution to journalArticlepeer-review

30 Citations (SciVal)


This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyze a real, large-scale problem: the automatic country-wide identification and classification of surface mines and mining tailings dams in Brazil. Locations of officially registered mines and dams were obtained from the Brazilian government open data resource. Multispectral Sentinel-2 satellite imagery, obtained and processed at the Google Earth Engine platform, was used to train and test deep neural networks using the TensorFlow 2 application programming interface (API) and Google Colaboratory (Colab) platform. Fully convolutional neural networks were used in an innovative way to search for unregistered ore mines and tailing dams in large areas of the Brazilian territory. The efficacy of the approach is demonstrated by the discovery of 263 mines that do not have an official mining concession. This exploratory work highlights the potential of a set of new technologies, freely available, for the construction of low cost data science tools that have high social impact. At the same time, it discusses and seeks to suggest practical solutions for the complex and serious problem of illegal mining and the proliferation of tailings dams, which pose high risks to the population and the environment, especially in developing countries.

Original languageEnglish
Article number6936
Pages (from-to)1-26
Number of pages26
JournalSensors (Switzerland)
Issue number23
Publication statusPublished - 4 Dec 2020

Bibliographical note

Funding Information:
Funding: The authors would like to thank FAPDF-Brazil for their financial support to this research project, Google X, and the EPSRC for generous financial gifts and the LLoyd Register Foundation for funding through the Alan Turing Institute Data Centric Engineering programme. The article publication fee was covered by Oxford University’s UKRI (RCUK) Block Grant on the basis of the university’s open access policy.

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Copyright 2020 Elsevier B.V., All rights reserved.


  • Cloud computing
  • Deep learning
  • Environmental impact of mining
  • Machine learning
  • Remote sensing
  • Surface mines detection
  • Tailings dam detection

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


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