  This work explores the combination of free cloud computing, free open-source
software, and deep learning methods to analyse 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 API and Google 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. Code is made publicly available at:
https://github.com/remis/mining-discovery-with-deep-learning.
