Client Background:

Client is a multinational metals and mining corporation, producing iron ore, copper, diamonds, gold and uranium


Business Objective:
  • In order to meet current or future demand and explore new business opportunities, client makes efforts to find new mineral sites
  • Client wants to process remote sensing image data to identify new mineral occurrence locations (of commercial interest)



 We developed a Machine Learning model to identify new mineral occurrence locations by scoring sites on likelihood (using band ratios and other such combinations of band reflectance values)

Key steps involved:

  • Re-factor code to python and access AWS processing infrastructure
  • Test and validate re-factored python code in current process
  • Information extraction on RS scenes to create prediction variables
  • Use the information gained from the above analysis to create Machine Learning model
  • Validate Machine Learning model against hold out data
  • Score incoming RS scene data to identify new potential mineral sites



Our solution achieved an accuracy of 95% on round 1 of Images.