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