ABSTRACT
The definition between lithologies is vital for a successful mine planning. new field data is constantly collected to feed the reconciliation process. Whilst performing with the traditional approach, the professional is exposed to weather conditions, dust, risks with the moving equipment and bench highs. This task can benefit from the automation of boundaries detection between materials. The use of Unmanned Aerial Vehicles (UAVs) and automated classification using Machine Learning (ML) techniques improved in the last few years. However, non-visible wavelengths are still rarely used for materials classification. Other electromagnetic frequencies can be captured by specific sensors so the automatization process could use these data as training and test in a supervised classification. The characterization of materials that are similar in visible wavelengths can be useful to understand the behavior of the electromagnetic bands to clearly differentiate materials, so the correct sensor can be plugged onto the aerial vehicle.
