Automated lithological classification using UAV and machine learning on an open cast mine

ABSTRACT

Mine planning is directly dependent on the lithological features and the definition of contacts between materials. Geological modelling is a continual duty that is performed using observation data, which includes open faces information. New data must be continuously acquired and more details are added to the model. This task can benefit from the automation of lithological detection. Unmanned aerial vehicles (UAVs) are widely used in open pit mining projects, with low risk to the operators, to the aircraft or third parties. Topographic modelling using UAV imagery is now common in the mining industry. The next step, presented here, is to automate the surface feature detection using machine learning (ML) algorithms to classify a complete detailed geological model. An inexpensive aircraft was used on a Brazilian phosphate mine with point spacing as small as 10 cm.

KEYWORDS:

• UAV
• machine learning
• geological model
• mine planning
• automated modelling

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

F. Beretta http://orcid.org/0000-0002-1846-0465

A. L. Rodrigues http://orcid.org/0000-0003-4524-4087

R. L. Peroni http://orcid.org/0000-0003-0131-5832

J. F. C. L. Costa http://orcid.org/0000-0003-4375-370X

Additional information

Funding

This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.