Extraction of Step-Feature Lines in Open-Pit Mines Based on UAV Point-Cloud Data

Area of Science:

• Geosciences
• Mining Engineering
• Geomatics

Background:

• Step-feature lines are crucial for mapping open-pit mines, impacting stripping planning and analysis.
• Current methods for extracting these lines often suffer from low accuracy, feature loss, and discontinuity.
• Efficient and accurate extraction is vital for operational planning and safety.

Purpose of the Study:

• To propose an automated method for extracting step-feature lines from open-pit mine point-cloud data.
• To address and overcome limitations of existing methods, specifically low accuracy, local feature loss, and discontinuity.
• To improve the reliability and efficiency of geological feature extraction in mining environments.

Main Methods:

• Utilized unmanned-aerial-vehicle (UAV) point-cloud data for extraction.
• Applied Moving Least Squares (MLS) algorithm for regular raster resampling of point clouds.
• Detected elevation-gradient changes to extract step-feature points.
• Employed seed-growth tracking algorithm to trace step-feature control nodes.
• Generated smooth step-feature lines by fitting space curves to control nodes.

Main Results:

• The proposed method significantly improved the accuracy of step-feature line extraction.
• Successfully addressed and resolved issues of local feature line loss.
• Overcame the problem of discontinuity in extracted step-feature lines.
• Demonstrated the effectiveness of the UAV point-cloud-based approach.

Conclusions:

• The developed method provides an effective solution for accurate and continuous step-feature line extraction in open-pit mines.
• This advancement is highly significant for open-pit mine stripping planning and analysis.
• The integration of UAV technology and advanced algorithms offers a robust approach to geological feature mapping.