Stockpile management has long been a challenge in mining operations. Knowing exactly how much material sits in a stockpile, its grade distribution, and its physical characteristics affects everything from processing decisions to financial reporting. Computer vision is providing new solutions.

The Stockpile Challenge

Traditional stockpile management relies on periodic surveys and calculated estimates:

Survey frequency: Drone or ground surveys might occur weekly or monthly. Between surveys, stockpile volumes change continuously with additions and withdrawals.

Grade uncertainty: Samples represent tiny fractions of stockpiled material. Grade estimates carry significant uncertainty, particularly for heterogeneous stockpiles.

Segregation effects: As material is added and removed, size and grade segregation occurs. Stockpile composition becomes spatially variable.

Reconciliation gaps: Differences between estimated and actual stockpile values create reconciliation challenges that affect mine planning and financial reporting.

These challenges have been accepted as unavoidable limitations. Computer vision is changing that assumption.

Visual Monitoring Systems

Modern computer vision systems for stockpile management combine multiple technologies:

Camera networks: Fixed cameras around stockpiles provide continuous visual monitoring. Multiple viewing angles enable comprehensive coverage.

LiDAR integration: Laser scanning provides precise volumetric data. Combined with visual data, this enables detailed 3D modelling.

Drone surveys: Automated drone flights supplement fixed infrastructure with high-resolution imagery and photogrammetry.

Edge processing: Analysis at the camera enables real-time insights without overwhelming network bandwidth.

The combination provides continuous, accurate understanding of stockpile status.

What Computers Can See

Computer vision algorithms extract information that would be difficult or impossible for humans to track:

Volume changes: Continuous monitoring tracks every loader bucket, every truck tip, every reclaimer pass. Cumulative volume calculations are precise.

Particle size distribution: Visual analysis of surface material estimates size distribution without physical sampling. This information supports processing decisions.

Material classification: Trained models can distinguish ore types, waste, and other materials based on visual characteristics.

Movement patterns: Understanding where material is placed and removed supports grade tracking through stockpiles.

Moisture estimation: Surface appearance correlates with moisture content. Visual analysis provides ongoing moisture estimates.

Grade Tracking Applications

Perhaps the most valuable application is improved grade tracking:

Spatial grade mapping: By tracking where specific truck loads are placed, systems build spatial models of grade distribution within stockpiles.

Blending optimisation: Understanding grade distribution enables strategic reclaim to achieve target blend grades for processing.

FIFO/LIFO management: Systems can identify oldest material for processing, important for oxidation-sensitive ores.

Contamination tracking: If problematic material enters a stockpile, systems can identify where it went for targeted removal.

Integration with Operations

Effective stockpile management requires integration with broader mining systems:

Fleet management: Truck destinations link production data with stockpile locations.

Laboratory systems: Assay results tie to specific loads and locations.

Processing plant: Reclaim data feeds back to update stockpile models as material is withdrawn.

Mine planning: Updated stockpile information improves planning accuracy.

The result is dynamic stockpile models that update continuously based on real operations.

Implementation Considerations

Deploying computer vision for stockpile management requires attention to several factors:

Environmental conditions: Dust, weather, and lighting affect visual analysis. Systems must handle variable conditions robustly.

Camera positioning: Coverage design ensures all relevant areas are visible. Maintenance access and protection from mining activities matter.

Processing infrastructure: Real-time analysis requires significant computation. Edge computing or robust connectivity to central processing is necessary.

Data architecture: Integrating vision data with existing operational systems requires careful planning.

Calibration and validation: Systems must be calibrated against surveyed volumes and validated through reconciliation studies.

Benefits Realised

Operations implementing computer vision stockpile management report various benefits:

Inventory accuracy: Continuous monitoring reduces variance between estimated and actual stockpile values.

Processing improvement: Better grade knowledge enables more consistent feed to processing plants, improving recovery and efficiency.

Reduced surveying costs: While surveys remain necessary for validation, frequency can decrease when continuous monitoring provides intermediate data.

Better reconciliation: Improved stockpile tracking reduces reconciliation variances between production, stockpiles, and processing.

Decision support: Real-time stockpile visibility enables better operational decisions about material routing and processing priorities.

Technology Evolution

Computer vision for mining applications continues to advance:

Multi-spectral imaging: Cameras capturing beyond visible light can identify mineral characteristics invisible to standard cameras.

3D reconstruction: Improved algorithms create detailed surface models from camera images alone, reducing LiDAR dependency.

Deep learning models: Neural networks trained on mining-specific data improve accuracy for challenging conditions and materials.

Autonomous integration: Vision systems increasingly feed autonomous equipment, enabling closed-loop material management.

Beyond Stockpiles

The same technologies apply to other mining visual analysis challenges:

Haul road condition: Visual analysis identifies road defects requiring maintenance.

Bench face analysis: Wall stability assessment from visual and LiDAR data.

Equipment condition: Visual inspection of equipment for damage or wear.

Safety monitoring: Detection of personnel in hazardous areas or unsafe conditions.

Computer vision is becoming a general-purpose tool for mining operations, with stockpile management as one of many applications.

Looking Forward

The future of stockpile management lies in fully integrated digital twins – complete virtual representations of stockpiles that update in real time and enable simulation of future operations.

These systems will optimise material handling automatically, routing trucks to locations that will facilitate optimal future blending. Processing plants will know what’s coming before reclaim begins.

This vision requires continued improvement in sensing technology, processing capability, and integration with operational systems. The building blocks are in place; the work of putting them together continues.