From Manual Estimation to Point Cloud Calculation: SHARE SLAM S100 Provides Accurate Coal Stockpile Volume Data

Handheld 3D laser scanning captures stockpile point clouds efficiently, supporting inventory management, production scheduling, and cost accounting with measurable data.

A SHARE3DCAM technician scans a coal stockpile on site using SHARE SLAM S100.

Project Overview: Solving Coal Stockpile Volume Measurement with 3D Scanning

In bulk material management scenarios such as coal, ore, sand, and aggregates, stockpile volume calculation is a critical part of inventory management. For logistics companies, coal stockpiles are often large, irregular in shape, and located in complex site environments. Quickly and accurately understanding the remaining stockpile volume directly supports weight estimation, production scheduling, transportation planning, and cost accounting.

Traditional stockpile measurement methods often rely on manual experience. Field data collection can be slow, labor-intensive, and exposed to potential safety risks. When dealing with large and irregular stockpiles, manual estimation or conventional surveying methods often struggle to balance efficiency, accuracy, and data traceability.

For this project, the SHARE3DCAM technical team used the SHARE SLAM S100 handheld 3D laser scanner in Zhenjiang, Jiangsu, to support coal stockpile volume measurement for a logistics company. By rapidly capturing high-precision 3D point cloud data of the stockpile surface and processing it with volume calculation software, the project helped the customer move toward more digitalized and accurate inventory management.

Project Background: Coal Inventory Management Requires Faster Data Acquisition

In logistics warehousing and energy material management, coal inventory usually needs to be checked regularly. Inventory data is not only used to understand remaining material on site, but also supports weight estimation, production planning, transport scheduling, and cost control.

However, coal stockpiles are naturally irregular. The height, slope, boundary, and surface coverage of each pile can vary significantly. On site, teams may also face large pile areas, unclear edges, and limited accessibility for close-range manual work.

When conventional methods are used for volume statistics, teams often encounter slow data acquisition, measurement results affected by operator experience, heavy field workload, and insufficient data reviewability. Therefore, the customer needed a faster, more intuitive, and more verifiable stockpile measurement approach to provide reliable data for coal volume statistics and inventory management.

Project Challenges: Conventional Surveying and Drone Mapping Both Have Practical Limitations

The difficulty of coal stockpile volume measurement lies not only in irregular stockpile geometry, but also in the practical efficiency of field data acquisition. Traditional surveying with fixed stations typically requires equipment setup, station selection, and multiple point measurements. The workflow can be relatively heavy and highly dependent on operator experience. For large stockpiles, field time, manpower input, and on-site safety pressure all increase accordingly.

In addition to manual station-based surveying, drone photogrammetry is also commonly used for stockpile measurement. In real-world projects, however, drone operations are often affected by airspace approval, site-specific no-fly or restricted-flight conditions, and the need for trained operators. In situations with no available airspace or no certified pilot, project teams may need to spend significant time on airspace applications, pilot coordination, or flight operation training. This increases workflow cost and may still fail to meet the customer's need for rapid inventory checks and timely measurement results.

For this project, a lighter, more flexible, and less site-restricted data acquisition method was required. The solution needed to maintain point cloud completeness and verifiable volume calculation while improving field efficiency and lowering the threshold for site organization and personnel deployment.

Solution: Capturing 3D Point Clouds with SHARE SLAM S100

To meet the coal stockpile volume measurement needs of the Zhenjiang project, the SHARE3DCAM technical team used the SHARE SLAM S100 handheld 3D laser scanner for data acquisition.

During the field operation, the operator simply held the device, aimed it at the target stockpile, and walked around the pile once to rapidly capture the 3D point cloud of its surface. Compared with conventional surveying, this workflow does not require complex station setup or long periods of manual point collection around the stockpile, significantly reducing field workload.

With a scanning rate of up to 640,000 points per second, SHARE SLAM S100 can quickly capture the surface geometry of coal stockpiles during mobile scanning and generate clear, complete point cloud results. Through real-time point cloud visualization on a mobile device, the field team can also check scanning coverage while working and identify any missing areas in time, improving data completeness and controllability.

The coal stockpile point cloud clearly presents the surface shape of the pile and its surrounding environment.

Operational Efficiency: Scanning a 2,500 m2 Stockpile in Under 7 Minutes

In the Zhenjiang coal stockpile project, each target stockpile covered approximately 2,500 square meters. Using SHARE SLAM S100 for handheld scanning, a single stockpile could be scanned in under 7 minutes.

The project team completed scanning of three target stockpiles in less than 20 minutes. For logistics companies that require regular inventory checks and rapid volume statistics, this level of efficiency can significantly improve on-site management workflows.

After scanning, the technical team cropped the valid stockpile point cloud, removed non-target areas, and processed the data with volume calculation software. The resulting point cloud model preserves the true surface geometry of the coal stockpile and can also be archived as a digital record for comparing stockpile changes over time.

Volume Calculation Results: Three Stockpile Volumes Output Quickly

Based on the point cloud data collected on site, the technical team calculated the volumes of three stockpiles. The results are shown below:

These volume results can be combined with coal density data to estimate the remaining stockpile weight, providing a basis for inventory statistics, production scheduling, and cost accounting.

Compared with manual estimation alone, 3D point cloud data not only outputs volume results, but also preserves the complete spatial shape of the stockpile. The customer can later review, compare, and archive the data, forming a more visual and traceable inventory management record.

Top-view point cloud results can be used for stockpile boundary identification, cropping, and subsequent volume calculation.

Surface Filling and Volume Calculation Results

During point cloud processing, the technical team cropped the valid coal stockpile area and processed the pile surface and boundaries to generate models suitable for volume calculation. The following placeholders correspond to the surface filling results of the three target stockpiles.

Coal Stockpile 1 surface filling result.

Coal Stockpile 2 surface filling result.

Coal Stockpile 3 surface filling result.

Project Value: Moving Stockpile Measurement from Manual Experience to Digital Statistics

1. Higher Efficiency in Coal Stockpile Volume Measurement

With SHARE SLAM S100, the operator can complete data acquisition by walking around the target stockpile once. Scanning a stockpile of approximately 2,500 square meters in under 7 minutes greatly reduces field acquisition time.

2. Reduced Field Workload and Safety Risk

Traditional measurement often requires personnel to work around the stockpile for an extended period. Handheld scanning is more flexible and reduces the time spent performing complex measurements near the pile, helping lower field workload and safety risks.

3. Complete and Verifiable Point Cloud Results

The generated point cloud can be used not only for the current volume calculation, but also as a digital project archive. The customer can compare stockpile data from different periods to better understand inventory changes.

4. Support for Weight Estimation and Cost Accounting

With volume calculation results, the customer can further estimate inventory weight based on coal density, supporting production scheduling, transportation planning, financial accounting, and operational management.

5. Applicable to Coal, Ore, Sand, Aggregates, and Other Bulk Materials

Beyond coal stockpiles, this scanning workflow can also be applied to ore, sand, aggregates, earthwork, and raw material yards, providing a digital tool for bulk material management.

Conclusion

For logistics companies and bulk material management scenarios, stockpile volume measurement is not just a one-time surveying task. It is a key foundation for digital inventory management. Especially when traditional station-based surveying is limited in efficiency and drone mapping may be constrained by airspace approval or pilot availability, a flexible ground-based 3D scanning solution can shift the workflow from asking whether the site can fly, who can fly, and how long approval will take, to a much simpler process: arrive, scan, calculate, and deliver results.

In the Zhenjiang coal stockpile project, SHARE SLAM S100 helped the customer complete point cloud acquisition and volume calculation for multiple stockpiles within a short period of time. The point cloud data made volume statistics more efficient and intuitive, while also supporting inventory management, production scheduling, cost accounting, and long-term data archiving.

From manual estimation and station-based surveying, to drone mapping constrained by airspace and pilot conditions, and now to handheld 3D point cloud acquisition, stockpile volume measurement is moving toward a lighter, more efficient, and more traceable digital workflow. SHARE3DCAM will continue to help coal, ore, sand, aggregate, and other material yards improve inventory management efficiency and build reviewable spatial data assets.