In civil engineering and land development sites, soil volume calculations associated with excavation and embankment are indispensable. Traditionally, survey data were used to create cross-sections and volumes were calculated by methods such as the average end-area method, but recently, methods that calculate volumes by capturing the entire site in detail using 3D measurements from point cloud data have attracted attention. By photographing the site using photogrammetry and converting it into a point cloud model, you can intuitively visualize where and how much soil was removed by excavation or where soil was added by embankment. This article provides an introduction to using point cloud data for soil volume calculations: it explains specific procedures and precautions for generating point clouds with photogrammetry and calculating volumes. It also touches on improving positional accuracy with RTK positioning and the new smartphone-based technology LRTK, offering an outlook on the latest methods that allow anyone to perform simple, high-accuracy surveying.

Table of contents

• What photogrammetry and point cloud data are

• Advantages of using point clouds for soil volume calculations

• Procedure for soil volume calculation using photogrammetry point clouds

• Visualizing excavation and embankment: point cloud comparison and volume calculation

• Shaping the ground surface model and defining the reference plane

• Key points for higher accuracy: using RTK positioning and LRTK

• Summary

• Frequently Asked Questions (FAQ)

Photogrammetry and point cloud data

Photogrammetry is a technique that reconstructs the three-dimensional shape of an object from multiple photographs and generates 3D models or point cloud data. By taking photos of the site from various angles using drones, digital cameras, or smartphones and analyzing them with specialized software, you can obtain point cloud data — a large collection of points that make up the terrain or structures. Point cloud data contain coordinates (X, Y, Z) and color information for each point and can be visualized as precise 3D digital models representing the shape of the subject. For example, photogrammetry of a construction site’s ground surface can generate a detailed point cloud model that includes subtle irregularities in rocks and soil.

Advantages of using point clouds for soil volume calculations

In conventional soil volume calculations, surveyors measured heights at set intervals in the field and created cross-sections on drawings to compute volumes. This method required significant time and effort to measure wide areas and often relied on estimates where humans could not measure directly. In contrast, soil volume calculations using point cloud data obtained from photogrammetry model the terrain before and after excavation or embankment in 3D and compare the differences, allowing accurate capture of changes across the entire ground surface. Because point clouds can contain millions of points and are high-density, they enable high-precision calculations that account for fine surface variations. Also, once point cloud data are acquired, volumes can be computed through meshing, making it easy to recalculate soil volumes for additional sections without re-surveying the site. In practice, there are reports where a task that previously required four people several days to measure was completed in one day by creating a point cloud model from drone aerial photos and calculating volumes. Moreover, point cloud-based methods have been shown to produce results comparable to traditional methods, with field validations confirming high accuracy on the order of about 1% error. Introducing point cloud technology therefore offers the major benefits of improved safety and efficiency while maintaining high accuracy.

Procedure for soil volume calculation using photogrammetry point clouds

Now let’s look at the basic steps to perform soil volume calculations using point clouds obtained by photogrammetry. The following is an example of a typical workflow.

• Shooting plan and photography: First, plan the shooting of the target area. Take photos with high overlap to cover excavation and embankment areas with margin. When using a drone, automated flights from altitude are efficient; with handheld cameras or smartphones you can shoot by surrounding the subject from the ground. The key is to ensure sufficient overlap between photos so that the subject is captured from all directions. If the subject has few patterns or features, placing artificial markers to increase feature points is effective. Also, avoid blur and out-of-focus shots and acquire high-resolution, sharp images, as this contributes to generating high-precision point clouds.

• Generating point cloud data (photogrammetry software): Import the many photos taken into dedicated photogrammetry software to generate point cloud data and 3D models. The software analyzes corresponding feature points across photos and reconstructs point clouds consisting of millions of points using the principle of triangulation. Using a high-performance PC or cloud services, large numbers of images can be processed in a relatively short time. The generated point cloud is often expressed in a relative coordinate system, so scaling and alignment are performed in the next step.

• Alignment to survey coordinates (georeferencing): Align the point cloud model obtained by photogrammetry to a real-world survey coordinate system. To assign absolute coordinates (latitude/longitude or plane rectangular coordinates), the common method is to use known points measured on site (ground control points: GCPs). For example, if you install multiple GCP targets on site and measure their coordinates with an RTK-GNSS or total station, you can assign those coordinates to corresponding points on the point cloud to correct the model’s overall position and scale. With a sufficient number of reference points, you can assign absolute coordinates to the point cloud with centimeter-level accuracy. Note that techniques such as LRTK, which can attach high-precision positional information to photos at the time of shooting, can enable rapid georeferencing without installing additional reference points.

• Editing and preparing point cloud data: Once alignment is complete, prepare the point cloud data for soil volume calculations. Remove unnecessary points and noise, and extract only the ground-surface points to be analyzed. For example, point clouds of machinery or trees are unnecessary for volume calculations and should be filtered out. If the photogrammetry software includes ground extraction (classifying ground vs. non-ground), you can automatically extract ground-surface points. For the extracted ground surface point cloud, interpolate areas with local data gaps and delete obvious outliers if necessary. If needed, generate polygon meshes or TIN (triangulated irregular network) models from the point cloud to facilitate subsequent volume calculations.

• Setting the reference plane: Define the reference plane for calculating soil volumes. If you have point cloud data from two time points to compare (for example, before and after excavation), prepare ground surface models for each and calculate the difference directly between the two models. Alternatively, if you want to calculate volume from a single terrain model — for example the volume of a berm or spoil heap — you can use a known horizontal plane or existing ground surface as the reference. For instance, you can treat the surrounding existing ground level as the reference plane for an embanked area and compute the volume between that plane and the embankment surface point cloud. You can also set an arbitrary horizontal reference plane (a virtual reference) and calculate volumes from the height differences relative to that plane. Choose an appropriate reference plane according to site conditions.

• Calculating volumes (embankment and excavation volumes): When preparations are complete, perform the volume calculation. In software that can handle point clouds or mesh models, compute the volumetric difference between two ground surface models or integrate the volume enclosed between a ground surface model and a reference plane. Specifically, this is a numerical integration process of the difference in elevation between two terrain models. Many programs allow you to specify the calculation area with a polygon to compute soil volumes only for that region. The output of the volume calculation includes total embankment and excavation volumes (positive and negative volumes) and local volume changes per mesh cell.

• Verification and utilization of results: Verify the calculated soil volumes and apply them to site management as needed. Check that the calculation results are not unreasonably large or small by comparing them with known values. Visualizing the point cloud together with volumetric differences makes it intuitive to see where and how much excavation or embankment occurred. When appropriate, include volume values in reports or drawings to confirm quantities with clients.

Those are the basic steps. The next chapter explains in more detail the “visualization” of soil volumes using point cloud differencing.

Visualizing excavation and embankment: point cloud comparison and volume calculation

By comparing point cloud data acquired via photogrammetry and calculating soil volumes, you can visually grasp excavation and embankment conditions. Concretely, overlay point cloud models from before and after excavation, or from design grade and post-construction grade, and compute volumes from the elevation differences. At that time, rather than merely obtaining a numerical volume, displaying a color-coded map (heatmap) showing how much and where excavation or embankment occurred makes the as-built condition immediately visible. For example, if at a given point excavation is 10 cm deeper than design, show it in blue; if embankment is 20 cm higher than design, show it in red — displaying such colors on the point cloud makes the distribution of surplus and deficit soil intuitive.

Volume calculation for embankment and excavation is typically done by separately aggregating the positive volume (embankment portions) and negative volume (excavation portions) between differential models. This allows you to determine how many cubic meters were added or removed overall. Comparing with design plans lets you quantify whether soil amounts are short or in excess, which helps adjust construction scheduling and soil transport plans. Another advantage unique to point cloud-based calculations is the flexibility to reaggregate soil volumes for arbitrary sections as needed. For example, dividing the construction area into grids or arbitrary ranges and calculating excavation/embankment volumes for each part supports detailed construction management. Using point clouds for soil volume calculations dramatically improves the accuracy and efficiency of as-built management.

Shaping the ground surface model and defining the reference plane

To perform accurate soil volume calculations, it is important to shape the ground surface model obtained from point clouds and to define an appropriate reference plane. First, regarding shaping the ground surface model: photogrammetry-derived point clouds may contain some errors and noise. For example, point clouds that include parts of vegetation or machinery can create irregularities that differ from the true terrain and distort volume calculation results. Therefore, removing non-ground points and, if necessary, smoothing the surface are recommended. After meshing, locally spiked areas should be leveled and holes filled so the shape reflects the actual terrain. Such preprocessing increases the reliability of volume calculations.

Next, defining the reference plane determines what you compare the soil volume against. The basic approach is to compare the terrain at one time with that at another, but in some cases you may use a design grade or an assumed horizontal plane as the reference. For example, when calculating an embankment’s volume, you might treat the surrounding ground as the reference plane and compute the volume of the raised portion above it. When measuring the volume of cavities created by underground excavation, use the pre-excavation natural ground as the reference and take the difference with the post-excavation point cloud model. In any case, choosing an inappropriate reference plane yields meaningless numbers, so set the reference appropriately according to site objectives. Generally, for as-built management use the design surface as the reference to compute deviations from the design, while for progress quantity management use the original terrain as the reference to calculate soil import/export quantities.

Key points for higher accuracy: using RTK positioning and LRTK

Improving positioning accuracy is a key point for further enhancing the precision of soil volume calculations using point clouds. Although photogrammetry alone can produce relatively precise relative models, high-precision positional information is essential to align the entire model accurately to survey coordinates. RTK-GNSS positioning is helpful here. RTK (Real-Time Kinematic) is a technique that combines data from a base station and a rover during satellite positioning to correct errors and achieve centimeter-level positioning in real time. Using RTK in point cloud measurements makes acquiring coordinates for ground control points easier and can also embed high-precision geotags into aerial photos so that photogrammetry results directly reflect absolute coordinates. Recently, solutions called LRTK that incorporate RTK receivers into smartphones have emerged. By attaching a small GNSS antenna to a smartphone and shooting or scanning, you can immediately assign global positioning coordinates to the obtained point cloud, which is a key feature. In other words, high-precision point cloud surveying can be realized easily without specialized equipment or complex post-processing.

With LRTK, when you scan a site with a smartphone camera or LiDAR sensor, RTK positioning coordinates are overlaid on the point cloud data in real time. Consequently, the resulting point cloud model is an accurate terrain model already in the survey coordinate system, removing the need for later alignment with control points. Also, acquired point cloud data and measurement results can be saved and shared in the cloud, enabling immediate office-side confirmation or collaboration with stakeholders. LRTK, which enables high-precision and efficient surveying by anyone on site, is expected to greatly simplify future soil volume and as-built management.

Summary

Using point cloud data derived from photogrammetry is a powerful way to understand the as-built condition of earthworks such as excavation and embankment in detail and intuitively. Calculating and color-coding soil volumes on a 3D model makes it easy to see at a glance what was done where, contributing to faster process management and prevention of rework. This article explained the flow and key points of using point clouds for soil volume calculations; the key to achieving good accuracy remains appropriate data acquisition and processing. For high-precision photogrammetry, sufficient overlapping shots and alignment with control points are important, and LRTK — which combines smartphones with RTK — has recently dramatically simplified this process. By adopting new technologies, soil volume management that previously relied on surveying specialists is becoming something site personnel can easily perform themselves. Consider actively using point cloud-based soil volume calculations to achieve smarter and more efficient construction management.

Frequently Asked Questions (FAQ)

Q1. What is soil volume calculation using point cloud data? A1. Soil volume calculation using point cloud data is a method that uses 3D data (point clouds) composed of many points obtained by laser scanners or photogrammetry to determine volume changes in terrain. Instead of creating cross-sections from limited survey points as in traditional methods, it compares high-density point cloud models that cover the entire ground surface, enabling accurate volume calculations that capture even small undulations caused by excavation or embankment.

Q2. How do you calculate volume from a point cloud obtained by photogrammetry? A2. To calculate volume from a point cloud obtained by photogrammetry, first convert the point cloud into a 3D ground surface model (mesh or TIN), then perform differential calculations against another model or reference plane. For example, overlay the pre-excavation terrain model with the post-excavation model, calculate elevation differences, and integrate those differences across the area to determine the excavated volume. Similarly, for embankment, compute the increased volume from before and after models. Dedicated software can automatically calculate volumes from point clouds and display results numerically and as heatmaps.

Q3. Can you obtain point cloud data for soil volume calculations without a drone? A3. Yes. While drones are effective for efficiently photographing large areas, you can generate point clouds via photogrammetry by taking many photos from the ground with a camera. Also, recent smartphones and tablets with built-in LiDAR sensors can quickly scan point clouds of nearby objects. By using small GNSS receivers attachable to smartphones (such as LRTK), you can obtain high-precision point clouds even with handheld smartphone shooting, so soil volume measurement on site is becoming feasible without drones or expensive laser scanners.

Q4. How accurate are soil volume calculations using point clouds? A4. Accuracy depends on conditions, but with proper measurement and processing, soil volume calculations using point clouds often achieve errors within a few percent. Field validations have shown cases where results were within about 1% of traditional surveying calculations. However, ensuring accuracy requires high-quality photos (or scan data), a sufficient number of control features, and precise alignment. Georeferencing with RTK-GNSS or known control points improves absolute accuracy and enables reliable volume calculations.

Q5. What is LRTK and how does it help in soil volume calculations? A5. LRTK is a solution that combines smartphones with RTK-GNSS technology to achieve high-precision surveying. By attaching a small RTK-capable GNSS receiver to a smartphone and performing photography or LiDAR scans, you can add high-precision positional information to acquired point cloud data in real time. The advantage for soil volume calculations is that point cloud models are obtained already in the survey coordinate system, eliminating the need for post-processing alignment. Thus, anyone can easily acquire accurate point clouds and immediately calculate excavation and embankment volumes for onsite use. LRTK greatly contributes to the efficiency and labor-saving of soil volume management.

Q6. Can the results of point cloud-based soil volume calculations be displayed as a heatmap? A6. Yes. Results of point cloud-based soil volume calculations can be displayed as heatmaps (color maps). Dedicated point cloud processing software and cloud services provide functions to color-code elevation differences between design models and as-built point clouds. This makes it easy to see which parts of the terrain differ significantly from the design. For example, green can indicate nearly as-designed, blue can indicate over-excavation (lower than design), and red can indicate over-embankment (higher than design), allowing intuitive evaluation of construction quality. Heatmaps help visually identify problem areas and are useful for corrective action and quality control.