Table of Contents

• The relationship between earthwork volume calculation and point cloud data

• Features and challenges of photogrammetry for easy on-site point cloud measurement

• The full workflow from point cloud generation to volume calculation and the necessary tools

• Comparison with conventional methods (TS and GNSS surveying): accuracy, personnel, time, and reporting efficiency

• Time-series comparison and visualization of earthwork volume changes in embankments and excavations

• How smartphone surveying with LRTK works and its benefits (shooting support, high-precision positioning, cloud integration)

• Examples of on-site use of point cloud earthwork measurement and incorporation into daily reports and reports

• FAQ

The relationship between earthwork volume calculation and point cloud data

In construction as-built management, accurately determining excavated and fill volumes—earthwork volume calculation—is an indispensable task. Traditionally, contour data obtained by surveying were used to create cross-sections and compute volumes using methods such as the average end area method or grid method. However, recently methods leveraging point cloud data (a collection of numerous 3D coordinate points) have emerged, enabling earthwork volume calculation to be performed more efficiently and with higher accuracy. Point clouds acquired by 3D laser scanners or photogrammetry can reproduce fine undulations of the ground surface, allowing terrain shapes to be modeled nearly as they are in reality. By comparing these high-resolution terrain models before and after construction, fill and excavation volumes can be calculated directly.

The principle of volume calculation using point clouds is simple: it involves determining the volume difference between terrain models before and after construction. For example, in excavation work you overlay the point cloud of the pre-excavation terrain with that of the post-excavation terrain and compute the removed soil volume from their difference. Because point cloud data cover the ground surface with countless measurement points, there is no need to interpolate between distant survey points as in traditional methods, enabling accurate quantity calculations that fully reflect terrain irregularities. Moreover, once point cloud data are acquired, you can recompute volumes multiple times by changing the calculation area or reference elevation without additional surveying. The flexibility to run alternative scenarios without extra fieldwork is a major advantage. For these reasons, point cloud–based earthwork volume calculation excels in both accuracy and efficiency, and is becoming a foundational technology supporting the digital transformation (DX) of construction management.

Of course, to perform high-accuracy volume calculations, ensuring the quality of the source point cloud data is crucial. Errors can be minimized by acquiring point clouds without gaps and at sufficient density, ensuring point cloud coordinates conform to the correct survey coordinate system, and properly removing non-terrain points such as machinery or trees. Field validations have reported cases where quantities derived from point clouds differed by only about 1% from values obtained by traditional manual surveying. Given the right preconditions, the reliability of point cloud earthwork calculation is sufficient for practical field use.

Features and challenges of photogrammetry for easy on-site point cloud measurement

As mentioned above, there are significant benefits to using point cloud data for earthwork management, but a key field challenge is how to obtain high-accuracy point clouds conveniently. Previously, dedicated equipment such as ground-based laser scanners or survey drones operated by survey teams was required for full-scale measurement. However, advances in photogrammetry now allow site personnel themselves to capture point cloud data using smartphones or drones. “On-site photogrammetry” refers precisely to the method of easily taking photos on site and automatically generating 3D models and point clouds from those images.

The appeal of photogrammetry lies in the ease of measuring large areas with familiar devices. For example, drone aerial photography can capture photo data of a large construction site from the air in a short time, enabling surveying of steep or hazardous slopes without personnel entering them. Even with smartphone photography, if you take sufficient overlapping images around the target object, dedicated software can generate a high-density point cloud model. As a technique that can produce point clouds using ordinary cameras without expensive laser scanners, photogrammetry has attracted attention as a DX tool for the field.

That said, there are several challenges with point cloud acquisition via photogrammetry. First, measurement accuracy is highly dependent on shooting conditions. Low photo resolution or exposure issues, strong shadows, or specular reflections on the subject can cause noise or gaps in the generated point cloud. If vegetation or machinery appears in the target area, extra effort is needed to extract and filter only the ground surface from the generated point cloud. Another issue is that the processing workflow is not always easily completed on-site. Although photo capture itself may take little time, the subsequent image analysis (point cloud generation) can often require hours on a high-performance PC or cloud service. With traditional photogrammetry workflows, a time lag typically occurs between surveying, data processing, and volume calculation, making it difficult to obtain results in real time on site.

Other operational hurdles include obtaining flight permissions and pilot skill requirements when using drones, and the risk that models generated from smartphone-only photos may be misaligned with real-world coordinates due to GPS position errors. For example, even if you can generate a point cloud from smartphone photos, if the model’s elevation and position are unclear, it cannot be used for earthwork quantity calculation. Therefore, to make photogrammetry practical on-site, extra steps such as placing survey control points in the field and including them in photos (georeferencing with ground control points) or ensuring sufficient photo overlap have traditionally been necessary to guarantee accuracy. Photogrammetry is a highly promising technology, but existing methods alone have left some gaps in achieving the ideal of “anyone can easily complete high-accuracy point cloud measurement on site.”

The full workflow from point cloud generation to volume calculation and the necessary tools

So what steps and tools are actually required to generate point cloud data and compute earthwork volumes? Let’s look at a typical workflow step by step.

• Data acquisition (surveying): The first step is to acquire terrain data on site. To record the target terrain and soil conditions, capture the site with a smartphone camera, LiDAR scanner, or a drone-mounted camera. For photogrammetry, the key to high-quality point clouds is to capture the subject (the area to be measured) from various angles with a sufficient number of photos, ensuring overlap between images. For a drone, you can use an automated flight plan to take photos at regular intervals; with a smartphone, walk around the subject to take photos that leave no gaps. Meanwhile, the latest iPhone or iPad models with built-in LiDAR scanners can generate point clouds in real time on site.

• Point cloud generation (image analysis): Next, generate a point cloud model from the captured data. Converting photos to a point cloud requires dedicated photogrammetry software or cloud services. When you input photos into such software, it matches feature points between images to recover camera positions and 3D point coordinates, outputting huge point clouds on the order of millions to tens of millions of points. This analysis requires a certain amount of computing resources and time, but using a high-performance PC or GPU enables accurate and fast processing. Recently, cloud services have emerged that automatically generate point clouds when you upload photos, so if you send images from the site, the point cloud may be ready by the time you return to the office.

• Georeferencing (alignment): To use generated point cloud data for volume calculation, you must correctly position it in the real-world survey coordinate system. This involves assigning scale and reference orientation/elevation to the model. For drone photos, include ground control points (GCPs) placed in advance on site in the photos, and align the model to real-world coordinates based on their known coordinates. For smartphone photos, you can also photograph known points on site and match them in post-processing, or—as described later—use high-precision positioning from the start to obtain data with absolute coordinates. In any case, to compare point clouds acquired at different times or to overlay them with design data, it is essential that all data be on the same coordinate reference. Performing this step correctly turns point clouds into useful survey data that can be handled in GIS or CAD.

• Volume calculation: With point clouds in a consistent coordinate system, specify the area for which you want fill or cut volumes and compute the volumes. Typically, a TIN (triangulated irregular network) ground surface model created from the point cloud is used for volume calculations. Concretely, you compare against a reference horizontal plane to accumulate fill and cut volumes, or you overlay two point clouds from different times to compute differential volumes. Civil engineering 3D software or point cloud processing tools are used for these calculations, but once the procedures are set up, volume values can be obtained automatically. For example, if you want the fill volume for a specific area, define the polygon for that area and software will quantify fill and cut volumes based on the internal point cloud. Recently, open-source point cloud processing tools that can compute volumes and cloud-based services that let you load point cloud models in a web browser and display volumes have become available, making it possible to check as-built quantities via a browser without dedicated software.

• Deliverables sharing and reporting: Finally, share computed volume data within the site and compile reports for clients and stakeholders. Traditionally, results were organized in Excel sheets and, as needed, cross-section diagrams with color coding were created. When using point clouds you can take it a step further by attaching images that visualize the point cloud model and differential volumes from a 3D viewer to reports, or by sending cloud sharing links so stakeholders can interactively inspect site conditions. Sharing the 3D data that underlies the quantity calculation as evidence is a unique strength of point cloud utilization. Tools that support this workflow include capture devices (smartphones and drones), photogrammetry conversion software, point cloud processing software or cloud services, and viewer/sharing platforms. While these toolsets were once separate, integrated all-in-one solutions have begun to appear in recent years.

Comparison with conventional methods (TS and GNSS surveying): accuracy, personnel, time, and reporting efficiency

The use of point cloud data changes earthwork volume workflows significantly compared to conventional methods that rely solely on total stations (TS) or GNSS surveying. Let’s compare the differences in accuracy, required personnel, working time, and reporting work.

• Accuracy: The coordinate accuracy of individual measurement points is generally better with TS or RTK-GNSS surveys. A TS can measure points with millimeter-level accuracy using prism-based EDM, and RTK-GNSS with a base station can achieve planar positioning errors within several centimeters (several in). However, in earthwork volume calculation, “accuracy” is not only about single-point accuracy but also about how well the overall terrain shape is captured. When surveying with TS or GNSS, points are usually sampled on a grid at intervals of several meters (several ft) or measured along cross-section lines at key locations. Although each measured point may be highly accurate, fine undulations between measured points must be interpolated and can be missed. In contrast, point cloud data cover the surface with countless points, fully capturing small depressions and rises. Final volume accuracy depends on such undetected portions, so under proper conditions point cloud–based calculations can reach accuracy comparable to conventional methods. Indeed, in large-scale embankment work, reports have shown that volumes calculated from point clouds differed by about 1% from values obtained by the average end area method, demonstrating that point cloud approaches can provide sufficient accuracy.

• Required personnel: Traditional methods required many manual steps from surveying to drawing creation and volume calculation. On large sites, survey crews of four might take a week (a total of 20–30 man-days) to complete terrain surveying, cross-section creation, and volume calculation. By switching to drone aerial photography plus point cloud processing, there are cases where the job was completed by two people in one day (2 person-days). In that example, aerial photography took about 15 minutes and point cloud generation and volume calculation were finished the same day. Thus, point cloud utilization can dramatically reduce labor and time for survey tasks. In extreme cases, one operator can complete surveys by operating a smartphone or drone, freeing other staff for different tasks. In an environment where specialist surveyors are scarce, enabling “digital measurements anyone can use” has significant value for addressing labor shortages.

• Working time: As noted, labor-intensive traditional volume measurement took many days especially for large sites, but point cloud–based methods greatly shorten the cycle from field measurement to data processing. In some cases, you can obtain volume results the same day. For example, if you photograph the entire site with a drone in the morning and run point cloud generation and volume calculations in the cloud, you might report as-built quantities at an afternoon meeting. Being able to grasp earthwork volumes in near real time allows for same-day decisions such as arranging additional dump trucks or adjusting equipment allocation, enabling rapid responses to changing construction plans.

• Reporting work: There are also differences in reporting. Traditionally, survey results were manually compiled into cross-section drawings and calculation tables, and preparing documentation to substantiate as-built quantities took time. Large projects required internal checks and client confirmations, adding complexity. Point cloud data, however, serve as direct evidence. Sharing the point cloud data allows stakeholders to intuitively understand site conditions and visually verify the basis for quantity calculations. For instance, sharing a differential map colored to show excesses and deficits of fill and cut or a 3D view with overlaid calculated volumes immediately communicates where and how much material is lacking or in excess. From a report preparation standpoint, many tasks can be automated: generating cross-sections and overview diagrams from point clouds and placing them into reports reduces workload significantly. In short, the traditionally laborious creation of as-built reports can be semi-automated. Point cloud utilization speeds up the entire cycle from surveying to reporting and accelerates on-site decision-making.

Time-series comparison and visualization of earthwork volume changes in embankments and excavations

Another strength of point cloud data in earthwork management is the ability to capture terrain changes over time. Terrain at a construction site changes daily; by conducting weekly drone flights and saving point cloud models, you can graph weekly increases in fill volume or create color-coded maps showing where and how much fill was added since the previous week. Making time-series comparisons of earthwork changes is straightforward, enabling objective, data-driven progress management. If progress is lagging, you can quickly arrange additional equipment, improving the PDCA cycle in construction management.

From an as-built management perspective, comparing with design data is also important. If you have the acquired point cloud and the design model (such as the design ground surface), you can check whether the current state matches the design across the site. On excavation sites, you can identify areas excavated to the required depth versus areas with remaining soil; in embankment works, you can detect overfills above the specified heights by analyzing point cloud differences. Visualizing differential results as a heatmap makes issues immediately clear—for example, coloring areas higher than design red and lower areas blue shows at a glance where corrections are needed. Spatially indicating fill and cut surpluses and deficits streamlines corrective instructions on site and helps prevent rework and material waste.

Accumulated time-series point cloud data are useful not only during construction but also for post-construction maintenance and disaster response. If you store the as-built point cloud at completion, you can later compare it with current point clouds to quantitatively evaluate long-term changes. For instance, if an embankment or fill structure is settling over time, you can compute settlement amounts by differencing with past point clouds; after a slope failure, you can estimate collapsed soil volume by comparing pre- and post-event point clouds. Traditionally, disaster aftermath quantities were measured by manual surveys in hazardous conditions; remote point cloud measurement enables safe and rapid assessment of soil volumes, offering significant safety and speed advantages. Differential use of point cloud data thus applies across pre-construction, construction, and post-construction phases, providing multifaceted value beyond simple volume management.

How smartphone surveying with LRTK works and its benefits (shooting support, high-precision positioning, cloud integration)

As described earlier, there were challenges in applying photogrammetry on site, but new technologies have begun to solve these problems. A representative example of an environment that enables anyone to easily acquire high-accuracy point clouds is LRTK. LRTK is a smartphone-integrated high-precision positioning system that enhances smartphone GPS to centimeter-level accuracy using a small antenna attached to the phone and a network RTK method. By attaching an LRTK antenna to an iPhone or iPad and scanning with the device’s built-in camera or LiDAR, accurate coordinates are assigned to each point in the acquired point cloud. In other words, the drone + GNSS base station or expensive laser scanner previously required for high-accuracy 3D surveying can be replaced by a single handheld smartphone—an innovative approach. No complicated equipment operation knowledge is required; site technicians can operate it as an extension of normal smartphone use, which is a major advantage. Compared to traditional 3D measurement methods, the introduction cost is lower and there is no need for dedicated vehicles or external power, so it offers excellent mobility and can be used immediately when needed. For frequent, everyday measurements, smartphone surveying carried in a pocket dramatically lowers the barrier.

Let’s summarize the benefits of LRTK solutions from the perspectives of “shooting support,” “high-precision positioning,” and “cloud integration.”

• Shooting support: LRTK smartphone apps can generate point clouds in real time and display them on the screen. This allows the operator to check the point cloud while capturing to ensure complete coverage. For example, if scanning a slope and a blind spot does not appear in the point cloud, you can take additional photos on the spot to fill the gap. Data captured in multiple sessions are automatically aligned, so high-quality point clouds can be obtained without specialized post-processing. The app may also display guide lines that navigate an optimal shooting route and features to retake photos from the same position and angle for repeat observations, enabling anyone on site to scan without measurement errors.

• High-precision positioning: LRTK’s key advantage is improving smartphone GNSS accuracy. With an LRTK antenna compatible with network RTK, smartphone GPS—normally subject to errors of several meters—can achieve accuracy within several centimeters (several in). This means all acquired point clouds and photos have accurate world coordinates, so you can immediately use them for volume calculations and drawing comparisons without installing GCPs or performing alignment afterwards. Vertical accuracy is also high enough for measuring reference elevation differences and cross-section shapes. For example, combining LRTK with an iPhone Pro’s LiDAR scan results in point clouds with accurate absolute coordinates from the outset, enabling direct use as as-built management deliverables. Because the cumbersome alignment steps of traditional photogrammetry are unnecessary, the overall workflow is greatly simplified.

• Cloud integration: LRTK offers a cloud service linked to the field app, enabling automatic data sharing and storage. Point clouds and geotagged photos captured by the app are uploaded to the cloud immediately after capture, eliminating the need to copy files to a PC or convert formats after returning to the office. Uploaded point clouds can be viewed in a 3D viewer in the cloud, and analysis functions such as browser-based volume calculation and drawing generation are available. For example, the workflow “on-site smartphone scan → automatic cloud processing → immediate confirmation of volume results” can be realized as a one-stop process, making the time lag from measurement to analysis nearly zero. Accumulating data in the cloud allows unified management of terrain changes throughout the project lifecycle and makes time-series comparisons easy. Past point clouds can be retrieved for reference, and links can be shared with stakeholders for collaborative review. Real-time sharing of on-site information—difficult with paper records—becomes possible, strongly supporting faster and more efficient construction management.

In this way, LRTK is an integrated system that realizes “easy on-site point cloud measurement with a smartphone → immediate high-precision volume calculation → cloud data sharing.” It packages solutions to the obstacles that prevented on-site completion of photogrammetry—shooting skill, positioning accuracy, and data processing environment—making it a solution directly contributing to labor reduction and efficiency improvements on construction sites.

Examples of on-site use of point cloud earthwork measurement and incorporation into daily reports and reports

Finally, here are practical examples of how point cloud earthwork measurement is used on construction sites and how it can be leveraged in routine reporting.

• Progress management and daily reports: On one development site, the site supervisor scanned piles of leftover soil every evening with a smartphone + LRTK and immediately determined the day’s removed soil volume. The figures were recorded in daily reports and used to decide next-day equipment allocation and dump truck dispatch. Where daily soil volumes were previously estimated from the number of dump trucks and their load capacities, measurements now provide accurate recorded values, improving the reliability of daily reports and facilitating smoother information sharing among contractors. Reviewing accumulated weekly and monthly point cloud data later provides objective evidence of progress and quantities for internal and external reporting, and can inform needed revisions to construction plans and schedule control.

• As-built verification and report creation: Upon completion of embankment work, point clouds acquired with LRTK were compared with design models to verify as-built (fill/cut surpluses and deficits). Automatically computed fill and cut volumes were used directly in as-built quantity tables, and cross-sections, longitudinal sections, and 3D view images extracted from the point cloud were attached to reports to provide clear explanations to clients. Above all, because point cloud data serve as immutable evidence, clients can immediately verify quantities without time-consuming on-site remeasurement. Furthermore, LRTK cloud services offer functions to output prescribed report formats (PDF) from acquired data, enabling the generation of measurement reports with photos, coordinates, and notes at the push of a button. Using these features, you can easily create original as-built documents that combine on-site photos and point clouds, improving reporting efficiency and quality.

• Safety management and special-case applications: Point cloud technology is also effective for measuring hazardous areas where personnel cannot enter. In places at risk of collapse or immediately after disasters, remote measurements with drones or LRTK can accurately quantify soil volumes that previously had to be estimated from a distance. For example, LRTK was used to estimate post-heavy-rain sediment accumulation and supported rapid restoration planning. For asset management of structures, repeatedly acquiring point clouds from fixed points allows you to record long-term changes like an electronic medical chart. By comparing 3D point clouds shot from the same position and angle over time, you can numerically and visually demonstrate settlement or deformation trends, enhancing the persuasiveness of maintenance reports. These are advanced applications beyond daily construction management, but they are new developments made possible by having an easy-to-use 3D scanning capability on site.

By introducing simplified surveying with LRTK on site, you can create a system that allows immediate measurement and instant sharing and reporting of results when needed. This enables as-built management that secures accuracy and reliability while significantly saving labor and time, contributing to productivity improvements, cost reductions, and enhanced safety across the entire project. Bringing surveying in-house—work that was once entrusted to specialists—dramatically increases the speed of construction management. This is truly the revolutionary effect of “on-site completion of earthwork volume calculations and point cloud measurement.” In future civil engineering projects, smartphone-based point cloud measurement and real-time as-built management are becoming the new standard processes. Please consider trying smartphone scanning and LRTK-enabled streamlined earthwork management at your site.

FAQ

Q1. Is the accuracy of earthwork volume calculation using point cloud data reliable? A1. Yes. If point cloud data are properly acquired and processed, accurate volume calculation is possible. Generally, point clouds from photogrammetry or laser scanners, when calibrated with control points and measured at sufficient density, yield volume calculation errors within the same range as traditional survey calculations (within a few percent). Field comparisons have reported differences around 1% between quantities derived from point clouds and those from conventional methods. However, to ensure high accuracy, prerequisites include complete coverage in point cloud acquisition, removal of non-ground points, and correct coordinate alignment. If these conditions are met, point cloud earthwork calculations are sufficiently reliable for field use.

Q2. Are specialized skills required to use photogrammetry or LRTK? A2. Traditional photogrammetry required some specialist knowledge and experience, but recent solutions have become much easier to operate. Smartphone apps for scanning are intuitive; simply follow on-screen instructions to capture photos and create 3D point clouds without special photography skills. With LRTK, attach the antenna to the smartphone, start the app, and move the device according to the on-screen guide—the system automatically handles positioning, point cloud acquisition, and volume calculation. Users do not need to be familiar with technical terms or complex settings, and site personnel can start using it after short training. Because data processing and analysis are automated in the cloud, users only need to check the results. Modern photogrammetry tools and LRTK are designed so that anyone can operate them at the site level.

Q3. What equipment and environment are needed for on-site point cloud measurement? A3. Basically, devices with high-quality cameras (smartphones, tablets, drones) and supporting software or services are sufficient for point cloud measurement. For smartphones, recent iPhone or iPad models with LiDAR scanners are desirable, but photogrammetry can also be performed with standard cameras. When using LRTK, you need a compatible smartphone, an LRTK antenna, and a mobile network connection (to receive RTK correction information). For drones, you need a GPS-equipped aircraft and camera, as well as flight permissions and a qualified pilot. In any case, cloud services or PC-based point cloud software are required to process acquired data. However, all-in-one services like LRTK can complete capture-to-cloud storage on site with only a smartphone and antenna, eliminating the need for a special PC. In terms of environment, ensuring clear lines of sight and sufficient shooting positions is important for capturing wide areas. Photogrammetry is also affected by weather and lighting, so choosing sunny days and front-light conditions improves accuracy. For safety, avoid having personnel enter high or unstable slopes; use drones or pole-mounted devices when necessary and take measures such as monopods or poles to protect surveyors.

Q4. Which should be used for earthwork volume measurement: smartphone scans or drone surveys? A4. It is best to choose according to site scale and purpose. For large sites or sites with many high areas, drone surveys efficiently capture overview point clouds in a short time. Conversely, for confined sites, interior spaces, or detailed measurements, smartphone scanning offers greater mobility. Smartphones do not require takeoff/landing space and are suitable in no-fly zones; they’re also convenient when frequent, routine measurements are needed. In practice, both are often used together: first, a drone survey captures the overall site to create a broad 3D model; then, smartphone + LRTK rapidly scans detailed areas and places prone to frequent change for supplementary and follow-up measurements. Point clouds from both sources can be integrated on a common coordinate system, allowing you to select the optimal method based on site conditions. In short, drones are best for wide-area rapid surveys, and smartphones are best for quick, detailed on-the-spot measurements. Either approach can produce point clouds suitable for earthwork volume calculation, so choose flexibly according to the site’s conditions and the required accuracy and frequency.