Table of Contents

• The relationship between earthwork volume calculation and point cloud data

• Features and challenges of on-site photogrammetry

• The complete flow from point cloud generation to volume calculation and required tools

• Comparison with conventional methods (total station & standalone GNSS) (accuracy, personnel, time, reporting)

• Time-series comparison of earthwork changes (visualizing fills, excavations, and design differences)

• Smartphone-based point cloud acquisition with LRTK and its benefits (shooting assistance, positioning, cloud sync)

• On-site use cases and integration with daily reports and report generation

• FAQ

The relationship between earthwork volume calculation and point cloud data

On civil construction sites, earthwork volume calculation for excavation and embankment is an indispensable task for as-built management. Traditionally, terrain surveys were used to create cross-sections and compute volumes using the average-end-area method or grid methods. However, in recent years, the use of point cloud data (3D data composed of countless coordinate points) has made these volume calculations more efficient and more accurate. Point clouds captured by 3D laser scanners or photogrammetry can record fine surface irregularities, allowing terrain to be reproduced almost exactly as it is. By comparing point cloud data of the terrain before and after construction, embankment and excavation volumes can be derived directly.

The principle of volume calculation using point clouds is simple: determine the volume difference between the surface models before and after construction. For excavation work, for example, you compare the point cloud of the existing ground before excavation with the point cloud after excavation to calculate the volume of soil removed. Because point clouds can reconstruct the terrain as a surface from countless measured points, there is no need to interpolate between sparse survey points as in conventional methods, enabling accurate quantity estimation that reflects terrain undulations in full. Also, once a point cloud is acquired, volumes can be recalculated multiple times with different calculation ranges or reference planes without additional surveying, making it flexible for reanalysis or scenario estimates. For these reasons, volume calculation using point clouds excels in both accuracy and efficiency and is becoming a foundational technology supporting digital construction management.

To achieve the highest-accuracy volume calculations, ensuring the quality of point cloud data is crucial. This includes acquiring a sufficiently dense point cloud with no unmeasured areas in the shooting range, aligning coordinates correctly to the reference coordinate system, and appropriately removing or processing unnecessary objects (such as machinery or vegetation). Meeting these conditions enables low-error volume calculations. Field validations have reported cases where the quantities derived from point clouds differed only about 1% from values obtained by traditional manual surveying, indicating that, under proper procedures, point cloud-based volume calculation is highly reliable.

Features and challenges of on-site photogrammetry

Given the usefulness of point cloud data for earthwork management, how easily those point clouds can be obtained on-site becomes a practical issue. Traditionally, specialized surveying equipment (terrestrial laser scanners or surveying drones) and survey teams were required, but recent advances in photogrammetry have increased cases where site staff themselves capture point cloud data using smartphones or drones. "On-site photogrammetry" refers to the method of easily taking photos directly at the location and generating point clouds by creating 3D models from those images.

A key feature of photogrammetry is the ease of measuring large areas using familiar devices. Drone aerial surveys can capture a wide area from above in a short time, allowing measurement of steep or hazardous terrain without risking personnel. Even with smartphone photography, if you take enough photos around a subject, specialized software can generate a high-density point cloud model. The major advantage is that point clouds can be created with ordinary cameras without expensive laser scanners, making photogrammetry a notable DX (digital transformation) tool for fieldwork.

However, there are several challenges with obtaining point clouds via photogrammetry. First, the accuracy of measurements depends heavily on shooting conditions. If photo resolution or exposure is insufficient, or if the subject has many shadows or reflections, errors or voids can occur during point cloud generation. If the scene contains vegetation, debris, or other unwanted objects, extracting just the ground surface from the generated point cloud can be time-consuming. Furthermore, the workflow can be difficult to complete entirely on site: although shooting itself may be quick, subsequent photo analysis (point cloud generation) can take hours on a high-performance PC or cloud service. In conventional photogrammetry, a time lag often occurs between surveying, data processing, and volume calculation, making it hard to obtain real-time results on site.

Other operational hurdles include the need for flight permissions and skilled operators when using drones, and the risk that smartphone-only photogrammetry yields models misaligned with real-world coordinates due to GPS inaccuracies. For example, even if you can generate a point cloud from smartphone photos, it is unusable for quantity calculations if the absolute height reference or position is unclear. Therefore, practical application in construction management required additional steps such as tying to survey control points (setting ground control points) or ensuring sufficient photo overlap to secure accuracy. While on-site photogrammetry has high potential, conventional techniques alone left gaps in realizing the ideal of "anyone easily completing high-precision point cloud measurement on site."

The complete flow from point cloud generation to volume calculation and required tools

So, what procedures and tools are required to actually generate point cloud data and calculate earthwork volumes? Below is a general workflow.

First, Step 1 is data acquisition (measurement). To record the terrain and soil conditions, capture the site with a smartphone camera, LiDAR scanner, or a drone-mounted camera. In photogrammetry, it is important to take sufficient photos from all directions and ensure overlap between images. With drones, automatic flight programs can capture at set intervals; with smartphones, walk around the subject to take photos without gaps. Devices like iPhone LiDAR that can capture point clouds in real time may allow point cloud generation to be completed on site.

Step 2 is point cloud generation (processing). To create point cloud models from photos, use photogrammetry software or cloud services. When photos are input into the software, feature matching and angle calculations recover camera positions and 3D point coordinates, outputting point clouds on the order of millions to tens of millions of points. This processing requires computing resources and time, but high-performance PCs and GPUs enable accurate and fast processing. Recently, cloud services have emerged that automatically generate point clouds simply by uploading photos; if data is sent from the field via the internet, the point cloud may be ready by the time you return to the office.

Step 3 is georeferencing. To use the generated point cloud for volume calculation, you must position it properly in a real-world survey coordinate system. Georeferencing involves assigning scale, orientation, and elevation references to the model. For drone imagery, ground control points (GCPs) with known coordinates are placed in the photos so the model can be aligned to real-world coordinates. For smartphone photos, you can either capture known points on site for later matching or, as described later, acquire data with high-precision positioning from the outset. In any case, to compare point clouds from different times or to overlay design data, all datasets must be in the same coordinate reference. Proper execution of this step makes the point cloud usable as measurement data in GIS or CAD.

Step 4 is volume calculation. With point clouds aligned to a coordinate system, specify the region of interest and perform computations. Typically, a TIN (triangulated irregular network) terrain model created from the point cloud is used for volume calculations. Specifically, volumes of fill or cut are integrated relative to a reference plane, or two different terrain point clouds are overlaid as differential point clouds to determine volume differences. Civil-oriented 3D software or point cloud processing tools perform these calculations, and once the workflow is configured, volume values can be obtained automatically. For example, to compute embankment volume in a specified area, a polygon can be defined and software will quantify the vertical volumes within that polygon. Recently, open-source point cloud tools with volume calculation features and web-based cloud services that display volumes in a browser have appeared, enabling as-built quantity checks even without specialized software.

The final step, Step 5, is sharing and reporting results. Share computed volume data on site and compile reporting materials for clients and stakeholders. Traditionally, results were summarized in Excel, and colored cross-section diagrams were created as needed. With point clouds, you can go further: attach screenshots of 3D viewers showing the point cloud and differences to reports, or send cloud sharing links so stakeholders can interactively inspect the site. Being able to share the underlying 3D data itself—not just numerical results—is a major advantage of point cloud utilization. Tools supporting this workflow include capture devices (smartphones, drones), photogrammetry conversion software, point cloud processing software or cloud services, and viewers/sharing platforms. While these tools used to be used in separate combinations, integrated solutions that unify them have emerged in recent years.

Comparison with conventional methods (total station & standalone GNSS) (accuracy, personnel, time, reporting)

Approaches using point clouds versus conventional methods relying solely on total stations (TS) or GNSS surveying differ significantly in earthwork volume tasks. Let’s compare differences in accuracy, personnel requirements, time, and reporting.

On accuracy, single-point positioning accuracy itself is superior with TS or high-precision GNSS. TS can obtain coordinates with millimeter accuracy via prism total station measurements, and RTK-GNSS can achieve horizontal errors within a few centimeters when using a base station. However, in earthwork calculation, "accuracy" is not only about small point errors but also about how comprehensively the overall terrain shape is captured. TS/GNSS surveys typically sample terrain at meter-scale grid intervals or place points along cross-section lines, resulting in sparse sampling; while individual points are accurate, finer undulations between points must be interpolated and may be missed. Conversely, point clouds are a dense set of points covering the terrain continuously as a surface, capturing even small depressions or mounds. Therefore, overall volume errors depend on these undetected areas, and under comparable conditions, point cloud-based volume calculations can reach accuracy levels comparable to traditional methods. Indeed, there are reports from large embankment projects where volumes derived from point clouds differed by about 1% from volumes calculated via the average-end-area method, confirming sufficient accuracy with point clouds.

Next, compare required personnel and time. Conventional methods required many manual steps from surveying to drawing creation and volume calculation. For example, surveying a large development site might have required a surveying team of four working for a week (20–30 person-days) to perform terrain measurement, cross-section creation, and volume calculation. When switching to photogrammetry plus point cloud processing, there are cases where two people completed the job in one day (two person-days for shooting and processing). A drone can capture photos in about 15 minutes and the point cloud and volume calculation can be completed the same day. Thus, point cloud utilization dramatically reduces personnel and time for surveying. One operator can operate a smartphone or drone while others perform other tasks, which is significant amid a shortage of experienced surveyors. Digital measurement that anyone can handle helps mitigate labor shortages.

Reporting efficiency also differs. Traditionally, survey results were manually compiled into drawings and tables and preparing documentation proving as-built quantities took time. Large sites especially involved complex reporting processes including internal checks and client walkthroughs. With point clouds, the 3D model itself can serve as evidence. Showing stakeholders the point cloud allows intuitive understanding of the terrain and visual explanation of quantity bases. For example, sharing color-coded difference maps or point cloud views with overlaid volumes instantly communicates where material is lacking or excess. Reporting is simplified by automatically generating cross-sections and overview diagrams from point clouds and pasting them into reports, reducing the workload for staff. In short, reporting that used to be time-consuming 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 of earthwork changes (visualizing fills, excavations, and design differences)

Another strength of earthwork volume calculation is capturing terrain changes over time. Because construction sites change daily, regularly acquiring point clouds allows quantitative tracking of embankment and excavation progress. For example, if a site is drone-surveyed and point cloud models are saved every weekend during an embankment operation, you can graph weekly increases in volume or display maps color-coded by week-over-week changes that show where fill was added. Ease of time-series comparison enables objective management of construction progress. If progress lags, you can quickly arrange additional machinery, improving the PDCA cycle in construction management.

From an as-built management perspective, comparing against design data is also important. If you have a point cloud and the design finished-surface model (e.g., design ground surface data), you can check overall conformity to the design. In excavations, you can identify areas already excavated to design depth and areas with remaining soil; in embankments you can detect places exceeding specified heights through point cloud differences. Visualizing differences as a heatmap makes interpretation intuitive. For example, coloring areas higher than design in red and lower areas in blue helps instantly identify spots needing correction. Spatially indicating excesses and shortages of cut and fill makes issuing corrective instructions straightforward and helps prevent rework and material waste.

Accumulating time-series data also aids post-construction maintenance and disaster response. Saving the as-built point cloud at completion lets you quantify long-term changes by comparing future inspections to the baseline. For instance, if an embankment subsides over time, the amount of settlement can be computed from differences in earlier point clouds; after a slope failure, pre- and post-event point clouds can estimate the collapsed volume. Previously, disaster assessments required on-site manual surveying, but remote point cloud measurement allows immediate estimation of material volumes even in hazardous sites, offering major safety and speed advantages. Differential use of point clouds is applicable across pre-, during-, and post-construction phases and provides broad value beyond earthwork management.

Smartphone-based point cloud acquisition with LRTK and its benefits (shooting assistance, positioning, cloud sync)

As noted, on-site photogrammetry has challenges, but recent technologies have begun to overcome them, creating an environment where "anyone can easily obtain high-precision point clouds." A representative example is LRTK. LRTK is a smartphone-integrated high-precision positioning system provided by Refixia Co., Ltd. By attaching a dedicated small antenna to an iPhone and using network RTK corrections, LRTK improves smartphone position information to centimeter-level accuracy. This allows accurate coordinates to be assigned to each point in point clouds captured by a phone’s camera or LiDAR, enabling smartphone-based measurement at accuracies comparable to surveying instruments. Previously, high-precision 3D surveying required drone + GNSS base stations or expensive laser scanners, but LRTK’s ability to substitute these with a single handheld smartphone is revolutionary. It requires no specialized equipment operation knowledge, so field technicians can handle it as an extension of their usual workflow. Compared to other 3D measurement methods, LRTK has a low introduction cost and does not require vehicle mounting or external power setup, providing agility to measure whenever needed, which is ideal for frequent routine measurements.

Organizing LRTK solution benefits around "shooting assistance," "high-precision positioning," and "cloud synchronization":

• Shooting assistance: The smartphone LRTK app generates point clouds in real time and displays them on the screen, allowing operators to shoot while visually confirming that there are no missing areas. If a slope has blind spots not captured in the point cloud, additional shooting to fill them is easy on the spot. Multiple scans are automatically aligned, so users can obtain high-quality point clouds without specialized post-processing. The app may also display guides for optimal shooting paths, and features to re-shoot from the same position and angle for fixed-point monitoring, establishing a system where anyone on site can reliably capture measurements.

• High-precision positioning: LRTK’s defining feature is improving smartphone GNSS positioning accuracy. With an LRTK antenna compatible with network RTK, smartphone GPS, which normally has meter-level errors, is improved to centimeter-level. This attaches global coordinates to all captured point clouds and photos, allowing immediate use for volume calculations and drawing comparisons without GCP post-correction. Elevation information is also highly accurate, so measurements relative to reference planes and cross-section creation are directly usable. For example, LiDAR scans from an iPhone Pro combined with LRTK provide point clouds with high-precision coordinates from the outset, enabling direct use of the data for as-built management. Eliminating the need for a separate coordinate-alignment step greatly simplifies the overall workflow.

• Cloud synchronization: LRTK offers a cloud service linked to the field app that enables automatic data sharing and accumulation. Point clouds and georeferenced photos captured by the app are uploaded to the cloud immediately after shooting, eliminating the need to copy files via USB or perform format conversions back at the office. Uploaded point clouds can be displayed in a 3D viewer in the cloud and analyzed through a browser to compute volumes and create diagrams. For example, the workflow "smartphone scan on site → automatic cloud processing → immediate confirmation of volume results" can be realized as a one-stop process, reducing latency from measurement to analysis to nearly zero. As data accumulates in the cloud, managing terrain changes across a project from start to finish becomes easy; you can retrieve past point clouds for reference or share links with stakeholders for collaborative review. Instant sharing of on-site information, which was difficult with paper records, significantly accelerates and streamlines construction management.

Thus, LRTK is an integrated system that realizes "easy point cloud measurement with a smartphone → immediate high-precision volume calculation on site → cloud-based data sharing." It packages the technical hurdles of on-site photogrammetry (shooting skill, positioning accuracy, processing environment) into one solution and supports labor reduction and efficiency gains on construction sites.

On-site use cases and integration with daily reports and report generation

Finally, here are practical use cases of point cloud volume measurement in the field and how it can be leveraged in daily reports and as-built reporting.

• Progress management and use in daily reports: At one development site, the site supervisor scanned spoil piles every evening using a smartphone + LRTK to instantly determine the day’s removed soil volume. These figures were recorded in daily reports and used to inform next-day decisions on machinery allocation and truck scheduling. Daily soil quantities that were previously estimated from truck counts and payloads are now recorded accurately based on measurement, improving the reliability of daily reports and facilitating smooth communication among contractors. Reviewing accumulated weekly or monthly point clouds provides objective evidence of progress and quantities for internal and external reporting, aiding schedule adjustments and project management.

• As-built verification and report generation: Upon completion of embankment work, the point cloud acquired by LRTK was compared with the design surface to verify as-built quantities (excesses/deficits). Automatically generated cut-and-fill volumes were used directly as as-built quantity tables, and cross-sections and 3D view images extracted from the point cloud were attached to reports for the client. Because point cloud data serve as immutable evidence, clients can immediately verify quantities without re-measuring on site, eliminating duplicated measurements. The LRTK cloud can output reports in prescribed formats (PDF) from acquired data, allowing generation of measurement reports that include photos, coordinates, and notes with a single click. Using this feature, you can easily create customized as-built documentation combining on-site photos and point clouds, improving reporting efficiency and quality.

• Safety management and special-case applications: Point cloud technology is effective for measurements in hazardous areas where people cannot safely enter. For slopes prone to collapse or disaster-struck sites, remote measurement with drones or LRTK enables accurate estimation of soil volumes that previously could only be approximated from a distance. There are cases where LRTK was used to estimate sediment accumulation after heavy rain, aiding rapid restoration planning. For post-construction management, repeated fixed-point point cloud acquisition records long-term changes like an electronic medical record. Comparing 3D data taken from the same position and angle over time reveals settlement and deformation trends numerically and visually, enhancing the persuasiveness of maintenance reports. These applications go beyond daily construction management but are enabled by the underlying capability to easily perform 3D scans on site.

Introducing simple surveying with LRTK into the field enables a system where you can measure whenever necessary and immediately share and report results. It drastically cuts labor and time while maintaining accuracy and reliability in as-built management, ultimately improving productivity, reducing costs, and enhancing safety across projects. By internalizing measurement tasks previously outsourced to specialists, construction management workflows accelerate dramatically. This is the effect of "completing earthwork volume calculation and point cloud measurement on site." Smartphone-based point cloud measurement and real-time as-built management are becoming standard procedures in future civil construction. Consider experiencing labor-saving earthwork management with smartphone scanning and LRTK at your site.

FAQ

Q1. Is the accuracy of earthwork volume calculation using point cloud data sufficient? A1. Yes. If point cloud data are properly acquired and processed, accurate volume calculations are possible. Generally, point clouds from photogrammetry or laser scanners, when calibrated with control points and captured at sufficient density, produce volume calculation errors comparable to conventional survey computations (within a few percent). Field comparison studies have reported differences on the order of about 1% between quantities derived from point clouds and those from traditional methods. However, ensuring accuracy requires that point cloud coverage has no gaps, non-ground points are removed, and coordinates are properly aligned. Satisfying these conditions makes point cloud-based volume calculation viable for field use.

Q2. Are specialized skills required to handle photogrammetry or LRTK? A2. Traditional photogrammetry required expertise and experience in parts, but recent solutions have become much easier to operate. Smartphone apps for scanning are intuitive: follow on-screen instructions to shoot and the app handles point cloud generation, so advanced shooting skills are not required. For LRTK, attach the antenna to the phone, launch the app, and follow on-screen guidance to automatically perform positioning, point cloud capture, and volume calculation. Users do not need to be familiar with technical terms or complex settings, and field personnel can start using the system after brief training. Data processing and analysis are automated on the cloud, so the user mainly needs to review results. Modern photogrammetry tools and LRTK are designed to be user-friendly so that field staff can operate them.

Q3. What equipment and environment are required to perform on-site point cloud measurement? A3. Fundamentally, a device with a high-quality camera (smartphone, tablet, drone) and supporting software/services are sufficient. For smartphones, recent iPhone or iPad models with LiDAR are desirable, though photogrammetry can be performed with ordinary cameras. For LRTK, you need a compatible smartphone, the LRTK antenna, and a mobile network connection to receive RTK correction data. Using drones requires a GPS-equipped drone and camera, plus flight permission and a qualified operator. Data processing typically requires cloud services or PC-based point cloud software. However, all-in-one services like LRTK allow capture through phone and antenna, with cloud upload and processing completing the workflow without a dedicated PC. Environmentally, clear lines of sight and adequate shooting positions are important for wide-area capture, and photogrammetry performs better in fair weather and with consistent lighting. For safety, avoid sending personnel into hazardous areas; use drones or poles/single-pole mounts for devices to maintain operator safety.

Q4. Which should be used for earthwork measurement: smartphone scanning or drone surveying? A4. Choose based on site scale and purpose. For large sites or areas with significant height variation, drones efficiently capture an overview point cloud in a short time. For confined sites, indoors, or detailed measurements, smartphone scanning provides agility. Smartphones require no takeoff/landing space and can be used in no-fly zones. If frequent measurements are needed, the ease of a smartphone lowers the operational barrier. In practice, both are often used together: drone surveys provide a rapid, broad base map while smartphone + LRTK captures detailed areas and changes for follow-up. Both point clouds can be integrated on a common coordinate system, so choose the optimal method according to site conditions and required accuracy/frequency. Either way, earthwork volumes can be calculated from the resulting point clouds, so select the tool that best fits your needs.