Table of Contents

• Relationship between earthwork volume calculation and point cloud data

• Features and challenges of in-field photogrammetry

• End-to-end flow from point cloud generation to volume calculation and required tools

• Comparison with conventional methods (total station and GNSS-only) (accuracy, personnel, time, reporting)

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

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

• Use cases on construction sites and integration with daily reports and deliverables

• FAQ

Relationship between earthwork volume calculation and point cloud data

On civil construction sites, calculating earthwork volumes for excavations and fills is an indispensable part of as-built management. Traditionally, terrain measured by surveying would be converted into cross sections, and volumes were commonly calculated using the average-end-area method or grid method. In recent years, however, the use of point cloud data (three-dimensional data composed of countless coordinate points) has made these volume calculations both more efficient and higher in accuracy. Point clouds acquired by 3D laser scanners or photogrammetry record fine surface details, allowing terrain to be reproduced nearly as it appears in reality. By comparing point cloud data of the terrain before and after construction, the quantities of fill and excavation can be directly calculated.

The principle of volume calculation using point clouds is simple: determine the volume difference between surface models before and after construction. For example, in an excavation project you compare the point cloud of the pre-excavation surface with the point cloud of the post-excavation surface and compute the volume of material that existed between them. Because point clouds can reconstruct the terrain as a surface from countless measured points, there is no need to interpolate between measured points as in conventional methods, enabling accurate quantity calculation that fully reflects terrain undulations. Also, once point cloud data are acquired, volumes can be recalculated any number of times by changing the calculation area or reference plane, allowing flexible re-evaluation under different conditions without additional surveying. For these reasons, point cloud–based volume calculation is superior in both accuracy and efficiency and is becoming a foundational technology supporting digital construction management.

To achieve higher-accuracy volume calculations, ensuring the quality of point cloud data is critical. This includes obtaining sufficiently dense point clouds with no unmeasured areas in the capture range, correctly aligning coordinates to the reference coordinate system, and properly removing or processing unwanted objects (such as machinery or trees). Meeting these conditions enables volume calculations with small errors. Field verifications have reported cases where quantities derived from point clouds differed from conventional manual surveying by about 1%, indicating that under proper operation, point cloud volume calculations are sufficiently reliable.

Features and challenges of in-field photogrammetry

Given the usefulness of point cloud data for earthwork management, a key challenge on-site is how to acquire those point clouds easily. Traditionally, specialized surveying equipment (terrestrial laser scanners or surveying drones) and surveying teams were required, but recent advances in photogrammetry have enabled field staff to capture point cloud data using smartphones or drones themselves. In-field photogrammetry refers to the method of taking photos on site and generating point clouds by 3D modeling from those images.

The advantage of photogrammetry is its accessibility: it enables wide-area measurement with familiar devices. Drone aerial photography can capture extensive image data from above in a short time, allowing measurement of steep or hazardous terrain without sending people into harm’s way. Even with smartphone photography, taking a sufficient number of photos by moving around the subject can generate high-density point cloud models using dedicated software. The ability to create point clouds with ordinary cameras without expensive laser scanners is a major benefit, and photogrammetry is attracting attention as a DX tool for construction sites.

However, photogrammetric point cloud acquisition has several challenges. First, measurement accuracy is influenced by shooting conditions. Low photo resolution or improper exposure, or many shadows and reflections on the subject, can cause errors or data gaps during point cloud generation. If the subject includes unwanted objects such as vegetation or debris, extracting only the ground surface from the generated point cloud can be laborious. Another challenge is that the workflow is not always easily completed on-site: even if shooting is quick, photo processing (point cloud generation) may take several hours on a high-performance PC or cloud service. Conventional photogrammetry often involves a time lag from surveying to data processing to volume calculation, making it difficult to obtain real-time results on site. When using drones, flight permission and operator skill are required, and smartphone-only photogrammetry risks misalignment with real-world coordinate systems due to GPS position errors. Even if you create a point cloud from smartphone photos, if the height datum and position of that point cloud are unclear, it cannot be used for as-built quantities. Therefore, using photogrammetry in actual construction management has required extra steps to ensure accuracy, such as tying to survey control points (placing ground control points) and ensuring sufficient photo overlap. While in-field photogrammetry has high potential, conventional technology alone has left some gaps in realizing the ideal of “anyone can easily complete high-accuracy point cloud measurement on-site.”

End-to-end flow from point cloud generation to volume calculation and required tools

So what steps and tools are required to generate point cloud data and calculate earthwork volumes? Below is a typical workflow.

• Data capture (measurement): To record the terrain or material conditions, photograph the site using a smartphone camera, a LiDAR scanner, or a drone-mounted camera. For photogrammetry, it is important to capture a sufficient number of images from all directions and ensure overlap between photos. For drones, set an automated flight plan to take photos at regular intervals; for smartphones, walk around the subject to take photos covering all gaps. Some modern smartphones (for example, models equipped with LiDAR) can complete point cloud capture in real time.

• Point cloud generation (processing): Use photogrammetry software or cloud services to generate a point cloud model from photos. When you feed photos into dedicated software, feature matching and angle calculations reconstruct the camera positions and 3D point coordinates, producing point clouds on the order of millions to tens of millions of points. This processing requires appropriate computing resources and time, but high-performance PCs and GPUs enable accurate, fast processing. Recently, services that automatically generate point clouds by uploading photos to the cloud have appeared; sending data from the field via the network can result in a completed point cloud by the time you return to the office.

• Georeferencing (position alignment): To use generated point clouds for volume calculations, they must be correctly positioned in the real survey coordinate system. Position alignment assigns scale, orientation, and vertical reference to the point cloud model. For drone photos, include pre-installed ground control points (GCPs) in the photos and use their known coordinates to align the model to real-world coordinates. For smartphone photos, you can shoot known site points and match them during post-processing, or—described later—use high-precision positioning functions to acquire data with absolute coordinates from the start. In any case, comparing point clouds from different times or aligning them with design data requires that the data share the same coordinate base. Properly performing this step converts the point cloud into useful survey data that can be used in GIS or CAD.

• Volume calculation: With aligned point clouds, specify the region to compute volume and perform the calculation. Typically, a TIN (triangulated irregular network) terrain model created from the point cloud is used for volume calculations. Specifically, you integrate volumes of fills or cuts relative to a reference plane, or overlay two different terrain point clouds as a differential point cloud to compute volume differences. Civil-focused 3D software or point cloud processing tools are used for these calculations, but once the procedure is set up, volume values can be obtained automatically. For example, to calculate fill volume within a certain area, specifying the polygon will let the software compute the upper and lower volumes of the point cloud inside it. Recently, open-source point cloud tools with volume calculation features and cloud services that display volumes in a web browser have appeared, making it possible to check as-built quantities via a browser without dedicated software.

• Sharing and reporting deliverables: Share calculated earthwork data on-site or compile it into reports for clients and stakeholders. Traditionally, results were organized into Excel sheets and cross-sections color-coded on drawings as needed. With point cloud utilization, you can go further: include visualizations of the point cloud model and differential volumes from a 3D viewer in reports or send cloud sharing links so stakeholders can interactively inspect the site. Sharing the underlying 3D data as evidence in addition to the calculation results is a major advantage of point clouds. Tools supporting this workflow include capture devices (smartphones, drones), photogrammetry conversion software, point cloud processing software (or cloud services), and viewers/sharing platforms. Recently, solutions integrating these tools into a single package have also emerged.

Comparison with conventional methods (total station and GNSS-only) (accuracy, personnel, time, reporting)

Approaches using point clouds differ significantly from conventional methods that rely solely on total stations (TS) or GNSS surveying instruments. Let’s compare differences in accuracy, personnel requirements, time, and reporting.

• Accuracy: Single-point positioning accuracy is better with TS or high-precision GNSS. TS achieves millimeter-level accuracy using prism-based distance measurement, and RTK-GNSS with a base station can restrict horizontal errors to within a few centimeters (a few in). However, accuracy in earthwork calculations is not just about small errors at individual points; it’s also about how well the overall terrain shape is captured. With TS/GNSS surveys, you typically sample elevation points on a grid at intervals of several meters (several ft) or place measuring points along cross-section lines. While the measured points themselves are accurate, fine undulations between points must be interpolated and may be missed. Point clouds, in contrast, are a huge set of points that continuously cover the terrain as a surface, capturing details down to every corner. Thus, small depressions or bumps can be captured by point clouds but may be missed by coarse conventional meshes. The overall volume calculation error is influenced by such undetected features, so under similar conditions point cloud–based calculations can reach accuracy comparable to conventional methods. In fact, reports from large-scale fill construction as-built verification showed differences of about 1% between point cloud–derived volumes and traditional average-end-area calculations, confirming adequate accuracy with point clouds.

• Personnel and time: Conventional methods required many manual steps from surveying to drawing creation to volume calculation. For instance, a large site might require a surveying team of four working a full week (a total of 20–30 person-days) to complete topographic surveying, cross-section creation, and volume calculations. Switching to photogrammetry plus point cloud processing, the same work was sometimes completed by two people in one day (2 person-days). A drone captured data in about 15 minutes and point cloud generation and volume calculation were done the same day. Thus, point cloud utilization dramatically reduces personnel and time required for surveying. One operator can handle a smartphone or drone, freeing other staff for different tasks. Given the current shortage of experienced surveyors, the ability of digital measurement to compensate for manpower shortages is highly significant.

• Reporting: There is also a large difference in reporting efficiency. Previously, survey results were manually compiled into drawings and tables, and preparing documents to prove as-built quantities took time. Large sites often involved complex internal checks and client inspections after surveying. Point clouds can act as direct evidence: presenting stakeholders with the point cloud model allows intuitive understanding of the terrain at that time and visually supports the basis for quantity calculations. Sharing color-coded difference maps or overlaying volume values in a point cloud viewer immediately communicates where and how much material is lacking or excess. Automatically generating cross-sections or plan views from point clouds and pasting them into reports reduces the preparer’s workload. In short, reporting that used to be laborious can be semi-automated. Point cloud usage speeds up the entire cycle from surveying to reporting and accelerates on-site decision-making.

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

Another strength of earthwork volume calculation is capturing terrain changes over time. Terrain changes daily on construction sites, and regularly acquiring point cloud data enables quantitative tracking of fill and excavation progress.

• Progress management: For example, if a site acquires drone imagery every weekend and preserves a point cloud model for each week, you can graph weekly increases in fill volume or display color-coded maps showing which areas gained fill compared to the previous week. Easier time-series comparison of earthwork volumes enables objective progress management. If progress is behind schedule, you can quickly arrange additional equipment or dump trucks, improving the PDCA cycle in construction management.

• Verifying design differences: From an as-built management perspective, comparing with the design model is important. With point clouds and the design finished-surface model (e.g., design ground surface data), you can check whether the site matches the design across the whole area. In excavation, you can identify places that have reached the required depth and places where material still remains; in fill work, you can find areas that exceed the specified elevation. Visualizing differences as a heatmap makes the results intuitive—for example, coloring areas higher than design in red and lower in blue highlights spots that need remediation. Showing surplus/deficit volumes spatially enables smooth directions for corrective work and helps prevent rework and material waste.

• Maintenance and disaster response: Accumulating time-series data is useful not only during construction but also for post-completion maintenance and emergency response. If you preserve the as-built point cloud at completion, you can quantitatively evaluate long-term changes by comparing current point clouds with earlier ones during periodic inspections. For example, if an embankment or fill structure is settling over time, you can calculate the settlement amount by differencing point clouds; in the event of a slope failure, pre- and post-event point clouds can estimate the volume of collapsed material. Traditionally, post-disaster surveys were done in person to compute damaged volumes, but remote point cloud measurement allows immediate estimation of landslide volumes even in hazardous locations, providing major advantages in safety and speed. Thus, differential use of point cloud data is applicable at all stages from pre-construction through completion and offers broad value beyond earthwork quantity management.

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

As mentioned, deploying photogrammetry on-site faced challenges, but new technologies have emerged to overcome them and make it possible for anyone to easily obtain high-accuracy point clouds on site. A representative example is LRTK. LRTK is a smartphone-integrated high-precision positioning system provided by Reflexia Inc. By attaching a dedicated small antenna to an iPhone and using network RTK corrections, LRTK improves smartphone positioning to centimeter-level accuracy (half-inch accuracy). This allows every point in point clouds acquired by the phone’s camera or LiDAR to be tagged with accurate coordinates, enabling smartphone-based measurements with precision comparable to surveying instruments. Previously, high-precision 3D measurement required drone + GNSS base stations or expensive laser scanners, but LRTK can replace those with a single handheld smartphone, which is revolutionary. It requires no specialized instrument operation knowledge and can be used by field technicians as an extension of their everyday tools. Compared to other 3D measurement methods, LRTK has lower introduction costs, requires no vehicle mounting or power setup, and excels in agility—being ready to measure whenever needed—making it well suited for frequent everyday measurements.

Organizing LRTK benefits into the aspects of shooting assistance, high-precision positioning, and cloud synchronization:

• Shooting assistance: The smartphone LRTK app generates a point cloud in real time and displays it on the screen, allowing operators to fill coverage gaps during shooting. For example, if a slope scan shows an occluded area not appearing in the point cloud, the operator can immediately take additional shots to supplement it. Even if scans are split across multiple sessions, the data are automatically aligned, enabling high-quality point cloud acquisition without specialized post-processing. The app also offers guides that display and navigate optimal shooting trajectories, and features for re-capturing from the same position and angle for fixed-point monitoring, so anyone on site can reliably measure without mistakes.

• High-precision positioning: LRTK’s standout feature is improving smartphone GNSS positioning accuracy. The LRTK antenna, compatible with network RTK, reduces the usual multi-meter smartphone GPS error to within a few centimeters (a few in). This attaches global coordinates to all captured point clouds and photos so that subsequent GCP-based correction is unnecessary and you can immediately use the data for volume calculations or drawing comparisons. Vertical information is also highly accurate, allowing direct use for elevation differences and cross-section creation. For example, LiDAR scans from an iPhone Pro, when used with LRTK, already have high-precision coordinates, enabling direct use of the measured data as as-built deliverables. Eliminating the positional alignment step that challenged conventional photogrammetry greatly simplifies the overall workflow.

• Cloud synchronization: LRTK provides a cloud service linked to the field app for automatic data sharing and storage. Point clouds and georeferenced photos captured in the app are uploaded to the cloud immediately after capture, eliminating the need to copy files to a PC via USB or perform format conversions after returning to the office. Uploaded point clouds can be displayed in a 3D viewer in the cloud, and analysis functions such as in-browser volume calculation and diagram generation are available. For example, a one-stop workflow of on-site smartphone scanning → automatic cloud processing → immediate volume result confirmation becomes possible, reducing time lag between capture and analysis to nearly zero. Accumulating data in the cloud enables centralized management of terrain changes from project start to finish, simplifying time-series comparisons. Past point clouds can be retrieved for reference and shared via links for collaborative checks. Immediate sharing of site information, which was difficult with paper forms, is now possible, strongly supporting faster and more efficient construction management.

In this way, LRTK is an integrated system that enables “easy smartphone point cloud capture → high-accuracy volume computation on-site → cloud data sharing.” It packages together solutions to technical hurdles for completing photogrammetry on-site (shooting skill, positioning accuracy, data processing environment) and is therefore a solution that contributes directly to labor reduction and efficiency improvements on construction sites.

Use cases on construction sites and integration with daily reports and deliverables

Finally, here are practical use cases of point cloud earthwork measurement on construction sites and how to apply them to daily reports and as-built reporting.

• Progress management and use in daily reports: On one development site, a site supervisor scanned stockpiles each evening with a smartphone + LRTK to immediately determine the amount of material removed that day. The obtained figures were recorded in the daily report and used to decide heavy equipment allocation and dump truck scheduling for the next day. Daily earthwork, which had been estimated from number of truckloads and capacities, could now be recorded with measured values, improving daily report reliability and smoothing information sharing among contractors. Reviewing weekly or monthly accumulated point cloud data also provides objective evidence of progress and quantities for internal and external reporting, aiding schedule adjustments and construction management.

• As-built verification and report preparation: At completion of a fill project, compare LRTK-acquired point clouds with the design surface to verify as-built surplus/deficit. Automatically computed cut/fill volumes can be used directly as an as-built quantity table, and including cross-sections and 3D views extracted from the point cloud in reports provides clear explanatory materials for the client. Since the point cloud data serve as indisputable evidence, the client can immediately verify quantities without re-measuring on-site with staff and tape measures. LRTK’s cloud enables quick generation of required charts and figures from acquired data, allowing immediate confirmation and agreement during as-built inspections.

Introducing simple surveying with LRTK on site makes it possible to measure whenever needed and promptly share and report results. This saves a significant amount of labor and time while ensuring accuracy and reliability in as-built management, contributing to overall productivity improvement, cost reduction, and enhanced safety. By internalizing measurements that used to be outsourced to specialists, construction management speed dramatically increases. This is the effect of “completing earthwork calculations and point cloud measurements on site.” Smartphone-based point cloud measurement and real-time as-built management are becoming standard processes in future civil construction. Please try smartphone scanning and LRTK-enabled labor-saving earthwork management 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 captured and processed, accurate volume calculations are achievable. Generally, point clouds derived from photogrammetry or laser scanners, when calibrated with control points and measured at sufficient density, produce volume calculation errors within the same range as conventional surveying calculations (within a few percent). Field comparative verifications have reported only very small differences (around 1%) between quantities calculated from point clouds and those from traditional methods. However, ensuring high accuracy requires prerequisites such as no gaps in the capture area, removal of non-ground points, and correct coordinate alignment. When these conditions are met, point cloud earthwork calculations are robust for field use.

Q2. Are specialized skills required to handle photogrammetry or LRTK? A2. Traditional photogrammetry required expertise and experience in some areas, but recent solutions are easier to operate. Smartphone apps for scanning are intuitive; following on-screen instructions to capture images will produce point clouds without special shooting skills. With LRTK, attach the antenna to a compatible smartphone, launch the app, and follow the guided workflow—positioning, point cloud capture, and volume calculation proceed automatically. Users do not need to know technical terms or complex settings; field personnel can start using the system after a short training period. Since data processing and analysis are automated on the cloud, users simply review results. Modern photogrammetry tools and LRTK are designed to be usable by anyone at the field-responsible level.

Q3. What equipment and environment are needed to perform on-site point cloud measurements? A3. Fundamentally, a device with a high-quality camera (smartphone, tablet, or drone) and supporting software/services are sufficient. For smartphones, recent iPhone or iPad models with LiDAR are preferable, but regular cameras can perform photogrammetry. Using LRTK requires a compatible smartphone, the LRTK antenna, and a mobile network environment (to receive RTK correction data). For drone surveys, a GPS-equipped drone and camera are needed, along with flight permissions and a qualified operator. In any case, a cloud service or PC software for processing captured data is required. However, all-in-one services like LRTK let you complete capture to cloud storage with only a smartphone and antenna, eliminating the need for a special PC. Environmentally, good visibility and adequate shooting positions are important for wide-area capture. Photogrammetry is affected by weather and lighting, so performing captures in clear conditions and with favorable lighting improves accuracy. For safety, avoid having personnel enter hazardous slopes—use drones instead, or use monopods/poles to capture from unsafe positions to protect surveyors.

Q4. Should I use smartphone scanning or drone surveying for earthwork measurement? A4. Choose based on site scale and purpose. For large sites or many high areas, drone surveying efficiently obtains overhead point clouds in a short time. For confined areas, interiors, or detailed measurements, smartphone scanning offers great mobility. Smartphones require no takeoff/landing space and can be used in flight-restricted areas. They also lower the barrier for frequent routine measurements. In practice, both are often used together: use a drone to survey the entire site and create a base map, then supplement and track detailed changes with smartphone + LRTK scans. Point clouds from both sources can be integrated into a common coordinate system, so select the method that best fits site conditions and required accuracy/frequency. Either way, you can perform volume calculations from the obtained point clouds—use the approach that matches your site’s needs.