Table of Contents

• Introduction

• The New Trend in Earthwork Volume Calculation Using Photogrammetry and Point Clouds

• Photogrammetry vs. Laser Scanning: Comparison of Methods and Accuracy

• Point Cloud Surveying Leaps Forward with High-precision GNSS

• Procedure for Calculating Earthwork Volumes Using Point Cloud Data

• MLIT As-built Management Guidelines and CIM Utilization

• LRTK-Enabling Simple Surveying and AR Construction Support

• FAQ

Introduction

In civil engineering, earthwork volume calculation is a crucial process for accurately determining how much earth has been moved during embankment or excavation. Traditionally, ground elevations were measured before and after construction to create cross-sections and volumes were calculated using the average end area method. However, this approach relied heavily on manpower and manual calculations, required great effort, and as the area increased, variability in accuracy and workload became problematic.

Recently, attention has focused on calculating volumes using point cloud data. By processing the countless 3D survey points (point clouds) obtained from scanning the entire site, ground volumes can be measured quickly and with high accuracy. In particular, advances in photogrammetry and the widespread adoption of drones have made it possible to acquire detailed point clouds of large earthworks sites in a short time. Furthermore, combining this with high-precision GNSS (such as RTK) dramatically improves and streamlines the positioning and coordinate assignment of point clouds, allowing as-built conditions to be determined more rapidly than ever before. This article explains the innovativeness of as-built measurement using the combination of photogrammetry and high-precision GNSS as the latest method for "earthwork volume calculation × point clouds," along with concrete procedures and practical tips for utilization.

The New Trend in Earthwork Volume Calculation Using Photogrammetry and Point Clouds

Using point clouds for earthwork volume calculation significantly improves both efficiency and coverage compared to traditional methods. For example, at a large-scale site where four people used to spend seven days measuring and calculating volumes, switching to a drone photogrammetry-based point cloud workflow reduced the task to just one day with two people. This shortened the work time to about 1/14 while the calculated as-built quantities showed errors of only around 1% compared to the traditional method, confirming high accuracy. Because point cloud data capture fine surface irregularities, overlooked features are reduced and reliable volume estimation is possible.

Point cloud–based volume calculation is also useful for site progress management. For instance, if you calculate the as-built volume immediately after excavation, you can recognize discrepancies with the design quantities on the spot. Decisions such as arranging additional backfill or planning disposal of surplus soil can be made promptly without halting work. Traditionally, as-built quantity management tended to lag due to the time required to compile survey data, but point clouds allow near real-time numerical representation of site conditions. In today’s environment where DX (digital transformation) is demanded, this becomes a powerful tool for construction management.

Moreover, since the acquired point cloud itself is a precise 3D record, it is easy to perform additional volume calculations for arbitrary areas after measurement. Once scanned, requests like “recalculate the volume for a particular section” can be handled immediately as desk work. Detailed as-built comparisons that were difficult with paper drawings or photos can be freely performed on digital point cloud models. For these reasons, using point clouds to calculate volumes is becoming the new standard in civil construction.

Photogrammetry vs. Laser Scanning: Comparison of Methods and Accuracy

Representative methods for acquiring point clouds are photogrammetry and 3D laser scanning. Each has its characteristics, but in recent earthwork volume calculation practice, photogrammetry has been increasingly adopted for cost and convenience reasons. Below is a brief comparison of the two.

• Photogrammetry: A technique that reconstructs 3D shapes from numerous photos taken with a camera. By photographing a site from various angles with UAVs, single-lens cameras, or smartphones, and analyzing feature points in specialized software, a point cloud is generated. Because the model is based on color photos, it is visually intuitive and the equipment is primarily cameras, making it relatively inexpensive. Large areas can be photographed quickly, and data collection from the air makes it easy to cover steep slopes or high places that are hard to access. However, to achieve high accuracy you must shoot with sufficient resolution and overlap, and the subject needs adequate textures or distinguishing features. Glass, water surfaces, or uniformly white walls may lack detectable feature points and be difficult to reconstruct. Photogrammetry’s algorithms are affected by camera placement and shooting angles, and vertical accuracy tends to be lower than horizontal accuracy. Therefore, combining photogrammetry with control points (ground control points) or high-precision GNSS for positional correction can achieve survey accuracies on the order of several centimeters, as discussed later.

• 3D Laser Scanning: A method that directly measures distances to targets by emitting laser light to acquire point clouds. There are various types, including ground-based fixed 3D laser scanners, vehicle- or drone-mounted mobile scanners, and handheld scanners. Laser scanning can acquire a very high-density point cloud—hundreds of thousands to millions of points per second—and performs well in low-light conditions, being less affected by ambient light. The per-point ranging error can be on the order of millimeters (for high-performance instruments), providing very high accuracy and generally stable accuracy compared to photogrammetry. Laser scanning excels in vertical accuracy and in capturing ground surfaces under vegetation, and is reliable for complex terrain or nighttime surveys. However, laser scanners are expensive, and large models require specialist handling. They also struggle with reflective or transparent materials like glass or mirrors and with very dark absorptive objects. Overall, photogrammetry has advantages in initial cost and operational ease, while laser scanning often outperforms in precision and environmental adaptability. In practice, it is ideal to selectively use or combine both methods to mitigate each other’s weaknesses depending on site conditions and required accuracy.

Point Cloud Surveying Leaps Forward with High-precision GNSS

What dramatically improves the positional accuracy weakness of photogrammetry is the use of high-precision GNSS. By itself, a point cloud generated from photogrammetry may be floating in an arbitrary coordinate system and could be offset from real-world geodetic coordinates. Traditionally, multiple ground control points were placed at the site and their marker positions were used to adjust the model and assign absolute coordinates. This process takes time, but using RTK-GNSS (real-time kinematic positioning) enables a smarter path to high accuracy.

RTK-GNSS is a surveying method that adds correction information from a ground base station to GNSS satellite positioning to reduce positioning error to the centimeter level or better. With improvements in communications infrastructure, network RTK services are now available nationwide, making centimeter-level positioning feasible. Installing an RTK receiver on a drone allows the recorded shooting positions (geotags) for each aerial photo to be highly accurate, and the point cloud generated by photogrammetry is obtained in an accurate coordinate system from the start. For ground-based photography, recording shooting positions with a GNSS device synchronized to the camera allows the model to be automatically aligned to true coordinates in post-processing. In other words, it’s like “performing surveying at the same time you take the photos.”

The biggest advantage of combining high-precision GNSS is that it reduces the effort of coordinate alignment in the point cloud workflow. Time spent installing control points and surveying known points can be greatly reduced, simplifying the work flow. Additionally, the resulting point cloud can be immediately overlaid with electronic maps and design data for smooth downstream usage. For example, an as-built point cloud acquired by an RTK-equipped drone can be directly compared with a 3D design model (BIM/CIM model) for as-built inspection without coordinate transformation. Combining the convenience of photogrammetry with GNSS accuracy enables efficient, high-precision as-built measurement even for large areas.

Procedure for Calculating Earthwork Volumes Using Point Cloud Data

Now, let’s confirm the concrete procedure for calculating earthwork volumes using point cloud data. In earthworks, the basic calculation is to find the “volume difference between a reference surface and the current surface.” A surface model is created from the point cloud, and the volume is derived by comparing the surfaces.

• Acquisition of current conditions by point cloud survey: First, acquire the terrain of the target area as point cloud data. Conduct surveys at each timing you want to compare volumes—for example, before and after construction or before and after embankment. The method can be drone photogrammetry or ground laser scanning; the key is covering the terrain with sufficient extent and density. Clean the acquired point cloud by removing unnecessary noise and distant objects (such as machinery or people) to prepare it for analysis.

• Extraction of ground surface and DTM creation: Extract only the points corresponding to the ground surface (terrain) from the point cloud. If points for construction equipment, trees, or structures are included, remove them using filtering or automatic classification features. With the ground point cloud, generate a digital terrain model (DTM) such as a TIN (triangulated irregular network) or mesh model. Representing the ground’s undulations with a network of triangles allows calculation of areas and volumes.

• Setting the comparison surface (reference surface): Prepare the reference surface to compare with the current terrain. Depending on the case, typical approaches include comparing “pre-construction original ground” with the “post-construction as-built ground” to determine volume changes, or comparing the “design planned ground level” with the “as-built ground” to check excesses and shortages. In any case, convert the comparison target into a DTM from point clouds, or create it from CAD drawings or a BIM model if using design data.

• Difference calculation and volume computation: Once the two surfaces—the current surface and the reference surface—are ready, overlay them and calculate the volume difference. Specifically, compute the vertical distance (height difference) between the two surfaces and integrate it over the area to obtain the volume. Specialized earthwork calculation software or 3D analysis tools can automatically compute the differential volume between surfaces. The calculation results will provide the total volume change, and you can also identify area-specific fill volumes (positive volumes) and excavation volumes (negative volumes).

• Verification and utilization of results: Perform accuracy verification on the calculated volumes as needed. For example, set independent verification points at key locations and compare their height differences with the point cloud difference results to check errors. If accuracy is assured, reflect the volume calculation results in as-built quantity determinations and construction management documents. Also, a benefit of point cloud–based volume calculation is that the 3D model used as the calculation basis can be saved as evidence. If later there is a disagreement with a supervisor, you can present the point cloud data and explain, “This is the shape and this is the volume,” facilitating smooth consensus building.

This is the general workflow. The point is that point cloud–based measurement captures large areas at high density, so there are fewer “hidden errors.” Small depressions in terrain that sectional methods might miss are reflected, improving volume estimation accuracy. Also, once modeled, changing the calculation area or recalculating can be done with the push of a button, enabling efficient quantity calculation without rework.

MLIT As-built Management Guidelines and CIM Utilization

The government has noted the effectiveness of as-built measurement and volume calculation using point clouds and is gradually establishing official operational standards. Since the late 2010s, the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) has promoted ICT-utilized construction as part of the i-Construction initiative, encouraging the introduction of 3D technologies on sites. For example, a guideline titled “Guidelines for As-built Management (Earthworks) Using Aerial Photogrammetry (UAV)” (2020 draft) describes procedures and accuracy control methods for using drone photogrammetry point clouds in as-built management. It specifically covers high-precision shooting methods using RTK-GNSS, and error verification using control points and validation points, laying out a path to treat photogrammetric point clouds as an official method for as-built measurement. With the preparation of such standards, contractors can adopt point cloud technology with greater confidence. When measurements are performed in accordance with the as-built management guidelines, there are increasing cases where point cloud–derived deliverables are accepted as submission documents by clients.

In addition to as-built measurement, there is movement toward utilizing CIM (Construction Information Modeling). CIM, the civil-engineering counterpart to BIM, is an initiative to use 3D models consistently from design through construction and maintenance. If as-built terrain obtained by point clouds is incorporated into a CIM model, it becomes easy to compare with design models and repurpose the data for maintenance management. For example, you can overlay the as-built point cloud on design data to verify quality, or use the completed 3D terrain for planning future renovation work; digital data enables life-cycle reuse. In the context of CIM, point cloud data becomes more than measurement results—it represents a “digital record of construction.” MLIT has positioned point cloud and 3D model utilization in CIM guidelines following the as-built management guidelines, and future standardization of 3D data submission is anticipated. For surveying and management engineers on site, point cloud skills will become increasingly important.

LRTK-Enabling Simple Surveying and AR Construction Support

A new technology that further lowers the barrier to point cloud utilization is the emergence of high-precision surveying devices combining smartphones × compact GNSS. A representative approach is LRTK. LRTK mounts a palm-sized RTK-GNSS receiver on a smartphone and, with a dedicated app, enables photo capture and point cloud scanning as a turnkey solution. This allows centimeter-class 3D measurement—previously requiring expensive surveying equipment—to be easily achieved with an everyday smartphone.

Using LRTK significantly streamlines small-site and routine as-built checks. For example, a person can walk around a developing embankment with a smartphone and scan it to generate a high-precision 3D model with coordinates on the spot. Data can be shared to the cloud immediately, enabling volume and distance measurements and decisions to be made without returning to the office. By enabling contractors to perform as-built surveying in-house in a short time—work that was previously outsourced—LRTK becomes a powerful tool for small and medium construction companies to advance DX.

Moreover, mobile measurement devices like LRTK have high affinity with AR (augmented reality) technology, and are expected to evolve into construction support tools. If you overlay the acquired point cloud data or design models onto real scenery through a smartphone or tablet screen, you can intuitively perform as-built checks and stakeout tasks. For example, if you hold up a tablet over an in-progress embankment and display color-coded differences between the design model and the as-built point cloud, you can instantly see where to cut and where to fill. AR visual support helps prevent mistakes while fully leveraging the accurate data obtained from point cloud measurement. As user-friendly measurement technologies become more widespread, a time will come when everyone naturally handles 3D data.

FAQ

Q1. How accurate is earthwork volume calculation using point clouds? A1. It depends on measurement conditions, but combining drone photogrammetry with RTK-GNSS generally yields accuracy on the order of several centimeters. In practice, there are cases where volumes calculated from photogrammetric point clouds were within ±1–2% of volumes from traditional methods. Point clouds from laser scanners can achieve millimeter-level accuracy, but for typical earthwork as-built quantity management, photogrammetry + RTK is sufficiently practical.

Q2. Is a drone mandatory for photogrammetry? A2. Not necessarily. Aerial photogrammetry is effective for quickly capturing large areas at big sites, but photogrammetric point clouds can also be generated from close-range photos taken with a smartphone or handheld camera. Recently, solutions that enable easy high-precision point cloud creation by mounting compact GNSS units on smartphones (e.g., LRTK) have emerged. Choose the optimal shooting method depending on site scale and target.

Q3. Does MLIT’s as-built management guideline accept photogrammetry? A3. Yes. MLIT has provided trial guidelines for UAV photogrammetry–based as-built management, and if certain conditions are met, as-built evaluation using photogrammetric point clouds is permitted. Specifically, the process requires accuracy verification using validation points before and after shooting, proving that errors are within prescribed ranges (for example, about 5 cm in the vertical direction). Regional bureaus and local governments have started operating according to these guidelines, so check the latest procedures before use.

Q4. What are the advantages and disadvantages of photogrammetry compared to laser scanning? A4. Photogrammetry’s advantages are lower equipment cost and rapid, wide-area measurement. It also produces intuitively understandable 3D models from color images. Drawbacks include sensitivity to shooting conditions (lighting and surface texture), leading to reduced quality at night or on texture-less targets, and longer processing times. Laser scanners require a higher initial investment but provide stable high-precision measurement and are better at acquiring data in low light or beneath vegetation. In short, photogrammetry is for “speed and convenience,” while laser scanning is for “accuracy and versatility.”

Q5. Can point cloud volume calculation be done without special software or skills? A5. User-friendly point cloud processing software and cloud services have increased recently, and semi-automatic volume calculation environments are becoming available even to non-specialists. Some drone manufacturers’ analysis software and construction-oriented 3D volume calculation tools will perform point cloud generation to volume calculation automatically simply by uploading photos. However, it remains important to follow appropriate measurement procedures and conduct accuracy verification. While accessibility has improved, basic surveying knowledge and habits for data checking are still advisable.