How to Improve Point Cloud Accuracy for Earthwork Volume Calculations: Using LRTK to Align Photogrammetry Point Clouds to cm-level Coordinates (cm level accuracy; half-inch accuracy)

When calculating earthwork volumes on construction sites, the use of point cloud data obtained by drones or photogrammetry is becoming more common. However, if the accuracy of the point cloud is insufficient, errors in volume calculation can occur and lead to misjudgments about as-built conditions and quantities. This article explains how to improve the accuracy of point cloud data obtained by photogrammetry so that earthwork volumes can be calculated with cm-level accuracy (cm level accuracy; half-inch accuracy). We focus especially on practical procedures and tips for using LRTK (a high-precision GNSS positioning technology) to align point clouds acquired by smartphones or drones to the site coordinate system.

Table of Contents

• Why earthwork volume calculation is important

• How photogrammetry point clouds work and their advantages

• Accuracy issues of photogrammetry point clouds and causes of error

• Main factors that affect point cloud accuracy

• How to use LRTK to improve point cloud accuracy to cm-level

• Applications of high-precision point cloud data for as-built control and volume calculation

• Ideas for small-scale earthwork measurement using smartphones

• Summary: the first step toward high-precision surveying anyone can do

• Frequently Asked Questions (FAQ)

Why earthwork volume calculation is important

Earthwork volume calculation in earthworks is an extremely important process for site construction management and as-built control. Accurately knowing excavation and fill volumes provides the following benefits:

• As-built control and quality assurance: You can confirm whether construction is being carried out with the design quantities and check whether the final shape (as-built) has any deficiency or excess. Insufficient or excessive filling/excavation should be corrected early.

• Quantity confirmation and schedule management: Because daily progress (quantity completed) can be captured numerically, you can manage whether the required earthwork volume will be secured within the schedule and whether arrangements for surplus soil disposal or backfill materials are appropriate.

• Reporting and acceptance to the client: You can present volumes based on objective data in final as-built drawings and quantity reports. Presenting quantities supported by measured data rather than vague estimates facilitates shared understanding and smoother acceptance with the client and inspectors.

Thus, earthwork volume calculation supports site operations in terms of quality, cost, and reliability. However, accurately measuring large-area earthworks is not easy, and traditionally it required time-consuming manual work such as surveyor-staked benchmarks and cross-section drawing. Recently, point cloud data from photogrammetry has attracted attention. By photographing the site with drones or smartphones and generating point clouds, highly dense datasets consisting of millions of points can enable accurate volume calculations in a short time. However, if point clouds are not processed correctly, errors can arise, so ensuring accuracy with appropriate methods is essential.

How photogrammetry point clouds work and their advantages

Photogrammetry is a technique that reconstructs the three-dimensional shape of objects from multiple photographs. By photographing the site from various angles with a drone-mounted camera or a smartphone and using software to find matching points (features) between images, camera positions and the 3D coordinates of the features are calculated. This process (SfM: Structure from Motion) automatically generates high-density point cloud data (a collection of many 3D points) from photos without the need to physically measure the site.

Photogrammetry point clouds have the following advantages:

• Detailed shape recording: Surface topography and structures can be captured with millimeter- to centimeter-level accuracy. For example, creating a terrain model from drone aerial photos makes it possible to identify small undulations that would be overlooked with conventional methods. This translates to volume assessments that faithfully reflect surface undulations and slopes for earthwork calculations.

• Non-contact, safe measurement: Because the site is photographed remotely, surveys of steep slopes or high places can be performed safely. Drones can capture areas that workers cannot enter, contributing to improved safety and efficiency since site workers do not need to measure directly.

• Short time and reduced manpower: A single drone can photograph tens of hectares in tens of minutes, and point cloud generation can be automated. Being able to perform earthwork measurement that used to require manual labor in a short time with fewer people is a major advantage. Especially amid serious labor shortages, ICT-driven labor savings directly improve site productivity.

• Low cost and flexibility: Commercial drones or smartphones are sufficient for photography in many cases. Compared to using expensive laser scanners, initial costs can be reduced, and photogrammetry can flexibly cover small to wide sites. Accumulated capture data can also be reused for later analysis or drawing creation.

For these reasons, photogrammetry-derived point cloud data is increasingly being used in civil surveying and as-built control. However, there are challenges to be overcome to use photogrammetry point clouds correctly for earthwork calculation. The next section discusses accuracy-specific issues unique to photogrammetry point clouds.

Accuracy issues of photogrammetry point clouds and causes of error

Point cloud models generated by photogrammetry software may look precise at first glance, but their absolute position and elevation may be incorrect. The main causes are:

• Remaining in a relative coordinate system: A model created by photogrammetry initially remains in an arbitrary scale and coordinate system. That is, while the model is internally consistent, it may be offset in position or scale relative to real-world map coordinates. For example, even if you photograph and point-cloud an area that is 10 m (32.8 ft) square, the resulting model may be several meters off from the site coordinates or have a slight scale difference (e.g., an actual 10 m (32.8 ft) appearing as 9.8 m within the model). Scale errors and position offsets mean the model cannot be used directly for correct volume calculations.

• Vertical (height) offset: Height-direction errors, especially elevation errors, are easily overlooked. Simply photographing with a camera that has ordinary GPS can cause altitude information to be off by several meters (several ft). As a result, the generated point cloud's ground elevation may be tens of centimeters to several meters higher or lower than reality, which greatly affects volume calculations. For example, on a 100 m (328.1 ft) × 100 m (328.1 ft) flat site, a uniform 0.2 m (0.7 ft) vertical offset would produce a simple volumetric error of 200 cubic meters. Even a small height offset can lead to a non-negligible earthwork volume difference over a large area.

• Differences in coordinate systems: If the coordinate system of the design drawings or known points does not match the point cloud data's coordinate system, errors will occur. In Japan, public works often manage coordinates in public coordinate systems (e.g., plane rectangular coordinates) or local site coordinates, but photogrammetry software outputs may default to WGS84 latitude/longitude or an arbitrary system. Without conversion, the model will not match the design data, so coordinate transformation or model translation/rotation is needed. Incorrect transformation can cause positional differences of several centimeters to more than ten centimeters depending on location, leading to inaccurate volume calculations.

• Insufficient control points (GCPs): Ground control points (GCPs) are important for georeferencing photogrammetry models. Without a sufficient number of GCPs to constrain the entire model, the model may tilt or warp slightly. For example, when photographing a large reclaimed area, a “bowl” deformation where the model drops toward the edges has been reported. This occurs when there are no appropriate control points at the corners or center, allowing the freely computed model to curve. Insufficient or unevenly distributed control points can cause localized accuracy degradation.

For these reasons, acquired point cloud data may not be reliable for direct earthwork calculations. In practice, issues such as “volumes calculated from point clouds do not match conventional survey values” or “the point cloud is offset when overlaid on as-built drawings” are not uncommon. It is therefore important to give the point cloud the correct scale and coordinate reference. The next section organizes the factors that determine point cloud accuracy and the key points to improve it.

Main factors that affect point cloud accuracy

Photogrammetry point cloud accuracy is determined by the following main factors. Pay attention to each to ensure accuracy suitable for earthwork volume calculations.

• Image resolution and overlap during capture: The finer the source photos' quality and resolution (GSD: ground sampling distance), the more detailed and accurate the point cloud. For example, a point cloud generated from imagery with a ground resolution of 2 cm/px (0.8 in/px) captures finer details and has smaller height and shape errors than one from 5 cm/px (2.0 in/px). However, physical limits apply to image resolution, and final accuracy is generally bounded by the GSD (if 1 px is about 2 cm (0.8 in), model error on the order of 2 cm (0.8 in) is a reasonable expectation). Overlap during capture is also important. Planning flight paths so that photos overlap sufficiently in the forward and side directions stabilizes the SfM analysis and yields higher-accuracy point clouds. Generally, forward overlap of about 80% and side overlap of about 70% is recommended. Ensuring resolution and overlap is the first step to creating high-quality point clouds.

• Georeference (positioning) accuracy: The accuracy of georeferencing the point cloud to the correct position and elevation is essential. The accuracy of the location information (geotags) attached to images and the survey accuracy of reference points directly affect the model accuracy. Position logs from ordinary consumer GPS commonly have errors on the order of meters, but using RTK-GNSS (discussed below) can record each photo’s position with centimeter-level accuracy. Using photos with high-precision geotags lets you bring the model close to real-world coordinates with fewer control points. Conversely, coarse location info has inherent limits even if corrected with many control points, so combining high-precision coordinate data is crucial.

• Number and distribution of GCPs (control points): In conventional methods, appropriately setting GCPs is fundamental for improving accuracy. Generally, at least 3 points for planar alignment plus 1–2 points for height adjustment — about 5 points total — is recommended. Photogrammetry software like Pix4D also recommends around 5–10 points. Place GCPs to surround the site as much as possible (corners and near the center), and if there is elevation difference include control points at high and low locations to stabilize vertical accuracy. However, placing an excessive number of points may yield diminishing returns relative to effort. It is important to place a necessary and sufficient number of points in a balanced way. Recently, RTK-equipped drones have enabled practical “GCP-less” surveys, but having a few GCPs for verification is advisable.

By addressing these three elements, you establish the foundation to dramatically improve photogrammetry point cloud accuracy. The next chapter focuses on using GNSS-based alignment, introducing LRTK as a solution to achieve cm-level accuracy easily.

How to use LRTK to improve point cloud accuracy to cm-level

Traditionally, achieving a point cloud model within a few centimeters error required specialized surveying equipment or many GCPs. However, by using LRTK (a compact RTK-GNSS device) that has recently emerged, anyone can easily obtain cm-level reference point coordinates. Here we explain methods and procedures to use LRTK to high-precision the photogrammetry point cloud.

What is LRTK?

LRTK is an all-in-one GNSS receiver that brings RTK positioning, which used to require large setups, to smartphones easily. Real-Time Kinematic (RTK) technology corrects satellite positioning errors and reduces positioning error to the centimeter or sub-centimeter level. LRTK devices are compact receivers that attach to smartphones or tablets and obtain positioning data via a dedicated app. In Japan, network RTK correction data such as the Quasi-Zenith Satellite System’s centimeter-class augmentation service (CLAS) or VRS are available, so high-precision positioning is possible without deploying your own base station. In short, the strength of LRTK is that a smartphone plus a pocket-size receiver can provide position coordinates comparable to first-order benchmark surveying.

With LRTK, you can measure the coordinates of points you need on-site immediately. Measured point coordinates can be automatically converted and recorded in any coordinate system (e.g., Japan’s plane rectangular coordinates) via the app, simplifying post-processing coordinate calculations. By turning RTK surveying that used to require specialist skills into a tool that site technicians themselves can handle, LRTK is an innovative device supporting construction DX.

Workflow to improve point cloud accuracy using LRTK

Now let’s see the concrete steps to use LRTK to align photogrammetry point clouds to cm-level coordinates. The general flow is “Preparation ▶ Capture ▶ Processing ▶ Verification.”

• Preparation (positioning setup and control point measurement): If you will capture aerial photos with a drone, plan the flight area, altitude, and capture intervals and prepare the aircraft and camera. At the same time, attach the LRTK receiver to your smartphone, launch the dedicated app, and confirm receipt of correction information (network RTK or CLAS). If the drone is not RTK-equipped, set and measure several control points on site at this stage. For example, place markers on survey manholes or immovable structures (or use existing boundary stakes), and measure their coordinates with LRTK. These will serve as GCPs for later model alignment; measure 3–5 points and place them balanced around the site (corners, center, and areas with elevation differences).

• Capture (photo acquisition): Use automated drone flight to photograph the entire site. As mentioned, set overlap to about 80% forward and 70% side and plan flight paths to capture ground uniformly. If using an RTK-capable drone, each photo will automatically be geotagged with high-precision coordinates. Even with a regular GPS drone, pre-measured control points are sufficient. As needed, supplement with ground photos of areas drones may miss (shaded slopes, underside of bridge girders) taken with an LRTK-equipped smartphone. If you use a smartphone with an integrated LRTK receiver, each photo you take with the phone camera can be automatically tagged with cm-level coordinates, allowing the drone and smartphone images to be processed in a unified coordinate system. When all captured photos have high-precision position information, the subsequent processing becomes much smoother.

• Processing (point cloud generation and coordinate alignment): Import captured images into photogrammetry software (or cloud service) and run 3D point cloud generation (SfM). Typically, the software calculates camera positions, orientations, and the point cloud via feature matching; here you utilize the LRTK-derived high-precision geotags. Specifically, by providing each photo’s initial position at cm accuracy, the resulting point cloud model is output already aligned to the real-world coordinate system. If you measured GCPs in advance, input their coordinates and perform fine adjustments and accuracy checks (mark the GCPs in the photos within the software and compare with known coordinates). Proper processing yields a point cloud whose positions and elevations agree within a few centimeters. Note that there are cloud services that automate point cloud generation by simply uploading images, so even without a high-performance PC you can generate point clouds and orthophotos within an hour or two and review results on the same day.

• Verification (accuracy checks and use of results): Check the accuracy of the generated point cloud model. When GCPs were used, review the software’s error reports for each GCP (reprojection error, checkpoint error) and confirm that errors are within acceptable ranges (for example, a few centimeters horizontally and vertically). Comparing verification points measured with LRTK against corresponding points on the point cloud to assess height differences is also effective. Once accuracy is confirmed, utilize the point cloud for volume calculations and as-built mapping (see next chapter for specifics).

This LRTK-incorporated photogrammetry workflow simplifies the traditional heavy labor of GCP surveying and coordinate alignment by enabling quick on-site acquisition of a small number of reference points and reflecting them in the software processing. Even with non-RTK drones, you can use pre-measured LRTK control points or mix in photos taken with an LRTK-equipped camera to achieve RTK-drone-like accuracy. In practice, appropriate operation can reduce the number of required control points from more than ten to around three, and in some cases produce a practically usable model with zero GCPs. LRTK thus streamlines the georeferencing task that was a weakness of photogrammetry, achieving both accuracy and labor savings.

Applications of high-precision point cloud data for as-built control and volume calculation

Once LRTK yields a high-precision 3D point cloud aligned to site coordinates, you can apply it to various management tasks. From creating as-built drawings to quantity aggregation, point cloud data becomes a powerful tool for digital construction management. Main applications include:

• As-built heatmap display: Overlay the design model or design surface data with the acquired current point cloud and visualize the differences with a color map. If each point on the point cloud is color-coded by how many centimeters it is above or below the design surface, you can immediately see areas of excess fill or excavation. For example, color the design-compliant range green, overfilled areas red, and over-excavated areas blue, so you can intuitively grasp the finishing status over wide areas. This helps determine “where and how much to cut to meet the design surface” or “where unevenness occurs,” enabling early correction of construction errors and preventing rework. Heatmaps are also effective visual aids for as-built reports to clients.

• Rapid quantity calculation (earthwork volume calculation): With a high-density point cloud, software can automatically compute volumes for arbitrary areas. For example, by calculating differences between the current point cloud and the design surface, you can instantly compute fill or excavation volumes. Where conventional methods required creating cross-sections from survey data and sequentially calculating volumes, point clouds dramatically shorten this process. By capturing daily fill/excavation digitally, you can immediately check on site “how many cubic meters more are needed to reach design elevation” or “how many cubic meters have been excavated already.” Combining cloud processing, you can even measure thousands of cubic meters in near real time on a tablet in the field. This greatly improves the speed and accuracy of progress-based quantity verification and makes acceptance and quantity settlement smoother.

• Quality inspection and records through point cloud data: SfM point clouds provide a detailed record of current conditions, so they can be used for comprehensive checks of as-built dimensions and construction accuracy. For roadworks, you can measure longitudinal profiles, widths, and heights at arbitrary locations on the point cloud and verify conformity to the design. For concrete structures, you can comprehensively verify as-built dimensions by scanning the entire surface after placement. Because point clouds contain surface measurement data, areas that formerly relied on interpolation between survey points can now be accurately assessed. Saving high-precision point clouds as electronic delivery data or inspection records also helps ensure traceability and serve as quality evidence later. Using point clouds in this way enables a data-driven PDCA cycle for construction management, improving the precision and reliability of quality control.

Such digital utilization is expected to dramatically improve efficiency in both construction management and quantity management. By sharing point clouds and orthophotos obtained on site via the cloud, office staff and clients can grasp and review the latest status without being on site. As high-detail 3D data can now be viewed and measured in web browsers, information transfer and consensus-building speed also improve. Photogrammetry combined with LRTK is reshaping site workflows.

Ideas for small-scale earthwork measurement using smartphones

While drone aerial photography is effective for large sites, small excavations or fills can be measured with a smartphone alone. Recent improvements in smartphone camera performance and the emergence of devices with LiDAR scanners (e.g., some iPhones and tablets) make it easy to acquire point clouds. Here are some smartphone measurement ideas you can use on site:

• Simple point cloud generation from smartphone photos: For small soil piles or trenches, take many photos around the target with a smartphone and later process them into a point cloud using software. Walk around the target and take 10–20 or more photos (ideally surrounding the object and varying vertical angles). Process these on PC with free SfM software or on a smartphone app to generate a simple 3D model. Important: scale alignment. Include some real-world length information in the photos, such as a ruler or a rod of known length, and scale the model afterward so that that length matches. More reliably, measure one or more reference points with LRTK to adjust the model’s position and elevation. Even a single reference coordinate from smartphone photogrammetry can bring the model very close to actual dimensions.

• Smartphone LiDAR scanning: If you have a recent smartphone or tablet with LiDAR, dedicated apps can directly scan and produce point clouds or mesh models. For depressions or small fills up to a few meters, LiDAR can create a point cloud on the spot. Some apps compute volumes on the phone immediately. However, smartphone LiDAR distance accuracy may fluctuate by several centimeters to several tens of centimeters depending on the environment, so be cautious for precise earthwork calculations. Integrating LiDAR with LRTK can ideally improve accuracy—for example, correcting the scanned point cloud by aligning it to LRTK-measured reference points.

• Small-site use cases: Smartphone measurement is suited to small excavations by one backhoe or short fill areas. Where a foreman might previously measure with a level and staff, quickly creating a 3D model with a smartphone and obtaining volume can complete same-day quantity checks. It’s also an alternative where drones cannot fly, such as narrow spaces. For instance, measure fill behind a building or indoor backfill volumes within walking range by pointing a smartphone around. Combining smartphone and LRTK allows area-based measurements without placing dense survey points, achieving both efficiency and reasonable accuracy.

Thus, smartphones can be nimble surveying tools. For large earthworks, drone plus LRTK remains more efficient, but by choosing appropriately between drones and smartphones and using LRTK for high-precision positioning, you can cover earthwork volume management in any scenario.

Summary: the first step toward high-precision surveying anyone can do

We have explained the use of photogrammetry point clouds and techniques to improve accuracy with LRTK. Key points:

• Photogrammetry point clouds provide detailed data useful for earthwork calculation, but by default they are in a relative coordinate system and contain errors, so georeferencing is essential.

• Point cloud accuracy is determined by image resolution, position information accuracy, and GCP placement; optimizing these factors can achieve centimeter-level accuracy.

• LRTK combines a compact RTK-GNSS device with a smartphone to enable anyone to perform cm-level reference point surveying on site. By obtaining a small number of reference points or high-precision geotags, LRTK dramatically simplifies photogrammetry georeferencing.

• High-precision point clouds enable practical applications such as as-built heatmaps and rapid volume calculation that directly support construction management DX. Real-time situational awareness and sharing improve safety and productivity.

• Combining smartphones with LRTK enables easy small-scale earthwork management, bringing digital measurement to even single-machine operations.

Today, without special large equipment or advanced expertise, site technicians themselves can perform high-precision 3D surveying. For example, all you need to start as-built and volume management with photogrammetry is a commercial small drone and a palm-sized LRTK receiver. Instead of waiting for a survey team, construction managers can measure points and check as-built conditions on the spot. This shortens waiting times and helps address manpower shortages, boosting site-wide efficiency.

Consider introducing new technologies like LRTK to balance accuracy and operational efficiency and take your site work to the next level. Small steps accumulate, accelerating site DX (digital transformation) and leading to safer, more resilient construction management.

Frequently Asked Questions (FAQ)

Q: What level of accuracy can I expect from point clouds created by photogrammetry? A: It depends on conditions, but if you follow proper procedures you can expect accuracy on the order of a few centimeters (a few inches). With high-quality camera images and proper shooting methods, combined with GCPs or RTK positioning corrections, horizontal and vertical errors of less than a few centimeters are not uncommon. Conversely, if image resolution is coarse or georeferencing is neglected, errors of more than ten centimeters may occur. As a guideline, the lower bound of point cloud accuracy is roughly the original image GSD (ground sampling distance)—for example, if GSD = 2 cm/px (0.8 in/px), you cannot realistically expect accuracy better than that. Adjust image quality and coordinate correction methods according to the accuracy required on site.

Q: Are control points (GCPs) always necessary? How many should I set? A: If you can secure high-precision geotags (e.g., from an RTK-equipped drone), in theory you can build a model without GCPs. Realistically, though, it’s safer to set a few GCPs. A common guideline is around five points, placing them at the corners and center for larger sites. If the drone has no RTK or you only use smartphone photos, we recommend at least three points (and preferably five including vertical control). With LRTK you can measure reference points on site quickly before and after capture. If you use an RTK drone plus LRTK, you might only need two or three points for checkpoint validation. In short, a few anchor points to suppress model distortion will improve accuracy—set them as needed.

Q: What is LRTK and how is it different from conventional RTK surveying? A: LRTK is an integrated high-precision GNSS device that is easy to use with a smartphone. Conventional RTK surveying typically required a fixed base station and a rover with radios, but LRTK integrates these functions into a compact device that works with a smartphone. One key feature is that a dedicated local base station is not required; public reference stations or satellite augmentation services are accessed via communication for real-time correction. This enables centimeter-level positioning on site without specialized equipment or deep expertise. In short, think of LRTK as “RTK anyone can use”: it provides far greater portability and ease of use while delivering accuracy comparable to conventional expensive survey gear.

Q: How do I align point cloud data to the site coordinate system? A: There are two main methods. One is to use GCPs within photogrammetry software: input GCPs measured in the site coordinate system (plane rectangular coordinates or local coordinates) into the software, mark the GCPs in the photos, and assign their true coordinates so the model is transformed into that coordinate system. The second is to attach high-precision coordinates during image capture: with an RTK drone or LRTK, if photos are geotagged with cm-level positions, the processed point cloud will automatically align to the chosen coordinate system. In that case, set the coordinate system in the software or app before processing. If a residual offset remains, you can also perform a three-point transformation (or a 7-parameter transform) using additional known points.

Q: Can I calculate volumes without a drone—using only a smartphone? A: Yes. For small areas, photos taken with a smartphone or point clouds from smartphone LiDAR can be used to calculate volumes. For example, for a 5 m (16.4 ft) square pile, taking around 30 photos from various angles with a smartphone and running free point cloud generation software can yield a rough 3D shape. Compute volume differences against a reference surface to obtain earthwork volume. To improve accuracy, combine smartphone data with at least one measured real-world dimension—measurements from a laser distance meter or tape, or a single LRTK-measured reference point to scale/align the model increase reliability substantially. Some commercial simple 3D scan apps can output volumes in real time. For larger areas, walking with a smartphone becomes impractical and a drone is preferable.

Q: What are the steps to calculate earthwork volumes from point cloud data? A: The basic steps are: create a mesh or surface model representing the terrain from the point cloud, then compute the volume difference between that surface and a reference surface (design surface or previous epoch). Concretely, after generating a point cloud in photogrammetry software, import it into earthwork or CAD software that can perform volume calculations. Specify the current point cloud surface and the reference surface (e.g., design TIN or a horizontal zero plane), and the software will integrate height differences within the specified polygon range to compute volume. You can also compare two epochs of point cloud data to calculate cut/fill differences. The output typically reports “cut volume, fill volume, net balance,” which you can record in daily reports or progress management sheets. Ensure the point cloud is in the correct coordinate and elevation system before calculation.

Q: What are the benefits of introducing LRTK to the site, and is it difficult for first-time users? A: The benefits of LRTK fall into three categories: improved accuracy, increased efficiency, and reduced manpower. Accuracy: anyone on site can obtain centimeter-level position information that previously required specialized equipment, improving as-built measurement and stakeout precision and reducing rework. Efficiency: tasks that previously required waiting for a survey crew can be handled immediately by construction staff, reducing downtime—e.g., measure in the morning and prepare reports by the afternoon. Reduced manpower: one-person smartphone surveying means fewer survey personnel are needed to cover a large site, helping mitigate chronic shortages. As for initial hurdles, basic operations are completed through intuitive smartphone apps, so users can perform measurements by following on-screen guidance. Some setup steps like device initialization or subscribing to correction services are required, but manuals and support are generally available. After a few uses, operators quickly gain confidence. Once experienced, many find the convenience and accuracy indispensable. LRTK offers a high return on modest investment, making it worth considering as a first step in site DX.