Procedure for 3D Slope Survey: Thorough Guide to Smartphone Point Cloud Measurement with LRTK

In recent years, as construction sites become more digitalized, 3D technology has begun to be applied to surveying civil engineering structures such as slopes. A slope is an artificial incline formed by cut or fill, and its safety management, as-built verification, earthwork volume measurement, and deformation monitoring are critically important for construction management. However, conventional slope surveying required workers to enter hazardous slopes and use tape measures or total stations, which was time-consuming, labor-intensive, and posed safety issues. At the forefront is a 3D point cloud surveying approach combining smartphones and LRTK (high-precision GNSS positioning devices). With easy mobile scanning using smartphones, an era is approaching in which anyone can create high-precision 3D data of slopes. This article provides a thorough explanation, from the importance of slope surveying and the benefits of 3D conversion to the concrete procedures for point cloud measurement using a smartphone + LRTK, how to utilize acquired data, precautions, and future prospects.

Importance of slope surveying and background of 3D conversion

Slopes are found everywhere in civil sites—roads, reclaimed land, dam embankments—so shape control and safety checks are important tasks for engineers such as civil construction supervisors. Traditionally, slope height and gradient were partially measured with tape measures or leveling rods, or several points were measured with a total station to create cross-sections. However, this method made it difficult to grasp the entire slope shape in detail, sometimes leading to oversights. Also, surveying by people entering steep slopes carries the risk of falls or collapse.

Against this background, methods that digitize the entire slope as 3D data have attracted attention. 3D surveying with point cloud data records countless surface points of the slope as X, Y, Z coordinates, so local irregularities and anomalies can be recorded without omission. With the Ministry of Land, Infrastructure, Transport and Tourism-led i-Construction initiative, even small and medium contractors are increasingly adopting digital techniques for as-built control and deformation monitoring. In particular, the increasing performance of smartphones and the proliferation of affordable GNSS devices have made it possible for field technicians themselves to perform 3D surveying easily. 3D conversion of slope surveying offers significant benefits in both safety and quality control, making it a field whose spread is eagerly anticipated.

Advantages of smartphone × point cloud surveying (compared to conventional methods)

Mobile scan surveying using smartphones and point cloud technology offers many advantages not available in conventional methods. Below, we outline the superiority of smartphone surveying compared with traditional methods such as total stations, tape measures, and levels.

• Improved safety: Previously, slope surveying required carrying heavy equipment to the slope and having an assistant stand on hazardous slopes with a prism or tape measure. With smartphone 3D scanning, one worker can take measurements from a safe distance, reducing the risk of falls or collapse since people do not need to enter unstable high areas. It also reduces the need to survey close to heavy machinery or vehicles, helping prevent third-party accidents.

• Improved efficiency: Mobile scanning can acquire large-area data in a short time. There is no need to spend time setting up equipment or reading individual points; simply walking with a smartphone can capture millions of points at once. Steps that used to require returning to the office for calculation and drawing can be completed on-site with automatic processing and cloud sharing via smartphone surveying, enabling same-day results and speeding up work. Consequently, the labor and time required for surveying can be drastically reduced.

• Balance of accuracy and coverage: Traditional total station (TS) surveys offered high point accuracy but lacked information for unmeasured areas. In contrast, smartphone point cloud surveying, when combined with RTK, can achieve centimeter-level position accuracy (half-inch level) while recording the entire slope surface in a planar manner, providing excellent coverage. Once point cloud data are acquired, dimensions and gradients at arbitrary locations can be measured in the data later, preventing additional surveys due to omissions. In short, it allows faithful digital recording of the site while maintaining sufficient accuracy.

• Low cost and labor reduction: Dedicated 3D laser scanners and drone survey systems were very expensive in terms of equipment and software, but the smartphone + LRTK approach drastically reduces initial investment. Existing smartphones can be used, and only a small additional device needs to be attached, making it affordable for small and medium enterprises. There is also no need to hire specialized operators; on-site personnel can perform surveying duties themselves, improving labor cost efficiency. Equipping multiple staff with units so the whole team can survey simultaneously can boost overall site productivity.

As described above, smartphone-based point cloud surveying brings revolutionary benefits in safety, efficiency, accuracy, and cost. It is becoming the optimal on-site solution to meet the long-standing requirement of being “fast, cheap, accurate, and safe.”

What is LRTK? Coordinate correction technology linked with smartphones

LRTK is the name of an ultra-compact RTK-GNSS receiver used by attaching it to a smartphone. RTK (Real Time Kinematic) is a technology that applies correction information from a base station to satellite positioning (such as GPS) in real time, greatly reducing positioning errors. RTK used to be available only on expensive surveying instruments, but by linking an LRTK device with a smartphone, anyone can easily achieve centimeter-level positioning (half-inch level).

When an LRTK is attached to the top of a smartphone and connected via Bluetooth or cable, the smartphone quickly becomes a surveying instrument with a high-precision GNSS antenna. By receiving base station data via Ntrip distribution over the internet or CLAS signals from Japan’s quasi-zenith satellite “Michibiki,” positioning is corrected in real time. If the correction information is properly received and the device reaches a Fix state, the smartphone’s current position can be enhanced to an accuracy of about 2-3 cm (0.8-1.2 in).

The greatest feature of this smartphone + RTK combination is that each point in the point cloud acquired by the smartphone’s built-in camera or LiDAR sensor can be immediately assigned accurate absolute coordinates. Normally, scans from a smartphone alone are recorded in a local coordinate system (relative coordinates), so it is unclear where they lie on a map, and slight shakes during scanning can cause overall distortion. LRTK is a coordinate correction technology that solves these problems. Because the smartphone’s position is tracked at high precision by RTK even while moving, distortions and positional shifts in the point cloud are corrected in real time when scanning over a wide area. As a result, the acquired point cloud is tied to real-world coordinates such as public coordinate systems, producing 3D data that can be used on the spot without complicated conversions afterward.

Another strength of LRTK is its smartphone compatibility and simplicity. A small receiver weighing only about 100-odd grams can be attached, the app launched, and then by simply pointing the camera and walking, advanced positioning runs intuitively without the operator being consciously aware of it. The display shows the current positioning mode and accuracy; once in Fix state, you can confidently start scanning. No difficult settings are required; the app handles positioning and data recording automatically, so even field staff without specialized knowledge can use it. In this way, LRTK is a powerful technology that brings smartphone surveying to practical accuracy levels.

Preparation: required equipment and applications

Now, let’s explain the preparations needed to start slope 3D surveying with a smartphone and LRTK. Fortunately, the required equipment is not many; you can start quickly by preparing the following items.

• Smartphone: Most recent high-performance smartphones are generally usable. Models equipped with LiDAR sensors are especially recommended; examples include certain high-end phones that have rear-mounted LiDAR. Even without LiDAR, photogrammetry apps can generate point clouds, but LiDAR-equipped devices excel in real-time capability and ease of use. Make sure the smartphone is fully charged in advance to prepare for long survey sessions.

• RTK-GNSS receiver: A compact high-precision GNSS receiver for use with smartphones. LRTK and other RTK-compatible receivers that can be attached to smartphones are available from multiple manufacturers. Connection methods vary (Bluetooth, Lightning cable, etc.), but all are pocket-sized devices that achieve centimeter-class positioning. Attaching one to your smartphone reduces position errors from several meters (several ft) with ordinary GPS to a few centimeters (half-inch level), providing high-precision position information to the point cloud data. Compared to dedicated large instruments, they are affordable, making it easier to obtain multiple units for field personnel.

• 3D scanning app: A point cloud measurement app installed on the smartphone. These are available from app stores for each OS, with free or reasonably priced options. LiDAR-compatible apps utilize the camera and sensor to acquire point clouds in real time and save/share the data on the spot. For first-time users, choose and install a well-reviewed app. Some apps include settings to link with RTK receivers. Follow the device manual and, if necessary, set up correction information reception in the app (such as inputting an Ntrip server) and select the coordinate system.

• Other useful items: A mobile battery (external power) and a smartphone stabilizer (gimbal) are helpful. 3D scanning consumes the smartphone battery quickly, so a spare power source is essential for long sessions. A gimbal stabilizes the phone and reduces shake; it’s not mandatory but helps smooth scanning. Also, if the site has unstable connectivity, confirm an internet connection for positioning corrections (pocket Wi-Fi or tethering). It’s also a good idea to simulate the app usage and survey flow beforehand to avoid confusion on the day.

Once the equipment and app are ready, connect the smartphone and RTK receiver and perform initial survey settings. Specifically, confirm Bluetooth pairing or cable connection and check on the app whether RTK correction information is being received (satellite lock count and Fix status display). If GNSS reception is good and Fix is established, you can begin 3D scanning of the slope.

Shooting procedure: safe positioning and path considerations

When conducting slope point cloud measurement, it is essential to move the smartphone in a planned and safe manner. Here we describe the field shooting (scanning) procedure, including slope-specific precautions.

• Confirm surveying positions: First, inspect the target slope to see what is visible from which positions. Choose a position with a stable footing from which you can see as much of the slope as possible. For steep slopes, a basic approach is to scan from the toe toward the mid and upper slopes. If the upper area is hard to see due to steepness, step back a little and tilt the smartphone upward to capture the top. Never cling to a slope in an unsafe posture; if you cannot cover a section from a distance, move around to the top and scan downward—prioritize safety when selecting positions. If needed, station a lookout to watch for slipping or third-party approach.

• Start scanning and posture: Start a new scan in the measurement app; when the camera image and point cloud preview appear, begin walking slowly. Hold the smartphone firmly with both hands and minimize shake. When photographing a slope, aim the smartphone as perpendicular to the slope surface as possible to obtain better point clouds—imagine pointing the sensor nearly at a right angle to the slope. At oblique angles the laser may not return correctly and points may be sparse, so aim for as close to perpendicular as feasible. Even when stepping back to capture more of the terrain, keep the slope in the camera frame to avoid exposing the sky or distant background through hand shake.

• Movement path and coverage: When moving while scanning, walk smoothly at roughly half the normal pace or slower. Avoid sudden turns or stops; a steady pace is key. For wide slopes, plan the scan path so nothing is left unscanned. For example, for a laterally long slope, scan the lower area by moving horizontally along the toe, then, if there is a safe route onto the mid-slope, scan the middle horizontally, and finally scan near the top—this kind of divided measurement is effective. For very large areas, divide the area and scan over multiple runs rather than trying to capture everything at once; this yields higher-quality data.

• Real-time checking and filling blind spots: The app displays the acquired point cloud in real time. Use this to continuously check for unscanned areas (blind spots) and fill them in. Upper slope areas and depressions are easy to miss when scanning from below, so if the screen shows sparse points in a section, point the camera there and rescan. Moving objects such as people or machines in the frame add noise, so keep the environment as still as possible and maintain a constant motion yourself. If you spot an omission later, return immediately to supplement it. Avoid passing the same spot repeatedly after long intervals, as the app may lose tracking and duplicate points (ghosting); thus, don’t rescan the same area unnecessarily.

• Finish scan and save: After scanning the entire slope, press the app’s “Complete” button to end the scan. Post-processing to generate the point cloud is automatically executed on the smartphone. After a few seconds to tens of seconds, a 3D point cloud model will be displayed, allowing rotation and zoom for detailed inspection. Some apps include automatic noise removal or clipping functions—edit out obvious erroneous points if needed. If the point cloud is satisfactory, save the data on the device and export point cloud files as required (common formats such as `.ply` or `.las`). Since point clouds acquired with LRTK already have absolute coordinates, you can perform as-built measurements and gradient calculations without coordinate alignment. For example, measure distances between two arbitrary points or the area of a polygon on the smartphone to get a rough slope gradient or surface area on the spot.

• Data sharing and cloud linkage: If necessary, upload the saved point cloud data to cloud storage. If within network coverage, you can immediately share data with your company cloud from the field and have the office PC access or download it. If using a dedicated platform, uploading may generate a browser-based point cloud viewer for interactive 3D review and meetings with stakeholders. If measurements were taken in remote mountain areas without reception, upload later when back in coverage. Cloud services can also run analyses (volume calculations, cross-section generation, etc.), and those outputs can be used in drawings and reports. Being able to share and analyze the latest 3D information from the field instantly speeds decision-making and improves site management efficiency.

These are the basic shooting and scanning procedures. The key is to “move the smartphone safely and deliberately, checking the status in real time while uniformly capturing point clouds.” With practice, you’ll be able to digitize large slopes in short time.

Point cloud generation: flow and cautions for SfM / LiDAR

Smartphone point cloud generation broadly falls into two approaches: LiDAR scanning and SfM (Structure from Motion) photogrammetry. Both yield 3D point cloud data, but their workflows and points to note differ, so understand the outlines of each.

• LiDAR scan method (real-time point cloud acquisition): When using a LiDAR-equipped smartphone, you can generate point clouds in real time via a dedicated app as described above. Moving the smartphone performs instant distance measurements and acquires millions of points, allowing immediate 3D model confirmation on-site. A drawback of LiDAR is that the sensor’s effective range is limited. Typical smartphone LiDAR effective range is about 5 m (16.4 ft), so it is not suitable for distant or high-elevation measurements. Also, optically, mirrors, glass, and water surfaces can reflect or transmit laser pulses, making them unmeasurable. Black objects tend to absorb light and cause missing points. In slope surveying, large mirrors are rare, but wet rock or dark protective nets can cause sparse point clouds. While not entirely preventable, changing angles, scanning multiple times, or creating shade can partly mitigate this. LiDAR is also affected by ambient light; very strong direct sunlight increases noise and can degrade accuracy (infrared interferes with the sensor). In such cases, shading the smartphone or measuring in morning/evening or cloudy conditions is desirable. Conversely, in very dark environments color information cannot be captured; to get color point clouds at night, lighting is necessary. LiDAR scanning is convenient but has measurement-environment constraints to keep in mind.

• SfM photogrammetry method (point cloud generation from images): If the smartphone lacks LiDAR or you need to cover a wider area, photogrammetry from smartphone photos (SfM) is an option. SfM reconstructs 3D shapes from the motion of feature points across multiple images. The workflow involves taking many overlapping photos of the slope from various angles (or extracting frames from video) and processing them later with software or cloud services to produce a point cloud. The advantages of SfM are that more distant targets can be captured and high-resolution textured models are easier to obtain. For example, taking dozens of photos of the whole slope can later generate dense point clouds and orthophotos. SfM from ground-level photos is effective where drones cannot be used. However, SfM has several cautions and requires more effort. You cannot get immediate results on-site; data processing can take from tens of minutes to several hours depending on data volume. During photography ensure sufficient overlap (common area across images) and avoid blur or poor exposure. Moving subjects and flickering light cause processing failures, so keep people and machines still and aim for uniform illumination. The generated point cloud is in the camera coordinate system, so to use it as survey results you must adjust scale and orientation (georeference). Here too, LRTK is helpful: record known point coordinates on-site with LRTK and use those to align the model. For example, place easily identifiable markers at the toe or crest of the slope and record their RTK coordinates. After SfM processing, align the model to these markers to assign correct coordinates to the whole model. Recently, solutions have emerged that record position and orientation when shooting and embed RTK coordinate tags in the photos themselves. Using LRTK in combination makes integrating SfM point clouds into real-world coordinates easier and increases practicality.

Overall, smartphone point cloud generation has two main methods, LiDAR and SfM, each with trade-offs. LiDAR is quick and easy but limited by range and environment, while SfM takes time but can cover wider areas and yield high-detail records. Choose the appropriate method for site conditions and objectives, and consider combining both when useful. In any case, leveraging LRTK’s high-precision coordinates is key to making slope point cloud data truly usable.

Coordinate correction: integrating LRTK data to make slope point clouds practical

As noted, point cloud data obtained with a smartphone alone are typically recorded in an arbitrary local coordinate system. This makes it inconvenient to directly compare slope angles or heights with design drawings or other survey results. Therefore, to utilize 3D point clouds in civil engineering, coordinate correction to match survey control points is essential. Traditionally, this georeferencing involved manually matching features on the point cloud to known coordinates or running alignment algorithms in post-processing. However, by combining with LRTK you can perform high-precision coordinate assignment simultaneously with point cloud acquisition, producing immediately usable 3D survey data without complicated post-processing.

The impact of LRTK coordinate integration on slope management is enormous. For example, when verifying as-built slope shape, point clouds with public coordinates can be overlaid directly on design data (design cross-section CAD or 3D models) to check deviations. For earthwork volume calculations, you cannot compare without a common coordinate system, but if both pre-existing ground survey data and acquired point clouds share the same coordinate basis, you can calculate cut-and-fill volumes directly from the differences. For long-term deformation monitoring, comparisons across time require consistent coordinates; point clouds acquired with LRTK are always positioned on the same geodetic system, allowing precise detection of temporal changes. Thus, point cloud data assigned absolute coordinates are far easier and more reliable to work with for analyses and integration with other data.

Another benefit of LRTK integration is that it reduces the need for additional on-site surveys. If parts are inevitably missed by smartphone scanning and later supplemented with total station measurements, aligning them was previously cumbersome; but when the smartphone point cloud is already tied to control coordinates via LRTK, supplementary survey data can be overlaid easily. Likewise, orthophotos from drones or other measurement results can be integrated on GIS if coordinate systems match. LRTK serves as a “bridge between point clouds and existing controls,” turning simple field records into valuable information for practical work.

In summary, giving smartphone-acquired point clouds high-precision coordinates with LRTK elevates slope survey data to practical accuracy and usability. Georeferenced point clouds can be used immediately for as-built checks, quantity calculations, and monitoring without worrying about coordinate adjustments. To fully leverage acquired 3D data, incorporate LRTK-based coordinate correction into your workflow.

Using point clouds for slope management (as-built, volumes, deformation detection, maintenance)

Next, let’s look at concrete ways to use the 3D point cloud data obtained in slope management. Point cloud data for slopes and fills are powerful across a wide range of applications from safety management to as-built inspection and maintenance.

• Use in as-built management: To confirm that post-construction slope shapes match the design, you need to know the slope’s overall gradient and local irregularities. Point cloud data, measured densely over the entire slope, allow accurate verification of local bumps, shoulders, and lower edge finishes that planar drawings or limited measured points might miss. For example, extract longitudinal and cross sections from the point cloud and overlay them with design sections to immediately see whether finishing is acceptable. Major civil CAD software supports point cloud import and can color-code deviations when overlaid with design data. Comparing the as-built point cloud with the design model improves the efficiency of as-built inspections and strengthens evidence. Attaching generated cross sections and plan views to inspection documents provides objective backing when explaining to supervisors and inspectors.

• Quantity and volume calculation: Managing excavation and fill volumes is important in slope construction. 3D point clouds make such calculations far easier and more accurate. By comparing pre- and post-construction terrain point clouds, excavated or filled volumes can be calculated automatically. Previously, volume estimates from a few cross-sections had limitations, but volume calculation from point clouds reflects the entire slope shape and yields higher reliability. Some dedicated software and cloud services calculate volume differences between two point clouds with one click, enabling quick verification of progress quantities—helpful to determine “how much more fill is needed” or “how much soil has been removed beyond plan.” If you can internally track daily fill or grading quantities, project schedule and payment management accuracy improves.

• Deformation detection (monitoring): Point clouds are useful for early detection of age-related changes or deformation such as cracks and settlement. By performing 3D scans at regular intervals and comparing datasets, even minor shape changes can be quantified. For example, measuring the slope before and after heavy rain allows accurate evaluation of displacement due to landslides or erosion. Centimeter-scale bulges or misalignments that are difficult to detect visually can be visualized as differences from prior data, enabling timely countermeasure planning. On-site, subjective judgments like “the crack seems to have widened” can be replaced with objective, quantifiable evidence. Since the coordinates of detected deformations are clear on the point cloud, the data can be used directly to specify repair extents and design mitigation works.

• Maintenance and record use: Detailed data collected as point clouds become digital records useful over time. If you store the slope’s shape right after construction, future inspections can compare the current state with the original to assess degradation. Record the positions of embedded anchors or bolts during construction by scanning, enabling accurate location retrieval during later repairs. Some LRTK-capable apps let you attach photos and notes to arbitrary points on the point cloud; this allows you to record problematic areas (cracks, seepage) with high-resolution photos. Information that is hard to convey in text reports can be shared intuitively when combined with colored point cloud models or AR overlays. For example, you can display the acquired slope point cloud model on a smartphone or tablet with AR, overlaying past data or design models on the actual scene, and discuss “this area has deformed by this much compared to previously.” Accumulating and using site information in the form of point clouds + photos + AR is a new style of safety and maintenance management that will become increasingly widespread.

Thus, point cloud usage in slope management covers many aspects: streamlining as-built inspections, improving quantity calculations, enhancing deformation detection, and organizing records. Point clouds positioned with LRTK are particularly easy to combine with other survey results and design information, making them a powerful tool for on-site DX (digital transformation).

Troubleshooting examples and countermeasures (light interference, fall prevention, vegetation effects, etc.)

Smartphone + LRTK slope 3D surveying is convenient and precise, but there are points to be careful about and common problems that can occur. Here we introduce frequent issues and countermeasures.

• Unstable GNSS positioning: RTK positioning is affected by the surrounding environment. Near tall buildings, inside forests, or in mountainous areas satellite visibility can be blocked and RTK may become unstable, degrading accuracy to Float solutions. To mitigate this, choose open locations when possible, perform measurements when many satellites are visible, and use devices that can leverage Japan’s augmentation signals (Michibiki CLAS). If the positioning status drops from Fix, stop and adjust the antenna orientation or wait for Fix to reestablish before continuing scanning. Although it may be tempting to hurry, continuing on a Float solution will compromise overall point cloud accuracy, so be patient and wait for Fix recovery.

• Missing measurements or point cloud gaps: LiDAR struggles with certain materials—typical examples are mirrors, glass, and water surfaces that strongly reflect or transmit laser pulses; these return few or no points. Very dark objects also absorb light and produce sparse points (dark protective mats or rock faces). If such surfaces exist on the slope, accept that they may not be captured and plan to measure them separately or record supplemental photos. For critical locations, try illuminating by light during the day or scanning from a different angle; sometimes changing angle or scanning multiple times yields points. Vegetation on the slope also affects acquisition. Windy vegetation introduces noise, and even still vegetation masks the ground, making it harder to extract the surface. Scanning over tall grass records the grass height rather than the ground, making later ground extraction difficult. Where possible, mow vegetation beforehand, schedule surveys when foliage is minimal, or use point cloud filtering to remove vegetation points.

• Errors from environmental motion and vibration: Measuring in strong winds can cause trees and grasses to sway, producing blurring in point clouds. Nearby heavy machinery can cause ground vibration affecting the sensor. Aim to scan when the surroundings are as still as possible. If flags or nets are flapping, wait for a lull to measure or record the data knowing those moving parts will be removed later. The operator’s stability is also important in handheld mobile scanning. Since slopes often have unstable footing, wear non-slip shoes and use safety belts or ropes as needed; prioritize safety more than in standard surveys. If there is any risk of falling or slipping, do not proceed until safety is ensured.

• Effects of direct sunlight and darkness: While LiDAR works indoors in the dark, it is sensitive to intense sunlight. Scanning under strong summer sun can produce infrared noise, degrading accuracy and causing missing points. Prefer cloudy days or low-sun times, and if only hot sunshine is possible, use a sunshade for the smartphone. Conversely, in extreme darkness color information cannot be captured and visual odometry for camera-based positioning may fail. To obtain color point clouds at night, illuminate the area with floodlights or capture depth-only LiDAR data and later colorize by photo overlay (requires supported apps). Plan shooting times according to sensor characteristics.

• Smartphone overheating and battery drain: 3D scanning heavily loads the smartphone CPU/GPU and causes the device to heat up. On hot days in direct sun, thermal throttling may reduce processing speed or the app may forcibly close, risking loss of the scan. To avoid this, take frequent breaks to cool the device, remove the phone case for better heat dissipation, or use a small fan. Monitor battery level—LiDAR and screen usage deplete battery fast, so carry a mobile battery for frequent charging. Always start with a full charge and ensure enough margin so the device won’t shut down mid-scan. If the battery dies or the app crashes, you may have to restart the measurement from the beginning.

• Measurement range and method limitations: Finally, recognize the physical limits of smartphone point cloud surveying. Smartphones can measure areas that people can walk to, and LiDAR range is around a few meters (a few ft). You cannot capture mid-slope faces of a cliff with no access or the far side of a slope that requires top-down views. In such cases, do not force it; combine drone photogrammetry or terrestrial laser scanning to cover those areas. Smartphone surveying is not万能, but it works extremely efficiently for “areas people can easily approach.” Assign tasks appropriately: let the smartphone cover what it can, and use other technologies for harder-to-reach sections. This approach avoids major failures caused by “inaccessible” or “invisible” parts.

By understanding these countermeasures, you can greatly reduce failures such as “important areas were missing in the point cloud” or “positioning became unstable and data became unusable.” Identifying risk factors in advance and preparing a site-specific plan will allow smartphone + LRTK surveying to proceed smoothly and reliably.

Future prospects and recommendation for LRTK adoption

The 3D surveying method combining smartphones and LRTK is becoming a new standard that supports DX of construction sites beyond slope management. Until now, 3D surveying was the realm of specialists, but this approach dramatically lowers that barrier. A time is coming when site supervisors and engineers can casually perform high-precision surveys using their own smartphones. The most important thing is to try it on-site rather than worry about complex theory. Start with familiar tasks like small-scale as-built slope checks or fill volume calculations to quickly experience its convenience and usefulness.

Currently, multiple products enabling smartphone surveying are on the market, including the LRTK series. LRTK is gaining attention for its simplicity—just attach it to a smartphone to perform positioning and point cloud scanning. Deployment is straightforward: purchase the device, install the dedicated app, and set up an environment to receive RTK corrections, and you can use it on-site immediately. Initial setup can be completed in minutes following guided steps, and the intuitive design allows anyone to learn without special training. In terms of cost, it is far more accessible than large 3D scanners (hundreds of thousands of yen) or drone systems, and leveraging existing smartphones further reduces cost. It has the potential to become standard equipment on site as a “pocket surveying device for each person.”

Adopting smartphone + LRTK for slope 3D surveying dramatically improves measurement and management efficiency while enhancing safety and quality. This technology aligns with the Ministry of Land, Infrastructure, Transport and Tourism’s i-Construction and construction DX initiatives and is practical for projects from large to small. Try it in the field and experience the benefits. With cutting-edge LRTK technology, your smartphone transforms into a high-precision survey instrument, making slope surveying that was once impractical far more accessible. A future in which everyone at the site can use 3D point cloud data is within reach. Now is the time to introduce LRTK and take the next step in slope management. It will be a major investment that improves on-site safety and productivity and contributes to the development of Japan’s construction industry as a whole.