Instant CAD Conversion of Solar Power Plant Survey Data｜Rapid Layout Design with LRTK Point Clouds

Introduction: Required Survey Accuracy and Design Speed for Solar Power Plant Layouts

In layout design for solar power plants (mega-solar), high accuracy in field surveys and speed of design work are the keys to success. To efficiently place solar panels across a vast site, you must accurately grasp fine terrain undulations and obstacle locations. If survey data contain errors, foundation positions for racking and estimates of earthwork volumes can be off, leading to rework during construction. At the same time, increasing competition in the renewable energy sector makes it necessary to create and revise design plans quickly within limited timeframes. Smooth project progress for solar power plants requires a workflow that can reflect precise field data into CAD drawings quickly.

The Gap Between Field Surveying and CAD Design

In practice, a gap often arises when moving from field surveying to CAD design. Conventional surveying uses total stations or GPS to obtain elevation and position at discrete points; designers then create topographic maps in CAD based on those point clouds or contour lines. However, this process is time-consuming due to extensive manual work and risks overlooking terrain details when interpolating between measurement points. Nuances from the surveyor’s data are often lost when designers interpret the measurements, causing discrepancies with actual conditions. For large, uneven sites like solar farms, point-by-point measurements are insufficient: later on, “we should have measured this spot, too” becomes apparent, requiring inefficient repeat field surveys. In this way, the wall between field survey data and CAD design drawings has hindered project speed and accuracy.

What Is Point Cloud Data? Advantages in Solar Power Surveying

One technology that addresses these challenges is the use of point cloud data. Point cloud data are three-dimensional datasets composed of countless points in space that express the shapes of terrain and structures in detail. For example, when measuring the ground surface with a laser scanner or photogrammetry, millions of points covering terrain and building surfaces can be captured. The result is a precise 3D model that is virtually a digital copy of the entire site. The advantages of using point clouds for solar power surveys include:

• High-density measurement of large areas in a short time, enabling capture of terrain irregularities and slopes across expansive sites. Areas impossible to measure manually can be fully digitized, reducing later “re-measurements.”

• Ability to record terrain and existing structures with high accuracy from millimeter (mm (in)) to several-centimeter (cm (in)) levels, allowing accurate determination of important factors for panel installation such as slope angles and height differences. Reliable information is available for processes requiring precision, such as adjusting racking leg lengths and calculating earthwork volumes.

• Preservation of the actual site as a 3D record, so you can measure arbitrary dimensions or create cross-sections later as needed. Areas overlooked during design can be checked on the point cloud without additional field surveys.

• Acquisition of non-terrain information as well. Point clouds generated from laser scanners or photos include trees, buildings, utility poles, and other surrounding features, enabling layout decisions that reflect tree locations and heights that cast shadows on panels, or verification of clearances from existing structures.

Thus, point cloud data give solar power plant designers a powerful tool that provides the site “as is.” However, traditional point cloud capture and processing required expensive, large equipment and specialized skills, and converting the data into CAD drawings was laborious. Enter the recently notable LRTK-based, easy point cloud measurement solution.

How LRTK Acquires Point Clouds and Its Real-Time Capability

LRTK is an innovative system that combines a compact high-precision GNSS receiver with a LiDAR-equipped smartphone, enabling anyone to easily obtain point clouds with absolute coordinates. Specifically, the LiDAR sensor built into the latest smartphones scans the surroundings while an LRTK device attached to the phone performs centimeter-class positioning. Using real-time kinematic (RTK) technology, the smartphone’s position is corrected to within a few centimeters (a few in) of error, allowing generation of high-precision point cloud data with coordinate information on the spot.

Traditionally, giving accurate coordinates to point clouds obtained by drones or laser scanners required separate georeferencing to known control points. With LRTK, positioning data are integrated during measurement, so no post-process alignment is required. For example, if you walk a solar site while scanning with LRTK, the resulting point cloud is immediately tagged with world geodetic system coordinates. This makes it possible to overlay survey point clouds with design drawings for direct comparison, enabling survey data to be used in CAD design immediately.

LRTK also supports Japan’s satellite positioning augmentation service “Michibiki”’s centimeter-class augmentation service (CLAS), and is designed to provide stable high-precision positioning even in mountainous areas with poor cellular coverage. Solar power plants are often located on former golf courses or forested land with poor communications, but LRTK can achieve centimeter-level positioning (cm (in)) even without an internet connection. This anywhere-measurement, real-time capability allows point clouds to be captured quickly at the desired time on large sites and prevents design work from being stalled while waiting for surveys. The device is battery-and-antenna integrated, palm-sized for excellent portability, and can be mounted on a tripod for fixed-point observation. Combined with operation that requires no training, field surveying is undergoing a major transformation.

The Specific Workflow for Converting Point Clouds into CAD (Drafting and Vectorization)

How are acquired point cloud data reflected in CAD drawings? Here is the specific workflow.

• Point cloud acquisition and upload: Point clouds scanned with LRTK can be uploaded to the cloud from the smartphone with a single click at the site. The data can be shared immediately with in-house designers and project stakeholders, so analysis can begin by the time you return to the office.

• Creation of terrain model: Using cloud-based or PC software, generate a 3D terrain model (DTM: Digital Terrain Model) from the point cloud. Filter out unwanted points (temporary objects such as machinery and people, and noise), extract only the ground surface, and create mesh models and contour lines. LRTK cloud features can automatically generate elevation data and contour maps from point clouds.

• Drafting and vectorization: Import the resulting terrain model into CAD software and draw the lines and shapes needed for design. For example, export contour lines in DXF format for display in CAD, or extract longitudinal and cross sections from the point cloud to draw terrain-section lines. It is also easy to trace important shapes like building edges or road edges on the point cloud and convert them to polylines to obtain planimetric shapes of existing structures. “Drafting” refers to creating such drawing elements from point clouds, and since LRTK tools support functions like outputting point cloud cross-sections to drawings, existing-condition drawings can be produced efficiently.

• Layout design in CAD: With the prepared terrain map, proceed to full-scale design of panel layout and earthworks plans. As needed, use the point cloud itself as a background reference in 3D view to confirm rack placements and earthwork areas. Because point clouds are real 3D measured data, they can be used to validate designs drawn in CAD. In this way, CAD drawings that reflect the actual site are completed and used for subsequent design reviews.

This workflow smooths the transition from surveying to design. In particular, by quickly performing point cloud acquisition and terrain model creation, it becomes possible to reflect survey results into design within the same day, dramatically shortening processes that previously took days to weeks.

Applications for Earthwork Design and Racking Layout Design

High-precision point clouds and terrain maps directly support earthwork design and panel racking layout for solar power plants. Here are specific applications.

In earthwork design (site grading and earthwork planning), use the existing terrain model generated from point clouds to optimize cut-and-fill plans. On the design screen, you can display a heat map of the difference between planned grading elevations and the current ground surface in the LRTK cloud. Comparing the existing point cloud and the design model makes it immediately clear where and how much to excavate or fill by color-coding highs and lows. There is also functionality to automatically aggregate cut and fill volumes at each point and calculate total earthwork volume. This allows you to accurately determine the amount of soil required for grading and plan without surplus or shortage. Earthwork quantity calculations that used to require drawing many sections and hand calculations can now yield accurate numbers instantly using point cloud data, greatly improving efficiency and cost-control accuracy.

For racking layout design, you can consider panel layouts that match the terrain. The detailed terrain from 3D point clouds enables precise calculation of leg positions, heights, and slope angles for each rack, allowing designs to be tailored to the installation conditions. For example, on steep slopes you may specify longer racking legs, while on gentle slopes you may minimize earthworks and adjust by racking height; such decisions are possible using slope information obtained from point clouds. Using terrain and surrounding point cloud data to run shadow simulations lets you understand seasonal and time-of-day shading to set appropriate panel spacing and row gaps. If tree point clouds are included, you can decide which trees to fell or retain and even run future power-generation simulations, contributing to layout optimization and reduced generation losses. Point cloud–based 3D design enables “terrain- and environment-adapted panel placement,” allowing construction on non-flat, challenging sites without undue difficulty while maximizing generation efficiency.

Incorporating Existing Structures and Surrounding Environment and Their Use

The benefits of point cloud data extend beyond terrain. Existing structures and the surrounding environment can be comprehensively digitized, and using them in design improves plan completeness. For example, utility poles and transmission lines, substation equipment, buildings, and retaining walls inside and near the site can be captured during the initial point cloud survey so you don’t need to measure or draw them later; their spatial relationships are accurately preserved. In solar layout design, panels must be placed with appropriate clearances from existing structures and shading impacts must be evaluated. With point clouds, you can check distances between panels and utility poles in CAD, examine elevation differences with substation equipment to plan cable routing, and verify other details during design.

Also, including the surrounding terrain and structures in the dataset helps with adjustments and harmony with areas outside the site. For example, you can confirm boundary lines with adjacent properties, assess whether adjacent buildings will be affected by solar shading, or run visual simulations from surrounding roads—factors that help the project proceed smoothly. These tasks used to require additional surveys or on-site verification after design, but if you capture them comprehensively in the initial point cloud scan, you reduce the need for follow-up surveys. In short, by incorporating the site “as is” into the design via point clouds, you can raise design accuracy and eliminate unforeseen issues on site.

Effectiveness of AR and Cloud Integration to Prevent Design–Reality Gaps

A unique feature of the LRTK point cloud solution is that integration with AR (augmented reality) and cloud services allows visualization and sharing of discrepancies between design drawings and actual conditions. Using the data consistently from design through construction minimizes the gap between design and field reality.

First, with AR you can overlay the absolute-coordinate 3D model and design data obtained by LRTK onto the site via a smartphone or tablet. For example, displaying the planned solar panel layout in AR on the actual landscape lets you confirm the completed image on-site, which paper drawings alone cannot convey. This helps owners and contractors share an on-site image and prevents mismatches such as unnatural panel placement relative to slopes or impractical equipment layouts. Thanks to LRTK’s high-precision positioning, AR remains correctly positioned no matter how much you walk around, enabling verification of how well the design model matches the terrain at various points across a wide site.

During construction, you can also check whether the built work matches the design by comparing as-built point clouds with the design data. The LRTK cloud’s as-built heat map function overlays a re-scanned point cloud after construction with the design 3D model and color-codes areas with deviations. For example, racking heights that are too high or low are highlighted in red for immediate detection. This facilitates error correction and quality control during construction and reduces the risk of discovering problems after completion. By always sharing point cloud data and design drawings in the cloud, stakeholders can grasp design changes and field corrections in real time. Because the site supervisor and designer can discuss using the same data, communication loss–related rework is reduced. In these ways, workflows using AR and cloud services detect and correct discrepancies between design and reality in advance, strongly supporting the goal of “building what was designed.”

Benefits of Implementation: Speed, Accuracy, and Reduction of Rework

Finally, summarize the benefits of implementing the LRTK point cloud solution from the perspectives of speed, accuracy, and reduction of rework.

• Speed improvement: Processes that used to take weeks in sequence—surveying → drafting → design—can, with LRTK, sometimes have point cloud acquisition and drawing reflection completed on the same day. Even for large solar plants, combining drone surveys and LRTK enables rapid acquisition of 3D data for the whole site and immediate commencement of design review. Because design changes can be handled quickly with supplemental field scans and fast data sharing, overall project schedules are shortened.

• Accuracy improvement: Centimeter-level positioning via RTK and high-density point clouds dramatically raise the accuracy of site information that forms the basis of design. Misreads of terrain caused by contour thinning and human errors in conventional surveying are greatly reduced, allowing design to be performed in a state where “there is almost no difference between the site and the drawings.” Construction accuracy also improves, for example, by enabling accurate stake-out without a surveyor using stake-guidance functions. High-accuracy surveying and construction are important benefits that contribute to panel generation efficiency and structural safety.

• Reduction of rework: Digitally copying the site in the early stages greatly reduces unforeseen issues during design and construction. It becomes less likely to discover buried utilities after design or to find that racks don’t fit due to terrain irregularities during construction. With point clouds and AR, design and actual conditions can be checked continuously and problems can be discovered and corrected on the spot, preventing rework after construction. In construction management, progress can be checked sequentially with data, reducing corrective rework due to as-built defects.

These benefits greatly improve overall project productivity and quality. Rapid and accurate surveying and design directly lead to shorter construction periods and cost savings, contributing to earlier commissioning and improved profitability of power plants.

Conclusion: DX of Surveying and Design with LRTK and the First Step to Adoption

In solar power plant design and construction, point cloud surveying using LRTK represents the first step in surveying-and-design DX (digital transformation). A workflow that digitizes all onsite information and immediately applies it to design unifies the formerly fragmented processes of surveying and design. Together with government-led i-Construction initiatives by the Ministry of Land, Infrastructure, Transport and Tourism and industry-wide DX promotion, the use of 3D point clouds and AR will increasingly become standard.

LRTK’s defining advantage is its ease of use: “all you need is a smartphone.” Even without specialized equipment or techniques, your in-house designers and field staff can obtain high-precision point clouds and reflect them in design. Start with small sites or pilot cases to experience the benefits firsthand. Fortunately, LRTK is simple to use and easy to trial, and cloud integration makes data sharing inside and outside the company smooth.

Organizations accustomed to traditional methods may initially resist, but many cases demonstrate that solving field issues digitally one by one leads to dramatic improvements in surveying and design productivity and quality. If you are involved in solar power plant layout design, consider adopting an LRTK point cloud solution and its new workflow. As the first step in DX for surveying and design, incorporate the latest technologies on site to achieve faster, more accurate, and less wasteful project execution.