Visualize Point Cloud Data on Maps with KML Output: Easy 3D Sharing with LRTK

In recent years, there has been growing demand to display and share point cloud data acquired by drone surveys and 3D laser scanners on Google Maps. Point cloud data are three-dimensional survey data made up of countless points, a digital "3D copy" that can precisely reproduce a site’s terrain and structures. If such point cloud data can be visualized on a map, the site situation can be intuitively understood and it becomes very convenient when sharing in 3D with stakeholders. However, standard Google Maps does not support direct display of point cloud data. What you can use instead is data output in KML format. KML (Keyhole Markup Language) is a file format for displaying geospatial information in Google Earth/Maps, and through it you can overlay point cloud data on a map for 3D display.

This article explains the benefits of visualizing point cloud data on maps, an overview of the KML format, and how to convert point cloud data into KML files and display them on Google Maps (Google Earth). It also introduces how the dedicated device LRTK makes it easy to output and share KML of point cloud data acquired on site. We will touch on the points that make high-accuracy 3D data sharing accessible to anyone through simple surveying with LRTK.

Table of Contents

• What is point cloud data?

• Benefits of displaying point cloud data on Google Maps

• What is KML used for 3D data visualization

• How to convert point cloud data to KML

• Easy sharing and visualization of point cloud data with LRTK

• Summary: Start simple surveying with LRTK

• FAQ

What is point cloud data?

First, let’s cover the basics of point cloud data. Point cloud data are 3D data composed of many measured points, each with XYZ coordinates (and sometimes RGB color information, etc.). When you scan terrain or buildings with a laser scanner or generate a 3D model from drone photogrammetry, you obtain point cloud data made up of a large number of points. Recently, technologies that allow easy acquisition of surrounding point clouds using smartphones equipped with LiDAR sensors have appeared, lowering the barrier to 3D data measurement. Because point clouds include details of ground surfaces and structures, they can capture subtle undulations and shapes that conventional 2D drawings or sparse survey points cannot.

Point cloud data can be thought of as a complete digitalization of a site, and their applications are expanding. In civil engineering and construction, point clouds are used for as-built management, volume calculations, and construction planning. They are also used in many contexts where 3D recording and visualization are required, such as equipment inspection, cultural heritage documentation, and spatial reproduction for VR/AR. However, point cloud data often become very large files—millions to hundreds of millions of points—making them difficult to handle without dedicated software or high-performance PCs.

Benefits of displaying point cloud data on Google Maps

So why display point cloud data on Google Maps? Here are the benefits.

• Intuitive 3D confirmation: By overlaying point clouds on map platforms like Google Earth, you can review the acquired 3D data together with real terrain and aerial imagery. Because everyone is familiar with the map platform, stakeholders can intuitively understand shapes and positional relationships, facilitating smoother communication.

• Sharing without specialized software: Even without a dedicated point cloud viewer or CAD software, you can display 3D data with the free Google Earth. If you send a KML file by email or share it via Google Drive, the recipient can easily view the data without specialist knowledge.

• Understanding site context: As standalone data, point clouds are collections of coordinates, but when displayed on a map you can grasp their relationship to surrounding environments and topography. For example, overlaying a point cloud of a surveyed development site on aerial imagery instantly clarifies its relationship to nearby roads and buildings, making reports more convincing.

• Faster decision making: If you share KML via the cloud and clients or designers can each check the 3D situation in Google Earth, discussions and decisions can proceed without visiting the site. Information that was hard to convey on paper can be visually shared through the combination of 3D data and maps, accelerating consensus building.

In this way, 3D display of point cloud data on maps offers major advantages in both visual clarity and ease of sharing. Next, let’s take a closer look at the key format: KML.

What is KML used for 3D data visualization

KML (Keyhole Markup Language) is an XML-based file format for displaying geospatial information in Google Earth, Google Maps, and similar tools. It was developed to express geospatial information and can describe various geo-data such as points (markers), lines, polygonal areas, and models (3D models). Originally popularized by Google Earth (formerly Keyhole’s Earth viewer), it is now an international standard of the OGC (Open Geospatial Consortium).

A KML file can specify longitude, latitude, and altitude together with icons, colors, and annotation text. For example, you can place markers at specific locations, draw profile lines, or embed COLLADA-format 3D models (file extension .dae) to display building models; the expressive capabilities are rich. KML does not natively embed point cloud data itself, but by writing the coordinate points that make up a point cloud as individual Placemarks in the KML, you can pseudo-display a point cloud. In practice, displaying a large number of points at once can impose a heavy rendering load, so when handling point clouds in KML it is common to thin (sample) the points appropriately or convert them to a mesh model and then load them as a COLLADA model.

KML is a general-purpose format supported by many GIS software packages and online map services. Converting data to KML enables use in various environments beyond Google Earth.

Because KML files can become large in practical use, it is also common to split them into multiple files or package related images and model files together into KMZ format (a ZIP-compressed KML package). KMZ bundles everything into a single file, making sharing easier and allowing Google Earth to unpack the contents.

How to convert point cloud data to KML

So how can you actually output point cloud data as KML files? Here are some common approaches.

• Export from GIS/CAD software: Some specialized GIS or 3D CAD tools can export geodata, including point clouds, to KML/KMZ. Load point cloud data (LAS, PLY, etc.) into the software, thin the points and color them as needed, then use the “Export to KML” function to generate a KML file that can be displayed on a map. However, some software may not support point clouds or may perform slowly with very large datasets.

• Use conversion tools or scripts: Even without dedicated software, you can export the point cloud XYZ coordinates to text and integrate them into a KML XML structure yourself. For example, write the point cloud to CSV and use Python or an Excel macro to inject it into a KML template to auto-generate the file. This method writes each point as a Placemark element in the KML. It can be automated for large point counts but requires programming knowledge and effort.

• Upload to lightweight viewers: Some national or research institution web tools let you upload small-to-moderate point clouds and display them on a map. However, the scale and features supported are limited, and such services may not be suitable for sensitive data.

• Use measurement device built-in features: Some measurement devices and apps include one-button KML export of acquired data. For example, as described later, if a service like LRTK automatically processes captured point clouds in the cloud and provides downloadable KML files, users don’t need to worry about complicated conversion steps.

As shown above, there are several ways, but for general users the easiest approach is when the measurement tool itself supports KML output. The next section looks in detail at a convenient device with such features: LRTK.

Easy sharing and visualization of point cloud data with LRTK

LRTK is a compact positioning and 3D measurement device used with a smartphone. It consists of a dedicated ultra-compact RTK-GNSS receiver that attaches to a phone and an app, enabling anyone to easily perform centimeter-level accuracy (half-inch accuracy) positioning and point cloud scanning—a versatile surveying tool. By achieving precise surveying that formerly required expensive GPS equipment or laser scanners with a palm-sized device and a smartphone, LRTK is attracting attention as a product that significantly advances on-site simple surveying.

Sharing and leveraging point cloud data obtained with LRTK is also very simple. Key points include:

• Immediate cloud storage and automatic processing of measurement data

• One-touch KML export (can be sent by email immediately on site)

• Cloud conversion to various formats such as LandXML and CSV (no additional office processing required)

• Issuance of a shareable URL to view the point cloud 3D model in a browser (recipients do not need any software)

By using LRTK, the process from acquiring point cloud data to visualization and sharing is dramatically simplified. You can walk around the site with just a smartphone and complete high-accuracy point cloud measurement, then share the 3D model with stakeholders and move on to the next action. In one field example using LRTK Phone, a survey that took several people half a day was reduced to a few minutes with a smartphone point cloud scan, demonstrating the method’s efficiency on-site. The major appeal of simple surveying with LRTK is that it enables teams to rapidly perform 3D surveying and drawing—tasks that were previously left to specialists—by themselves and at speed.

Summary: Start simple surveying with LRTK

Outputting point cloud data to KML and visualizing and sharing it on Google Maps makes conveying 3D information markedly easier. However, conventional approaches to this workflow required data conversion and software operation knowledge, which posed a high barrier. Introducing solutions like LRTK allows anyone to easily perform high-accuracy point cloud measurement on site and output and share the results directly in formats like KML. Because you can complete 3D data sharing without conscious/manual processing, on-site productivity and the speed of information sharing can improve dramatically.

Above all, the advantages of simple surveying with LRTK are speed, ease, and accuracy. Being able to measure when needed, share immediately, and move quickly to the next action directly boosts site efficiency. If you are looking to advance digital construction management including the use of point cloud data, consider adopting this latest compact surveying device LRTK. By incorporating such advanced technology, site DX will accelerate and conventional surveying and construction management practices will begin to change significantly. LRTK’s unprecedented ease of use will surely expand the possibilities of surveying and measurement work, becoming a reliable partner.

FAQ

Q: Can Google Maps display point cloud data directly? A: Standard Google Maps (2D view) cannot display point cloud data such as LAS or PLY as-is. A practical way to display point clouds on a map is to convert them to KML and view them in Google Earth as described here. You can import KML into “My Maps,” which is part of Google Maps functionality, but My Maps does not support 3D display that includes altitude information, so viewing in Google Earth is required.

Q: What is the difference between KML and KMZ? A: KML is a text-based file format, while KMZ is the ZIP-compressed version of that KML file. KMZ not only reduces file size but also lets you package photos and 3D model files referenced by the KML into a single package. The basic information is the same, so you can open KMZ in Google Earth just like a KML and the contained data will be expanded. When emailing point cloud KMLs, it’s advisable to package related files into a KMZ for reliability.

Q: How should I view KML files? A: The common method is to use the desktop Google Earth Pro (free). Install Google Earth Pro and double-click the KML or KMZ file to automatically launch Earth and display the 3D content. As a no-install alternative, you can import KML in the web-based Google Earth Web (new Google Earth). Open Earth in a browser such as Google Chrome and use the “Import project” function to specify the KML/KMZ file to render it on the globe.

Q: Is converting point cloud data to KML difficult? A: For those without expertise, manually converting point clouds to KML can be a hurdle. It often requires GIS software or programming skills, and adjusting large point sets can be cumbersome. Tools like LRTK that include built-in KML export eliminate that effort by automatically generating KML files. If KML is output on-site with a single button press, you reduce the risk of conversion errors and gain peace of mind.

Q: If the recipient does not have Google Earth, how should I share point cloud data? A: Since Google Earth is free, guiding recipients to install it is one option. With LRTK, sharing is even easier: point cloud data uploaded to the LRTK cloud can be shared via a link as a 3D model viewable in a browser. Recipients need no special software—just click the link to view the latest data. If datasets are large, LRTK can split point clouds or simplify models as needed to ensure smooth display.

Q: What is simple surveying with LRTK? A: Simple surveying refers to surveying methods that anyone can perform easily without specialized skills. By combining a smartphone with LRTK, high-precision surveying that previously required experts and expensive equipment is simplified. Fusing centimeter-class accuracy (half-inch accuracy) RTK positioning with a smartphone’s 3D scanning capabilities enables a single person to quickly obtain on-site 3D data. Because acquired data can be uploaded to the cloud on-site and automatically converted into drawings and 3D models, there is no need to be concerned about difficult post-processing. In short, LRTK-based simple surveying makes “fast, easy, and high-accuracy” surveying possible on site.