Visualize 350 TB of LiDAR Point Clouds Directly in Your Browser

The USGS 3D Elevation Program (3DEP) has collected LiDAR data across the United States totaling over 350 TB and 75 trillion points. Thanks to cloud-native formats and open data hosting by AWS and Microsoft Planetary Computer, you can now stream and explore this massive dataset directly in your browser without downloading anything.

In this tutorial, I walk through two ways to access the data using the USGS LiDAR Viewer I created: COPC (Cloud Optimized Point Cloud) tiles from Microsoft Planetary Computer for small areas, and Entwine Point Tiles (EPT) from AWS for large-area streaming. I also cover new visualization features including customizable color maps, elevation range controls, and elevation filtering.

What You Will Need¶

• A modern web browser (Chrome, Firefox, or Edge)

• An internet connection

No software installation, accounts, or downloads are required.

Two Ways to Access the Data¶

The viewer provides two access modes, selectable in the upper-right corner:

COPC (Cloud Optimized Point Cloud)¶

• Data source: Microsoft Planetary Computer

• The LiDAR data has been subdivided into small rectangular tiles.

• Best for viewing small areas at full detail.

• Select one or more tiles from the map and click Load Selected to visualize them.

• Uses a temporary access token from Planetary Computer. For bulk downloading, use your own Planetary Computer account.

EPT (Entwine Point Tiles)¶

• Data source: AWS Open Data Program

• The LiDAR data is organized by acquisition campaign and virtually mosaicked using Entwine indexing.

• Best for viewing large areas all at once without clicking individual tiles.

• Campaign footprints can be large and irregular (following the actual acquisition boundaries rather than rectangular grids).

• A single campaign can contain hundreds of billions of points. The largest in the dataset has over 467 billion points.

Credits to Howard Butler for processing the data into cloud-optimized formats, USGS for acquiring the data, and Microsoft and AWS for hosting it publicly.

View Data with COPC¶

1. Open the USGS LiDAR Viewer.

2. Make sure COPC is selected in the upper-right corner.

3. Zoom to your area of interest (e.g., Chicago).

4. Click Search Map Extent or draw a rectangle to find available tiles.

5. The footprints appear as rectangular tiles on the map. Select one or more tiles.

6. Click Load Selected to stream the point cloud data.

The points render directly in the browser. You can rotate, zoom, and pan to explore the 3D data. Each tile typically contains tens of millions of points.

View Data with EPT¶

1. Switch to EPT mode in the upper-right corner.

2. Zoom to your area of interest.

3. Click Search Map Extent to find available campaigns.

4. The footprints now follow campaign boundaries rather than rectangular grids. A single footprint can cover an entire county or region.

5. Select a campaign and click Load Selected.

The viewer streams point cloud data tile by tile based on your current map view. For large campaigns (93 billion points for the Chicago area, for example), it may take several seconds for all tiles to load. As you zoom in, higher-resolution data loads progressively.

This approach is especially powerful for getting an overview of a large area without clicking dozens of individual tiles.

Customize the Visualization¶

The viewer includes several tools for controlling how the point cloud is displayed.

Color maps¶

The right panel provides a selection of color maps (similar to those available in Matplotlib). Switch between them interactively to find the best representation for your data. Options include sequential, diverging, and qualitative color schemes.

Elevation range¶

Adjust the minimum and maximum values used for color mapping:

• Relative mode: Drag the sliders to set the range as a percentage of the data’s actual min/max values. This is useful when extreme outliers skew the visualization.

• Absolute mode: Enter specific elevation values in meters. The color bar at the top updates dynamically as you adjust.

• Click Reset to return to the original range.

Elevation filter¶

Unlike the color range control (which changes colors but still displays all points), the elevation filter removes points outside the specified range entirely. This is useful for isolating specific elevation bands, such as removing ground-level points to see only elevated features, or filtering out tall structures to focus on terrain.

Other visualization options¶

• Intensity: View the return intensity values instead of elevation.

• Classification: View points colored by their LiDAR classification (ground, vegetation, buildings, etc.).

• Z offset: Adjust the vertical offset to place the data at ground level. For large areas with significant elevation variation, the default offset may not be sufficient, so the visualization may appear elevated.

Exploring the Entwine Index¶

If you want to browse the campaign metadata directly, the Entwine USGS page lists all available campaigns with their footprints and total point counts. Each campaign links to its EPT JSON endpoint, which contains bounding box information and references to the underlying LAZ files on AWS.

Tips¶

• Start small: When using EPT mode for the first time, try a moderately sized campaign before loading one with hundreds of billions of points.

• Zoom in for detail: The viewer uses progressive loading, so zooming in reveals finer detail as higher-resolution tiles stream in.

• Download smaller pieces: If you want to download data locally, use the COPC mode to select individual tiles rather than trying to download an entire EPT campaign at once, which can be tens of gigabytes.

• Performance: Rendering is GPU-accelerated in the browser. Close other GPU-intensive applications for the best experience.

Resources¶

• USGS LiDAR Viewer: usgs-lidar.gishub.org

• GitHub Repository: github.com/opengeos/maplibre-gl-usgs-lidar

• Entwine USGS Index: usgs.entwine.io

• USGS 3DEP Program: usgs.gov/3d-elevation-program

The ability to stream and visualize 75 trillion points without downloading a single file demonstrates the power of cloud-native geospatial formats. Whether you are scouting data before a download, exploring terrain for a study area, or simply curious about what LiDAR coverage exists for a location, the viewer makes it easy to get started. Feel free to open an issue on the GitHub repository if you have questions or feature requests.