A LAS dataset pyramid structure is used to improve 3D display performance for a LAS dataset in ArcGIS Pro. It does this by organizing and indexing the points in a way that optimizes 3D display queries. LAS dataset pyramids use an octree-based indexing scheme that partitions space into a set of nested cubes. It's a 3D scheme that tends to retain more detail while still being fast. For example, an octree-based solution better supports visualizing and navigating to outlying points of a LAS dataset. The current status for the LAS dataset pyramid is reported on the LAS dataset properties dialog box. The LAS dataset pyramid structure is created using the Build LAS Dataset Pyramid tool. LAS dataset layers that don't have a defined pyramid structure will behave as previously.
When a pyramid is built, a folder of files is created in the same folder where the LAS dataset is stored. The naming convention for the pyramid folder is the .lasd name plus the suffix .slas. A LAS dataset pyramid works only on a LAS dataset and not individual LAS or zLAS files. When a pyramid is made for a dataset, its version number is increased and can't be opened or used by older software (earlier than ArcGIS Pro 2.6). You can remedy this by making a version of the dataset without a pyramid; *.lasd files are small and easy to make. Alternately, you can revert a LAS dataset back to its earlier version by using the LAS dataset properties dialog box to delete the pyramid.
Pyramid selection methods
The LAS dataset pyramid partitions space into cubes—small cubes for more foreground rendering and large cubes for background rendering. Each cube references a point, which is a level-of-detail biasing that controls the influence of the visual experience in a 3D space. Pyramid bias types or selection methods allow control over how lower level-of-detail points get selected and displayed.
The point budget is not based on the assumption that all points will be displayed, but more intelligently on the filtered point counts. For example, if points are selected by the transmission line classification code, that classification code will get the whole budget and will be less likely to be thinned out of the rendering. Their display will be maximized in the scene.
There are four types of point selection methods in ArcGIS Pro for LAS dataset pyramids.
|Pyramid selection method||Description|
Closet to center
The point closest to the center of the cube is selected. This option is best when you don't have an application-specific interest in using the data. This method tends to produce renderings where the drawn point distribution is even.
The point selected is the weighted class code, with the class code selected given the highest weight. If you're focused on specific features, like powerlines, you can use this to make the points more apparent in the scene.
The lowest point in the cube is selected. This is a reasonable choice if your primary interest in the lidar is for water resources. For lower level-of-detail areas, it will tend to draw points in low areas and channels.
The highest point in the cube is selected. This is a good choice if your primary interest in the lidar is for any type of visibility, like obstructions or air navigation safety. It will bias rendering to the tops of buildings, antennae, tree tops, and so forth.
Create a LAS dataset pyramid
To create pyramids for a LAS dataset, follow these steps:
- Open the Build LAS Dataset Pyramid geoprocessing tool.
- Select the LAS dataset to generate pyramids for.
- Select Point Selection Method to use for the pyramids.
- Click Run to execute the tool.
Classifying or editing LAS datasets that have pyramids
When edits are made to the class codes of a pyramided dataset, the edits to the LAS files need to be synchronized with the pyramid. Otherwise, it becomes outdated and won't be used for rendering. This syncing, or updating, of the pyramid is done automatically when using the interactive LAS class code editing tools. It's also done by geoprocessing tools that modify class codes. They have an option to update the pyramid that's on by default.
Pyramid update time is dependent on the number of points that need to be updated. When using geoprocessing tools that modify class codes, potentially large numbers of points get modified. When that's the case, a pyramid update will take longer, but it will always take less time than building a pyramid from scratch.
If you're using multiple geoprocessing tools in a row, such as in a model or script, it can be beneficial to hold off on a pyramid update until the last tool. In that situation, turn off the option to update until the last step. You can also use the Build LAS Dataset Pyramidtool as a post process to perform the updating.
You can find information about the status of a dataset's pyramid on the dataset LAS dataset properties dialog box. It has a Pyramid pane that reports whether or not a pyramid exists, and if so, what point selection method was used to build it, and whether it's up to date.
Special cases and considerations include the following:
When LAS files are added or removed from a pyramided LAS dataset, or LAS files are edited outside the scope of the LAS dataset, its pyramid becomes completely dirty and will not be used for display. In this case, the pyramid needs to be rebuilt from scratch. To do this, use the Build LAS Dataset Pyramid tool.
When editing LAS datasets with class weights based pyramids, the edits are updated into the pyramid, but it's no longer guaranteed to honor the weights. The pyramid is still used for display but is technically considered out of date. You can address this using the Build LAS Dataset Pyramid tool.
If you manually edit the withheld flag of one or more points, regardless of pyramid type, the pyramid is still used for display but is technically considered out of date. You can address this using the Build LAS Dataset Pyramid tool.
The pyramid building process will use memory as available and page to disk if needed. The paged data is written to the geoprocessing scratch workspace. You can set this location on the geoprocessing Environment tab. The amount of temporary disk needed is roughly the shortfall between available memory and the size of the input (uncompressed) LAS files. More memory availability will reduce the need for disk and will improve processing time. 16 GB of RAM is a recommended minimum. Processing of larger datasets will benefit from more memory.