LAS To Raster function

The LAS To Raster function is used to render lidar data stored using the LAS file format. The function will be used when you add lidar data to a mosaic dataset using the LAS raster type. With this function, you need to specify both input and output properties. Also, due to the resolution of the data and the time it can take to convert the point data to raster data, this function can write preprocessed raster data files to an output location (cache).

LAS is an industry format created and maintained by the American Society for Photogrammetry and Remote Sensing (ASPRS). This function supports version 1.0, 1.1, 1.2, and 1.3.

This function allows you to add the LAS data by selecting individual LAS files or selecting one or more folders containing LAS files. When adding a folder, all LAS files in the folder will be added to the mosaic dataset as individual items. Therefore, you will see each LAS file's extent and properties (such as average point distance) in the mosaic dataset. However, for folders with hundreds or thousands of LAS files, you may want to add the LAS folder as one dataset, thereby creating only one item in the mosaic dataset. To do this, check Treat each folder as a dataset. When using this option, the LAS files must all use the same spatial reference system; otherwise, they will not be added correctly.

The output location for the preprocessed raster data files defaults to the location next to the geodatabase where the mosaic dataset is stored, for file or personal geodatabases. When using a geodatabase stored in ArcSDE, they are stored within the geodatabase, by default. This location can be changed on the General tab of the LAS To Raster function dialog box.

Input properties

Input—The path and name of the LAS files or folder containing the LAS files. You can modify this value if the input is moved. Adding using a folder is recommended for many LAS files.

Return types—A single pulse from the lidar sensor can be returned more than once as it reflects off objects at different heights on or above the ground, resulting in pulses returning to the sensor at different times. Therefore, the return type can be used to differentiate ground returns from other returns, such as tree canopy. You can select one or more return values.

Diagram of return types

Class types—Classifications are defined for the points by the provider of the LAS files. You can select Any to add all the points regardless of their classification; you can also select more than one. The classification types (according to LAS specification 1.3) are Any, (0) Never Classified, (1) Unclassified, (2) Ground, (3) Low Vegetation, (4) Medium Vegetation, (5) High Vegetation, (6) Building, (7) Noisy Low Point, (8) Model Key Point, and (9) Water.

LAS example representing elevation

Data types—Defines the value to represent when generating the surface.

LAS example representing intensity

Output properties

Output properties affect how the LAS data is converted from points to raster and is displayed.

Pixel size—The minimum pixel size that will be generated to create the raster. Generally, if the pixel size is three times greater than the point spacing, the voids in the data should be filled (unless, for example, the voids are due to water).

The pixel size must be specified when adding the LAS data to the mosaic dataset.

It's important to understand that the point spacing estimates are for either all points or only the points per return type or class type. For example, with first or last return types, the point density is high, whereas, if you select fifth return types only, the point density will be much less and the average point spacing will be much higher. Typically, the Ground class type has many points, but there will be many voids due to buildings or trees that are removed. If you select buildings only or large trees only, there are even more voids and, therefore, a smaller point density and larger average point spacing.

It is better to go with a pixel size that is several times larger than the average point spacing but small enough to identify gaps or voids. A reasonable size is four times the point spacing. For example, if your data is sampled at 1 meter and your pixel size is 4, you can expect, on average, to get 16 points in a pixel.

In most cases, the point spacing is supplied by the vendor along with the point data files and can be found in a metadata file. If the point spacing is not known and you have the ArcGIS 3D Analyst extension, you can use the Point File Information tool to obtain a point spacing for the supplied data files. Otherwise, enter 1, add the LAS files, then check the mosaic dataset's attribute table for the correct value. If necessary, you can edit the value you entered in the LAS To Raster function.

Binning—This is the process of determining the value of a pixel by examining the points that fall within the pixel to determine the final value. It uses the following inputs:

To learn about the limitations of using Plane Fitting/IDW, see Adding lidar data to a mosaic dataset.

Triangulation—Uses Delaunay triangulation to create a surface from a network of triangular facets defined by nodes and edges that cover the surface, which is then rasterized. This is recommended for low density lidar data, when binning can't be used to create an appealing surface, or when zooming in to an area that will cause a low density lidar surface to be displayed.

TipTip:

If the pixel size is 3-4 times larger than the average point distance, you can safely use binning. If the cell size is smaller than that, you can try binning with void filling turned off. If the resulting raster mainly contains voids and only a few single data cells, binning generally does not produce a meaningful elevation raster. You need to either increase the pixel size or switch to triangulation. If the resulting raster shows enough content with some salt and pepper voids, and maybe a few larger voids, you can use binning with void filling turned on. Click the Void filling drop-down arrow and select either Simple or Plane Fitting/IDW.

Z factor—The scaling factor used to convert the z-values. The scaling factor is used for two purposes: (1) to convert the elevation units (such as meters or feet) to the horizontal coordinate units of the dataset, which may be feet, meters, or degrees, and (2) to add vertical exaggeration for visual effect.

To convert from feet to meters or vice versa, see the table below. For example, if your z-units are feet and your mosaic dataset's units are meters, you would use a value of 0.3048 to convert your z-units from feet to meters (1 foot = 0.3048 meters).

This is also useful when you have geographic data (such as GCS_WGS84 using latitude and longitude coordinates) where the z-units are in meters. In this case, you need to convert from meters to degrees (0.00001; see below). The values for degree conversions are approximations.

Units conversion factor

From

To

Feet

Meters

Degrees

Feet

1

0.3048

0.000003

Meters

3.28084

1

0.00001

Units conversion factor

To apply vertical exaggeration, you must multiply the conversion factor by the exaggeration factor. For example, if both z-values and dataset coordinates are meters and you want to exaggerate by a multiple of 10, the scaling factor would be unit conversion factor (1 from the table) multiplied by the vertical exaggeration factor (10), or 10. As another example, if the z-values are meters and the dataset is geographic (degrees), you would multiply the units conversion factor (0.00001) by 10 to get 0.0001.

Cache folder—The location where the cached LAS surfaces will be stored. By default, the cache is generated and stored in a folder next to where the mosaic dataset resides. This folder has the same name as the geodatabase, with a .cache extension. However, if the mosaic dataset is created in an ArcSDE geodatabase, the cache will be created within that geodatabase.

Number of cached surfaces—The maximum number of caches that can be created using different properties (in this dialog box) for this surface. For example, you might add the LAS data to create a surface displaying all the points but you also want to visualize only the points that are classified as ground from the same dataset; therefore, you can create two caches to visualize this data in two ways. Entering a value of 0 will disable caching or clear an existing cache.

Rendering the LAS data can be computationally intensive. Without the cache, you may have to wait several minutes for some surfaces to display. The cache is generated when the following occur:

The cache will be updated in the following scenarios:

Related Topics

5/18/2014