Classify LAS Noise (3D Analyst)—ArcGIS Pro

Summary

Classifies LAS points with anomalous spatial characteristics as noise.

Usage

Noise points in LAS returns typically have a detrimental impact on data visualization and analysis. For example, returns from high-flying birds and scattered pulses that encountered cloud cover, smog haze, water bodies, and highly reflective mirrors can distort the z-range of the points surrounding that location. Identifying such points as noise will allow them to be filtered out from the display and eliminated from the production of any derivative data, such as an elevation surface, slope or aspect profile.
The Isolation method will process the LAS data in tiled 3x3 bins based on the region defined by the Neighborhood Width and Neighborhood Height parameters. If the number of LAS points in the analysis volume is less than the Neighborhood Point Limit parameter , the LAS points will be treated as noise. The point limit should reflect a reasonable approximation based on the lidar point density and the number of LAS points that can be anticipated in the analysis volume.
If certain LAS point returns have abnormally high or low z-values for the region captured by the lidar collection, consider using the Absolute Height method to define the z-value threshold of the data to quickly identify the outlier points as noise.
If LAS points have abnormally high or low z-values for specific regions, but those values fall within the range of valid measurements captured in the lidar collection, consider using the Relative Height method to define the z-value threshold of valid data based on an offset from the ground. To generate a ground surface, filter the LAS dataset for ground classified points, and use the LAS Dataset To Raster tool.
Only LAS points with class code values of 0 or 1 will be reclassified. If unclassified points are represented by some other value, consider using the Change LAS Class Codes tool to assign the unclassified points a value of 1. When noise points are being classified and either the Isolation or Absolute Height method is used, all noise points will be assigned a class code value of 7. If the Relative Height method is used, noise points that are below the Minimum Height threshold will be assigned a value of 7, which represents Low Noise, and noise points that are above the Maximum Height threshold will be assigned a value of 18, which represents High Noise.
If you are unsure about the settings to be used for determining noise points, consider exporting the LAS points detected as noise as a point feature while the option to edit the LAS classification code is disabled. If the output points reflect the desired results, you can reclassify the LAS points using those features through the Locate LAS Points By Proximity tool.
The isolation method is a performance intensive operation that runs faster with larger bin sizes. Consider specifying the largest possible bin size depending on the nature and distribution of the data.

Syntax

arcpy.3d.ClassifyLasNoise(in_las_dataset, method, edit_las, withheld, {compute_stats}, ground, low_z, high_z, max_neighbors, step_width, step_height, {extent}, {process_entire_files}, {out_feature_class})

Parameter	Explanation	Data Type
in_las_dataset	The LAS dataset to process.	LAS Dataset Layer
method	The noise detection method that will be used. ISOLATION —Spatial proximity of LAS points will be analyzed in tiled volumes to determine noise measurements along with height based noise detection. This is the default. RELATIVE_HEIGHT —All points below the specified minimum height from the ground surface and above the maximum height from the ground surface will be identified as noise. ABSOLUTE_HEIGHT —All points below the specified minimum height and above the maximum height in relation to mean sea level will be identified as noise.	String
edit_las	Indicates whether LAS points identified as noise will be reclassified. CLASSIFY —Noise points will be reclassified. This is the default. NO_CLASSIFY —Noise points will not be classified.	Boolean
withheld	Indicates whether the withheld classification flag will be assigned to the noise points. This option is only enforced if the edit_las parameter is set to CLASSIFY. WITHHELD —Noise points will have the withheld classification flag assigned. NO_WITHHELD —Noise points will not have the withheld classification flag assigned. This is the default.	Boolean
compute_stats (Optional)	Specifies whether statistics should be computed for the LAS files referenced by the LAS dataset. Computing statistics provides a spatial index for each LAS file, which improves analysis and display performance. Statistics also enhance the filtering and symbology experience by limiting the display of LAS attributes, like classification codes and return information, to values that are present in the LAS file. COMPUTE_STATS —Statistics will be computed. NO_COMPUTE_STATS —Statistics will not be computed. This is the default.	Boolean
ground	The ground surface used to define relative height.	Raster Layer
low_z	The height that defines the lowest z-value threshold for identifying noise points. Any point that is lower than the specified value will be classified as noise. If a ground surface is specified, this threshold will be based on an offset from the ground such that a value of "-3 feet" means any points that are 3 feet below the ground surface will be classified as noise.	Linear Unit
high_z	The height that defines the highest z-value threshold for identifying noise points. Any point that is higher than the specified value will be classified as noise. If a ground surface is provided, this threshold will be based on an offset from the ground such that a value of "250 meters" means any points that are higher than 250 meters above the ground surface will be classified as noise.	Linear Unit
max_neighbors	The maximum number of points inside the analysis volume that can be qualified as noise when using the Isolation method. If the analysis volume contains any number of LAS points that are equal to or less than this value, those points will be classified as noise.	Long
step_width	The size of each dimension in the XY space of the analysis volume when using the Isolation method.	Linear Unit
step_height	The height of the analysis volume when using the Isolation method.	Linear Unit
extent (Optional)	Specifies the extent of the data that will be evaluated by this tool. MAXOF—The maximum extent of all inputs will be used. MINOF—The minimum area common to all inputs will be used. DISPLAY—The extent is equal to the visible display. Layer name—The extent of the specified layer will be used. Extent object—The extent of the specified object will be used. Space delimited string of coordinates—The extent of the specified string will be used. Coordinates are expressed in the order of x-min, y-min, x-max, y-max.	Extent
process_entire_files (Optional)	Specifies how the processing extent is applied. PROCESS_EXTENT —Only LAS points that intersect the area of interest will be processed. This is the default. PROCESS_ENTIRE_FILES —If any portion of a LAS file intersects the area of interest, all the points in that LAS file, including those outside the area of interest, will be processed.	Boolean
out_feature_class (Optional)	The output point features that represent the LAS points identified as noise.	Feature Class

Derived Output

Name	Explanation	Data Type
out_las_dataset	The LAS dataset to be modified.	LAS Dataset Layer

Code sample

ClassifyLasNoise example 1 (Python window)

The following sample demonstrates the use of this tool in the Python window.

arcpy.env.workspace = 'C:/data'

arcpy.ddd.ClassifyLasNoise('Denver_2.lasd', "ABSOLUTE_HEIGHT", 
                           edit_las='CLASSIFY', withheld='WITHHELD', 
                           high_z='450 Feet')

ClassifyLasNoise example 2 (stand-alone script)

The following sample demonstrates the use of this tool in a stand-alone Python script.

'''****************************************************************************
       Name: Classify Lidar & Extract Building Footprints
Description: Extract footprint from lidar points classified as buildings, 
             regularize its geometry, and calculate the building height.

****************************************************************************'''
import arcpy

lasd = arcpy.GetParameterAsText(0)
dem = arcpy.GetParameterAsText(1)
footprint = arcpy.GetParameterAsText(2)

try:
    desc = arcpy.Describe(lasd)
    if desc.spatialReference.linearUnitName in ['Foot_US', 'Foot']:
        unit = 'Feet'
    else:
        unit = 'Meters'
    ptSpacing = desc.pointSpacing * 2.25
    sampling = '{0} {1}'.format(ptSpacing, unit)
    # Classify overlap points
    arcpy.ddd.ClassifyLASOverlap(lasd, sampling)
    # Classify ground points
    arcpy.ddd.ClassifyLasGround(lasd)
    # Filter for ground points
    arcpy.management.MakeLasDatasetLayer(lasd, 'ground', class_code=[2])
    # Generate DEM
    arcpy.conversion.LasDatasetToRaster('ground', dem, 'ELEVATION', 
                                        'BINNING NEAREST NATURAL_NEIGHBOR', 
                                        sampling_type='CELLSIZE', 
                                        sampling_value=desc.pointSpacing)
    # Classify noise points
    arcpy.ddd.ClassifyLasNoise(lasd, method='ISOLATION', edit_las='CLASSIFY', 
                               withheld='WITHHELD', ground=dem, 
                               low_z='-2 feet', high_z='300 feet', 
                               max_neighbors=ptSpacing, step_width=ptSpacing, 
                               step_height='10 feet')
    # Classify buildings
    arcpy.ddd.ClassifyLasBuilding(lasd, '7.5 feet', '80 Square Feet')
    #Classify vegetation
    arcpy.ddd.ClassifyLasByHeight(lasd, 'GROUND', [8, 20, 55], 
                                  compute_stats='COMPUTE_STATS')
    # Filter LAS dataset for building points
    lasd_layer = 'building points'
    arcpy.management.MakeLasDatasetLayer(lasd, lasd_layer, class_code=[6])
    # Export raster from lidar using only building points
    temp_raster = 'in_memory/bldg_raster'
    arcpy.management.LasPointStatsAsRaster(lasd_layer, temp_raster,
                                           'PREDOMINANT_CLASS', 'CELLSIZE', 2.5)
    # Convert building raster to polygon
    temp_footprint = 'in_memory/footprint'
    arcpy.conversion.RasterToPolygon(temp_raster, temp_footprint)
    # Regularize building footprints
    arcpy.ddd.RegularizeBuildingFootprint(temp_footprint, footprint, 
                                          method='RIGHT_ANGLES')

except arcpy.ExecuteError:
    print(arcpy.GetMessages())

Environments

Current Workspace, Output Coordinate System, Geographic Transformations

Licensing information

Basic: Requires 3D Analyst
Standard: Requires 3D Analyst
Advanced: Requires 3D Analyst