Summary
Classifies LAS points from overlapping scans of aerial lidar surveys.
Illustration
Usage
Overlap points represent LAS returns that exist in close proximity to another point from a scan originating in a different flight line. When multiple points from different flight lines are present within a distance shorter than the nominal point spacing, the point with the greater scan angle is typically flagged as overlap. The overlap designation allows the points to be filtered and excluded from visualization and analysis.
This tool is designed to work on tiled LAS files that combine point records from multiple flight lines. Each LAS file is processed individually, the input LAS data on a file-by-file basis, which means it will not identify overlapping points if each flight line is stored in a separate LAS file. The Tile LAS tool can be used to merge a collection of LAS files comprised of individual flight lines into tiled files that merge multiple flight lines.
Unclassified overlap points may produce undesirable results in operations that assume regular point distribution. Data derived from processing points with irregular point densities and distribution may yield an undesirable margin of error. Classifying overlap points allows the LAS data to be filtered to produce a consistent point density and reduce the potential for point returns with higher margin of error.
The point source ID attribute of a LAS point provides information about the flight line from which it was collected. This tool processes LAS data in tiles by determining if multiple point source IDs are present and identifying the ID with the higher magnitude scan angle as the overlap. If multiple points with the same point source ID exist in the area being processed, all the points that share the point source ID of the point with the largest magnitude scan angle will be classified as overlap. For this reason, the sample size used to evaluate the LAS points should be approximately two to three times the size of the nominal point spacing of the LAS data. Larger tile sizes should be avoided because it risks the possibility of misclassifying points with smaller scan angle values. Smaller sample sizes may not capture enough points to properly identify and classify any overlap points.
Overlap points in LAS version 1.4 files and point record formats 6 through 8 will be assigned the overlap classification flag while retaining their original class code value. Overlap points in all other supported LAS files will be assigned a class code value of 12. If the class code value of 12 is already being used by the input LAS files to represent something other than overlapping scans, consider using the Change LAS Class Codes tool to reassign those points to another value before executing this tool.
Syntax
arcpy.ddd.ClassifyLasOverlap(in_las_dataset, sample_distance, {extent}, {process_entire_files}, {compute_stats})| Parameter | Explanation | Data Type |
in_las_dataset | The tiled LAS dataset to process. | LAS Dataset Layer |
sample_distance | The distance of either dimension of the square area used to evaluate the LAS data. This value can be expressed as a number and a linear unit value, such as 3 meters. If linear units are not specified or are entered as Unknown, the unit will be defined by the input LAS file's spatial reference. | Linear Unit |
extent (Optional) | Specifies the extent of the data that will be evaluated by this tool. | Extent |
process_entire_files (Optional) | Specifies how the processing extent is applied.
| 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.
| Boolean |
Derived Output
| Name | Explanation | Data Type |
| out_las_dataset | The LAS dataset to be modified. | LAS Dataset Layer |
Code sample
ClassifyLasOverlap example 1 (Python window)
The following sample demonstrates the use of this tool in the Python window.
arcpy.env.workspace = 'C:/data'
arcpy.ddd.ClassifyLasOverlap('Denver_2.lasd', '1 Meter')
ClassifyLasOverlap 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
Licensing information
- Basic: Requires 3D Analyst
- Standard: Requires 3D Analyst
- Advanced: Requires 3D Analyst