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Classify LAS Overlap

Summary

Classifies LAS points from overlapping scans of aerial lidar surveys.

Illustration

Classify LAS Overlap

Usage

  • The presence of unclassified overlap points may produce undesirable results in operations that assume regular point distribution, such as the Classify LAS Building tool. They may also produce an undesirable margin of error in the production of derived data if the overlaps come from extreme scan angles. Classifying overlap points provides the option to filter them out to achieve a more even point distribution of higher quality returns.

  • 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, then 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 prior to executing this tool.

  • The LAS format supports the classification of each point based on the specifications defined by the American Society for Photogrammetry and Remote Sensing (ASPRS). The ArcGIS platform applies the classification scheme specified for LAS file version 1.4:

    Classification Value Classification Type

    0

    Never Classified

    1

    Unassigned

    2

    Ground

    3

    Low Vegetation

    4

    Medium Vegetation

    5

    High Vegetation

    6

    Building

    7

    Low Noise

    8

    Model Key / Reserved

    9

    Water

    10

    Rail

    11

    Road Surface

    12

    Overlap / Reserved

    13

    Wire – Guard

    14

    Wire – Conductor

    15

    Transmission Tower

    16

    Wire – Connector

    17

    Bridge Deck

    18

    High Noise

    19 – 63

    Reserved for ASPRS Definition (LAS 1.1 to 1.3 support up to class code 31)

    32 – 255

    User Definable (Only supported in LAS 1.0 and certain versions of 1.4)

    Note:

    While the bulk of new class code assignments introduced with LAS 1.4 were previously designated as Reserved, class codes 8 and 12 were changed from Model Key and Overlap to Reserved.

Syntax

ClassifyLasOverlap_3d (in_las_dataset, sample_distance, {extent}, {process_entire_files}, {compute_stats})
ParameterExplanationData Type
in_las_dataset

The 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.

  • PROCESS_EXTENTOnly LAS points that intersect the area of interest will be processed. This is the default.
  • PROCESS_ENTIRE_FILESIf 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
compute_stats
(Optional)

Specifies whether statistics should be computed for the LAS files referenced by the LAS dataset. The presence of statistics allows the LAS dataset layer's filtering and symbology options to only show LAS attribute values that exist in the LAS files.

  • COMPUTE_STATSStatistics will be computed.
  • NO_COMPUTE_STATSStatistics will not be computed. This is the default.
Boolean

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())

Licensing information

  • ArcGIS Desktop Basic: Requires 3D Analyst
  • ArcGIS Desktop Standard: Requires 3D Analyst
  • ArcGIS Desktop Advanced: Requires 3D Analyst

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