filters.sparsesurface

filters.sparsesurface#

The Sparse Surface filter segments input points into two classes: ground or low point. It does this by adding ground points in ascending elevation order, and masking all neighbor points within a specified radius as low points. This process creates a sparse sampling of the ground estimate akin to the Poisson disk sampling available in filters.sample and marks all other points as low noise. It is expected that the input point cloud will either only include points labeled as ground or the where option will be employed to limit points to those marked as ground.

Default Embedded Stage

This stage is enabled by default

Example #1#

The sample pipeline below uses the SMRF filter to segment ground and non-ground returns, uses the expression filter to retain only ground returns, and then the sparse surface filter to segment ground and low noise.

[
    "input.las",
    {
        "type":"filters.smrf"
    },
    {
        "type":"filters.expression",
        "expression":"Classification==2"
    },
    {
        "type":"filters.sparsesurface"
    },
    "output.laz"
]

Example #2#

This sample pipeline is nearly identical to the previous one, but retains all points (including non-ground) while still only operating on ground returns when computing the sparse surface. It also sets the only option unique to the sparse sample filter, which is the sampling radius–no two ground points will be closer than 3.0 meters (horizontally).

[
    "input.las",
    {
        "type":"filters.smrf"
    },
    {
        "type":"filters.sparsesurface",
        "radius":3.0,
        "where":"Classification==2"
    },
    "output.laz"
]

Options#

radius

Mask neighbor points as low noise. [Default: 1.0]

where

An expression that limits points passed to a filter. Points that don’t pass the expression skip the stage but are available to subsequent stages in a pipeline. [Default: no filtering]

where_merge

A strategy for merging points skipped by a ‘where' option when running in standard mode. If true, the skipped points are added to the first point view returned by the skipped filter. If false, skipped points are placed in their own point view. If auto, skipped points are merged into the returned point view provided that only one point view is returned and it has the same point count as it did when the filter was run. [Default: auto]