This tool grids LiDAR files using nearest-neighbour (NN) scheme, that is, each grid cell in the output image will be assigned the parameter value of the point nearest the grid cell centre. This method should not be confused for the similarly named natural-neighbour interpolation (a.k.a Sibson's method). Nearest neighbour gridding is generally regarded as a poor way of interpolating surfaces from low-density point sets and results in the creation of a Voronoi diagram. However, this method has several advantages when applied to LiDAR data. NN gridding is one of the fastest methods for generating raster surfaces from large LiDAR data sets. NN gridding is one of the few interpolation methods, along with triangulation, that will preserve vertical breaks-in-slope, such as occur at the edges of building. This characteristic can be important when using some post-processing methods, such as the remove_off_terrain_objects tool. Furthermore, because most LiDAR data sets have remarkably high point densities compared with other types of geographic data, this approach does often produce a satisfactory result; this is particularly true when the point density is high enough that there are multiple points in the majority of grid cells.

The output grid can be based on any of the stored LiDAR point parameters (`parameter`), including elevation (in which case the output grid is a digital elevation model, DEM), intensity, class, return number, number of returns, scan angle, RGB (colour) values, time, and user data values. Similarly, the user may specify which point return values (`returns`) to include in the interpolation, including all points, last returns (including single return points), and first returns (including single return points).

The user must specify the grid resolution of the output raster (`resolution`), and optionally, the name of the input LiDAR file (`input`) and output raster (`output`). Note that if an input LiDAR file (`input`) is not specified by the user, the tool will search for all valid LiDAR (.las, .laz, *.zlidar) files contained within the current working directory. This feature can be very useful when you need to interpolate a DEM for a large number of LiDAR files. Not only does this batch processing mode enable the tool to run in a more optimized parallel manner, but it will also allow the tool to include a small buffer of points extending into adjacent tiles when interpolating an individual file. This can significantly reduce edge-effects when the output tiles are later mosaicked together. When run in this batch mode, the output file (`output`) also need not be specified; the tool will instead create an output file with the same name as each input LiDAR file, but with the .tif extension. This can provide a very efficient means for processing extremely large LiDAR data sets.

Users may excluded points from the interpolation based on point classification values, which follow the LAS classification scheme. Excluded classes are specified using the `exclude_cls` parameter. For example, to exclude all vegetation and building classified points from the interpolation, use --exclude_cls='3,4,5,6'. Users may also exclude points from the interpolation if they fall below or above the minimum (`minz`) or maximum (`maxz`) thresholds respectively. This can be a useful means of excluding anomalously high or low points. Note that points that are classified as low points (LAS class 7) or high noise (LAS class 18) are automatically excluded from the interpolation operation.

The tool will search for the nearest input LiDAR point to each grid cell centre, up to a maximum search distance (`radius`). If a grid cell does not have a LiDAR point within this search distance, it will be assigned the NoData value in the output raster. In LiDAR data, these void areas are often associated with larger waterbodies. These NoData areas can later be better dealt with using the fill_missing_data tool after interpolation.

## See Also

lidar_tin_gridding, lidar_idw_interpolation, lidar_tin_gridding, remove_off_terrain_objects, fill_missing_data

## Function Signature

`def lidar_nearest_neighbour_gridding(self, input_lidar: Optional[Lidar], interpolation_parameter: str = "elevation", returns_included: str = "all", cell_size: float = 1.0, search_radius: float = 2.5, excluded_classes: List[int] = None, min_elev: float = float('-inf'), max_elev: float = float('inf')) -> Raster: ...`

## Project Links

WbW Homepage User Manual Support WbW