# Create a function that can process a LAScatalog

The following demonstrates how to write your own functions that are fully applicable on a broad collection of point clouds and based on the available lidR tools. We will create a simple filter_noise function. This example should not be considered as the reference method for filtering noise, but rather as a demonstration to help understand the logic behind the design of lidR, and as a full example of how to create a user-defined function that is fully operational.

## Design a noise filter

A simple (too simple) way to detect outliers is to measure the 95th percentile of height in 10 x 10-m pixels (area-based approach) and then remove the points that are above the 95th percentile in each pixel plus, for example, 20%. This can easily be built in lidR using pixel_metrics, merge_spatial and filter_poi, and should work either on a normalized or a raw point cloud. Let’s create a function method filter_noise:

filter_noise = function(las, sensitivity)
{
p95 <- pixel_metrics(las, ~quantile(Z, probs = 0.95), 10)
las <- merge_spatial(las, p95, "p95")
las <- filter_poi(las, Z < p95*sensitivity)
las$p95 <- NULL return(las) } This function is fully functional on a point cloud loaded in memory las <- readLAS("file.las") las <- filter_noise(las, sensitivity = 1.2) writeLAS(las, "denoised-file.las") ## Extend the filter_noise function to a LAScatalog Users can access the catalog processing engine with the function catalog_apply i.e. the engine used internally. It can be applied to any function over an entire collection. This function is complex and we created a simplified (but less versatile) version names catalog_map that suit for most cases. Here we will apply our custom filter_noise function with catalog_map. To use our function filter_noise on a LAScatalog we must create a compatible function (see documentation of catalog_apply): filter_noise = function(las, sensitivity) { if (is(las, "LAS")) { p95 <- pixel_metrics(las, ~quantile(Z, probs = 0.95), 10) las <- merge_spatial(las, p95, "p95") las <- filter_poi(las, Z < p95*sensitivity) las$p95 <- NULL
return(las)
}

if (is(las, "LAScatalog"))
{
res <- catalog_map(las, filter_noise, sensitivity = sensitivity)
return(res)
}
}

And it just works. This function filter_noise is now fully compatible with the catalog processing engine and supports all the options of the engine.

myproject <- readLAScatalog("folder/to/lidar/data/")

opt_filter(myproject)       <- "-drop_z_below 0"
opt_chunk_buffer(myproject) <- 10
opt_chunk_size(myproject)   <- 0
opt_output_files(myproject) <- "folder/to/lidar/data/denoised/{ORIGINALFILENAME}_denoised"

output <- filter_noise(myproject, tolerance = 1.2)

## Finalize the functions

As is, the function filter_noise is not actually complete. Indeed the processing options were not checked. For example, this function should not allow the output to be returned into R otherwise the whole point cloud will be returned.

filter_noise = function(las, sensitivity)
{
if (is(las, "LAS"))
{
p95 <- pixel_metrics(las, ~quantile(Z, probs = 0.95), 10)
las <- merge_spatial(las, p95, "p95")
las <- filter_poi(las, Z < p95*sensitivity)
las\$p95 <- NULL
return(las)
}

if (is(las, "LAScatalog"))
{
options <- list(
need_output_file = TRUE,    # Throw an error if no output template is provided
need_buffer = TRUE)         # Throw an error if buffer is 0
res <- catalog_map(las, filter_noise, sensitivity = sensitivity, .options = options)
return(res)
}
}

Now you know how to build your custom functions that work either on a LAS or a LAScatalog object. Be careful, catalog_map is only a simplification of catalog_apply with restricted capabilities. Check out the documentation of catalog_apply.