R stages

Rasterize

In the tutorial, we mentioned that rasterize() supports the injection of a user-defined R expression. This is equivalent to pixel_metrics() from the package lidR. Any user-defined function can be mapped, making it extremely versatile but slower.

Let’s compute the map of the median intensity by injecting a user-defined expression. Like in lidR, the attributes of the point cloud are named: X, Y, Z, Intensity, gpstime, ReturnNumber, NumberOfreturns, Classification, UserData, PointSourceID, R, G, B, NIR. For users familiar with the lidR package, note that there is no ScanAngleRank/ScanAngle; instead the scanner angle is always named ScanAngle and is numeric. Also flags are named Withheld, Synthetic and Keypoint.

pipeline = rasterize(10, median(Intensity))
ans = exec(pipeline, on = f)

terra::plot(ans, mar = c(1, 1, 1, 3), col = heat.colors(15))

Notice that, in this specific case, using rasterize(10, "i_median") is more efficient.

Callback

The callback stage holds significant importance as the second and last entry point to inject R code into the pipeline, following rasterize(). For those familiar with the lidR package, the initial step often involves reading data with lidR::readLAS() to expose the point cloud as a data.frame object in R. In contrast, lasR loads the point cloud optimally in C++ without exposing it directly to R. However, with callback, it becomes possible to expose the point cloud as a data.frame for executing specific R functions.

Similar to lidR, the attributes of the point cloud in lasR are named: X, Y, Z, Intensity, gpstime, ReturnNumber, NumberOfreturns, Classification, UserData, PointSourceID, R, G, B, NIR. Notably, for users accustomed to the lidR package, the scanner angle is consistently named ScanAngle and is numeric, as opposed to ScanAngleRank/ScanAngle. Additionally, flags are named Withheld, Synthetic, and Keypoint.

Let’s delve into a simple example. For each LAS file, the callback loads the point cloud as a data.frame and invokes the meanz() function on the data.frame.

meanz = function(data){ return(mean(data$Z)) }
call = callback(meanz, expose = "xyz")
ans = exec(call, on = f)
print(ans)
#>  - 809.0835 
#>  - 13.27202

Here the output is a list with two elements because we processed two files (f is not displayed in this document). The average Z elevation are respectively 809.08 and 13.27 in each file.

Be mindful that, for a given LAS/LAZ file, the point cloud may contain more points than the original file if the file is loaded with a buffer. Further clarification on this matter will be provided later.

The callback function is versatile and can also be employed to edit the point cloud. When the user-defined function returns a data.frame with the same number of rows as the original one, the function edits the underlying C++ dataset. This enables users to perform tasks such as assigning a class to a specific point. While physically removing points is not possible, users can flag points as Withheld. In such cases, these points will not be processed in subsequent stages, they are discarded.

edit_points = function(data)
{
  data$Classification[5:7] = c(2L,2L,2L)
  data$Withheld = FALSE
  data$Withheld[12] = TRUE
  return(data)
}

call = callback(edit_points, expose = "xyzc")
ans = exec(call, on = f)
ans
#> NULL

As observed, here, this time callback does not explicitly return anything; however, it edited the point cloud internally. To generate an output, users must use another stage such as write_las(). It’s important to note that write_las() will NOT write the point number 12 which is flagged withheld. Neither any subsequent stage will process it. The point is still in memory but is discarded.

For memory and efficiency reasons, it is not possible to physically remove a point from the underlying memory in lasR. Instead, the points flagged as withheld will never be processed. One consequence of this, is that points flagged as withheld in a LAS/LAZ file will not be processed in lasR. This aligns with the intended purpose of the flag according to the LAS specification but may differ from the default behavior of many software on the market including lidR.

Now, let’s explore the capabilities of callback further. First, let’s create a lidR-like read_las() function to expose the point cloud to R. In the following example, the user-defined function is employed to return the data.frame as is. When the user’s function returns a data.frame with the same number of points as the original dataset, this updates the points at the C++ level. Here, we use no_las_update = TRUE to explicitly return the result.

read_las = function(f, select = "xyzi", filter = "")
{
  load = function(data) { return(data) }
  read = reader_las(filter = filter)
  call = callback(load, expose = select, no_las_update = TRUE)
  return (exec(read+call, on = f))
}

f <- system.file("extdata", "Topography.las", package="lasR")
las = read_las(f)
head(las)
#>          X       Y        Z Intensity
#> 1 273357.1 5274360 806.5340      1340
#> 2 273357.2 5274359 806.5635       728
#> 3 273357.2 5274358 806.0248      1369
#> 4 273357.2 5274510 809.6303       589
#> 5 273357.2 5274509 809.3880      1302
#> 6 273357.2 5274508 809.4847       123

Ground points can also be classified using an R function, such as the one provided by the RCSF package:

csf = function(data)
{
  id = RCSF::CSF(data)
  class = integer(nrow(data))
  class[id] = 2L
  data$Classification <- class
  return(data)
}

read = reader_las()
classify = callback(csf, expose = "xyz")
write = write_las()
pipeline = read + classify + write
exec(pipeline, on = f)

callback() exposes the point cloud as a data.frame. This is the only way to expose the point clouds to users in a manageable way. One of the reasons why lasR is more memory-efficient and faster than lidR is that it does not expose the point cloud as a data.frame. Thus, the pipelines using callback() are not significantly different from lidR. The advantage of using lasR here is the ability to pipe different stages.

Buffer

Point clouds are typically stored in multiple contiguous files. To avoid edge artifacts, each file must be loaded with extra points coming from neighboring files. Everything is handled automatically, except for the callback() stage. In callback(), the point cloud is exposed as a data.frame with the buffer, providing the user-defined function with some spatial context. If callback is used to edit the points, everything is handled internally. However, if an R object is returned, it is the responsibility of the user to handle the buffer.

For example, in the following pipeline, we are processing two files, and callback() is used to count the number of points. The presence of triangulate() implies that each file will be loaded with a buffer to make a valid triangulation. Consequently, counting the points in callback() returns more points than summarise() because summarise() is an internal function that knows how to deal with the buffer.

count = function(data) { length(data$X) }
del = triangulate(filter = keep_ground())
npts = callback(count, expose = "x")
sum = summarise()
ans = exec(del + npts + sum, on = f)
print(ans$callback)
#>  - 584995 
#>  - 868951
ans$callback[[1]]+ ans$callback[[2]]
#> [1] 1453946
ans$summary$npoints
#> [1] 1355607

We can compare this with the pipeline without triangulate(). In this case, there is no reason to use a buffer, and the files are not buffered. The counts are equal.

ans = exec(npts + sum, on = f)
ans$callback[[1]]+ ans$callback[[2]]
#> [1] 1355607
ans$summary$npoints
#> [1] 1355607

To handle the buffer, the user can read the attribute bbox of the data.frame. It contains the bounding box of the point cloud without the buffer or use the column Buffer that contains TRUE or FALSE for each point. If TRUE, the point is in the buffer. The buffer is exposed only if the user includes the letter 'b'.

count_buffer_aware = function(data) {
  bbox = attr(data, "bbox")
  npoints = sum(!data$Buffer)
  return(list(bbox = bbox, npoints = npoints))
}

del = triangulate(filter = keep_ground())
npts = callback(count_buffer_aware, expose = "b") # b for buffer
sum = summarise()
ans = exec(del + npts + sum, on = f)
print(ans$callback)
#>  - List:
#>    - bbox : 885022.4 629157.2 885210.2 629400 
#>    - npoints : 531662 
#>  - List:
#>    - bbox : 885024.1 629400 885217.1 629700 
#>    - npoints : 823945
ans$callback[[1]]$npoints+ ans$callback[[2]]$npoints
#> [1] 1355607
ans$summary$npoints
#> [1] 1355607

In conclusion, in the hypothesis that the user-defined function returns something complex, there are two ways to handle the buffer: either using the bounding box or using the Buffer flag. A third option is to use drop_buffer. In this case users ensure to receive a data.frame that does not include points from the buffer.

Parallelisation

Read the multithreading page before entering this section.

R is NOT multi-threaded, and thus calling these stages in parallel is not thread-safe and will crash the R session in the best case or deeply corrupt the R memory in the worst case. Consequently, these stages are protected and cannot run concurrently with a concurrent-file strategy. These stages are only meant to build complex but convenient pipelines and do not intend to be production tools. While lasR::rasterize(10, mymetrics(Z, Intensity)) produces the same output as lidR::pixel_metrics(las, mymetrics(Z, Intensity), 10), the lidR version is faster because it can be parallelized on multiple R sessions.

lasR, on the other hand, parallelizes the computation in a single R session. This approach has pros and cons which won’t be discussed in this tutorial. One con is that pipelines using injected R code are not parallelizable by default.