fout_laz <- paste0(dir_out, "/*.laz")
fout_dtm <- paste0(dir_out, "/*_dtm.tif")
fout_chm <- paste0(dir_out, "/*_chm.tif")
del = triangulate(filter = keep_ground())
norm = transform_with(del)
dtm = rasterize(1, del, ofile = fout_dtm)
chm = rasterize(1, "max", ofile = fout_chm)
write = write_las(ofile = fout_laz)
pipeline = del + norm + write + dtm + chm
ans = exec(pipeline, on = f, progress = TRUE)Complex tasks
The complex tasks described below consist of multiple individual steps executed within a single processing workflow, closely simulating real-world scenarios. When processing point cloud data, routines often involve a series of tasks performed sequentially, where data is read, processed, and saved at each stage. However, lasR allows users to build processing pipelines where the input data is read only once. This approach significantly reduces the overall processing time.
Complex task 1
This processing routine included three tasks: generate DEM, normalize the point cloud, and generate CHM. The following lasR pipeline was developed:
The total processing time for data processed sequentially was calculated by summing the processing times of the three basic tasks included in the routine.
Results
