LAS utilities
Tools to manipulate LAS objects maintaining compliance with ASPRS specification
las_rescale(las, xscale, yscale, zscale) las_reoffset(las, xoffset, yoffset, zoffset) las_quantize(las, by_reference = TRUE) las_update(las) quantize(x, scale, offset, by_reference = TRUE, ...) is.quantized(x, scale, offset, ...) count_not_quantized(x, scale, offset) storable_coordinate_range(scale, offset)
las |
An object of class LAS |
xscale, yscale, zscale |
scalar. Can be missing if not relevant. |
xoffset, yoffset, zoffset |
scalar. Can be missing if not relevant. |
by_reference |
bool. Update the data in place without allocating new memory. |
x |
numeric. Coordinates vector |
scale, offset |
scalar. scale and offset |
... |
Unused. |
In the specification of the LAS format the coordinates are expected to be given with a certain precision e.g. 0.01 for a millimeter precision (or millifeet), meaning that a file records e.g. 123.46 not 123.45678. Also, coordinates are stored as integers. This is made possible with a scale and offset factor. For example, 123.46 with an offset of 100 and a scale factor of 0.01 is actually stored as (123.46 - 100)/0.01 = 2346. Storing 123.45678 with a scale factor of 0.01 and an offset of 100 is invalid because it does not convert to an integer: (123.45678-100)/0.01 = 2345.678. Having an invalid LAS object may be critical in some lidR applications. When writing into a LAS file, users will loose the extra precision without warning and some algorithms in lidR use the integer conversion to make integer-based computation and thus speed-up some algorithms and use less memory. Creation of an invalid LAS object may cause problems and incorrect outputs.
Other las utilities:
las_check()
LASfile <- system.file("extdata", "example.laz", package="rlas")
las = readLAS(LASfile)
# Manual modification of the coordinates (e.g. rotation, re-alignment, ...)
las@data$X <- las@data$X + 2/3
las@data$Y <- las@data$Y - 5/3
# The point cloud is no longer valid
las_check(las)
# It is important to fix that
las_quantize(las)
# Now the file is almost valid
las_check(las)
# Update the object to set up-to-date header data
las <- las_update(las)
las_check(las)
# In practice the above code is not useful for regular users because the operators
# $<- already perform such operations on-the-fly. Thus the following
# syntax must be preferred and returns valid objects. Previous tools
# were only intended to be used in very specific cases.
las$X <- las$X + 2/3
las$Y <- las$Y - 5/3
# Rescale and reoffset recompute the coordinates with
# new scales and offsets according to LAS specification
las <- las_rescale(las, xscale = 0.01, yscale = 0.01)
las <- las_reoffset(las, xoffset = 300000, yoffset = 5248000)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.