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plot(ctg, chunk = TRUE) does not fail if an
invalid output file template is registered #537locate_trees() throws an informative error if
called with an on-disk raster. The former error was cryptic. If the
raster is small enough it is loaded on-the-fly.merge_spatial() with RGB and
SpatRaster was not working properly #545st_area() better estimates the area of small
point-clouds and is fasterinterpret_waveform #549.plot_metrics() returns NA if 0 points available #551.rasterize_canopy may generate error or messed-up CHM #552.print() and st_area() were not
working for point cloud with no CRStrack_sensor() does not fail with a
LAScatalog when no sensor position is found. It also
triggers a warning. #556.rasterize_terrain() now works with a
LAScatalog and shape = sfc_object #558.catalog_retile() now works when some tiles are
empty #563.crown_metrics() messed up tree IDs with a hull
geometry #554.merge_spatial() crops large vectors to the extent
of the point cloud before to perform the merge. This has for
consequences to sometime transform polygons into multipolygons. When
polygons and multipolygons were mixed the functions stopped with an
error. It now works.normalize_height() now sets the Z offset to 0 #571.We are currently developing rlas 1.6.0 that uses the ALTREP framework
to load compact representation of non populated attributes. For example
UserData is usually populated with zeros (not populated).
Yet it takes 32 bits per point to store each 0. With rlas 1.6.0 it will
only uses 644 bits no matter the number of points loaded for non
populated attributes. This applies to each attribute populated with a
single repeated value. This allows for saving approximately 30% of
memory usage depending on the number of non-populated attributes that
are present in the file. rlas 1.6.0 is compatible will all versions of
lidR but lidR 4.0.1 introduced some internal optimization, internal
fixes and new functions to fully take advantage of rlas 1.6.0. lidR
v<= 4.0.0 will work with rlas 1.6.0 but won’t take advantage of the
new compression feature.
the function LAS() no longer call
data.table::setDT() if the input is already a
data.table. Indeed data.table::setDT()
materializes the compressed ALTREP vectors and this is not what we want.
One consequence of this change is that readLAS() now
preserve the ALTREPness (i.e. the compression) of the output of
rlas::read.las().
Subsetting a LAS object no longer call
data.table native subset. We previously used something like
las@data[indx] to subset the point cloud. Sadly
data.table tries to materialized the ALTREPed vector
whenever it can. We implemented internally a smart_subset()
function that subset and preserves the compression of the vectors. One
consequence of such change is that all filter_*() and
clip_*() functions preserve the compression of the
point-cloud if any.
las_check() has been slightly modified to ensure it
does not materialize ALTREPed object. One side effect of
las_check() was to decompress the point cloud unexpectedly.
Such a pity! We also change las_check() to print
information about the compression.
We changed the way *_metrics() functions evaluates
the user defined expression because we found that it had the side effect
of materializing all the attributes instead of materializing only those
needed. For example pixel_metrics(las, mean(Z)) only needs
the attribute Z. No need to allocate and copy memory for
Intensity, ScanAngle and so on. In previous
version all attributes where inspected with the side effect to
materialize all compressed vectors. The *_metrics()
functions now properly detect which attributes are actually necessary
for the evaluation of func. Two consequences:
(1)*_metrics() functions are 20 to 40% faster, (2) the
compression is preserved if no compressed attribute is used in the
evaluation and e.g. pixel_metrics(las, mean(UserData))
uncompresses only UserData.
New functions las_is_compressed() that tells which
attributes are compressed and las_size() that returns the
true size of a LAS objects taking into account the
compression. las_size() should returns something similar to
pryr::object_size() but different to
object.size() that is not ALTREP aware. We also changed the
print function so it uses las_size() instead
of object.size().
On overall lidR’s functions are expected to almost never decompress a
LAS object. However other R packages and R functions may do it. For
example data.table::print do materializes the ALTREP
vectors. base::range() too but not
base::mean() or base::var().
las@data # Full decompression (print data.table)
range(las$Userdata) # Decompression of UserData
las@data[2, UserData := 1] # Decompression of UserData
las@data[1:10] # Full decompressionrgdal and rgeos will be retired on Jan 1st
2024. see twitter,
youtube, or
see the respective package descriptions on CRAN. Packages
raster and sp are based on
rgdal/rgeos and lidR was based on
raster and sp because it was created before
sf, terra and stars. This means
that sooner or later lidR will run into trouble (actually
it is more or less already the case). Consequently, we modernized
lidR by moving to sf,
terra/stars and we are no longer depending on
sp and raster (see also Older R Spatial
Package for more insight). It is time for everybody to stop using
sp and raster and to embrace sf
and stars/terra.
In version 4 lidR now no longer uses sp, it
uses sf and it no longer uses raster. It is
now raster agnostic and works transparently with rasters from
raster, terra and stars. These
two changes meant we had to rewrite a large portion of the code base,
which implies few backward incompatibilities. The backward
incompatibilities are very small compared to the huge internal changes
we implemented in the foundations of the code and should not even be
visible for most users.
lidR no longer loads raster and
sp. To manipulate Raster* and
Spatial* objects returned by lidR users need to load
sp and raster with:
r library(sp) library(raster) library(lidR)
The formal class LAS no longer inherits the class
Spatial from sp. It means, among other things,
that a LAS object no longer has a slot
@proj4string with a CRS from sp,
or a slot @bbox. The CRS is now stored in the slot
@crs in a crs object from sf.
Former functions crs() and projection()
inherited from raster are backward compatible and return a
CRS or a proj4string from sp.
However code that accesses these slots manually are no longer valid (but
nobody was supposed to do that anyway because it was the purpose of the
function projection()):
r las@proj4string # No longer works las@bbox # No longer works inherits(las, "Spatial") # Now returns FALSE
The formal class LAScatalog no longer inherits the
class SpatialPolygonDataFrame from sp. It
means, among other things, that a LAScatalog object no
longer has a slot @proj4string, or @bbox, or
@polygons. The slot @data is preserved and
contains an sf,data.frame instead of a
data.frame allowing backward compatibility of data access
to be maintained. The syntax ctg$attribute is the way to
access data, but statement like ctg@data$attribute are
backward compatible. However, code that accesses other slots manually is
no longer valid, like for the LAS class:
r ctg@proj4string # No longer works ctg@bbox # No longer works ctg@polygons # No longer works inherits(ctg, "Spatial") # Now returns FALSE
sp::spplot() no longer works on a
LAScatalog because a LAScatalog is no longer a
SpatialPolygonDataFrame
r spplot(ctg, "Max.Z") # becomes plot(ctg["Max.Z"])
raster::projection() no longer works on
LAS* objects because they no longer inherit
Spatial. Moreover, lidR no longer
Depends on raster which means that
raster::projection() and lidR::projection can
mask each other. Users should use st_crs() preferentially.
To use projection users can either load raster
before lidR or call lidR::projection() with
the explicit namespace.
library(lidR)
projection(las) # works
library(raster)
projection(las) # no longer worksSerialized LAS/LAScatalog objects (i.e. stored in
.rds or .Rdata files) saved with
lidR v3.x.y are no longer compatible with
lidR v4.x.y. Indeed, the structure of a
LAS/LAScatalog object is now different mainly because the
slot @crs replaces the slot @proj4string.
Users may get errors when using e.g. readRDS(las.rds) to
load back an R object. However we put safeguards in place so, in
practice, it should be backward compatible transparently, and even
repaired automatically in some circumstances. Consequently we are not
sure it is a backward incompatibility because we handled and fixed all
warnings and errors we found. In the worst case it is possible to repair
a LAS object v3 with:
r las <- LAS(las)
track_sensor() is not backward compatible because it
is a very specific function used by probably just 10 people in the
world. We chose not to rename it. It now returns an sf
object instead of a SpatialPointsDataFrame.
Former functions that return Spatial* objects from
package sp should no longer be used. It is time for
everybody to embrace sf. However, these functions are still
in lidR for backward compatibility. They won’t be removed
except if package sp is removed from CRAN. It might happen
on Jan 1st 2024, it might happen later. We do not know. New functions
return sf or sfc objects. Old functions are
not documented so new users won’t be able to use them.
tree_metrics() and delineate_crowns() are
replaced by a single function crown_metrics() that has the
same functionality, and more.find_trees() is replaced by
locate_trees().Older functions that return Raster* objects from the
raster package should no longer be used. It is time for
everybody to embrace terra/stars. However, these functions
are still in lidR for backward compatibility. They won’t be
removed except if package raster is removed from CRAN. New
functions return either a Raster*, a
SpatRaster, or a stars object, according to
user preference.
grid_metrics() is replaced by
pixel_metrics()grid_terrain(), grid_canopy(),
grid_density() are replaced by
rasterize_terrain(), rasterize_canopy(),
rasterize_density()New functions are mostly convenient features that simplify some
workflow aspects without introducing a lot of brand new functionality
that did not already exist in lidR v3.
New geometry functions st_convex_hull() and
st_concave_hull() that return sfc
New modern functions st_area(),
st_bbox(), st_transform() and
st_crs() inherited from sf for
LAS* objects.
New convenient functions nrow(),
ncol(), dim(), names() inherited
from base for LAS* objects
New operators $, [[,
$<- and [[<- on LASheader.
The following are now valid statements:
r header[["Version Major"]] header[["Z scale factor"]] <- 0.001
Operators $, [[, $<-
and [[<- on LAS can now access the
LASheader metadata. The following are now valid statements:
r las[["Version Major"]] las[["Z scale factor"]] <- 0.001
RStudio now supports auto completion for operator $
in LAS objects. Yay!
New functions template_metrics(),
hexagon_metrics(), polygon_metrics() that
extend the concept of metrics further to any kind of template.
Functions that used to accept spatial vector or spatial raster as
input now consistently accept any of Spatial*,
sf, sfc, Raster*,
SpatRaster and stars objects. This include
merge_spatial(), normalize_intensity(),
normalize_height(), rasterize_*(),
segment_trees(), plot_dtm3d() and several
others. We plan to support SpatVector in future
releases.
Every function that supports a raster as input now accept an
“on-disk” raster from raster, terra and
stars i.e. a raster not loaded in memory. This includes
rasterization functions, individual tree segmentation functions,
merge_spatial and others, in particular
plot_dtm3d() and add_dtm3d() that now
downsample on-disk rasters on-the-fly to display very large DTMs.
On-disk rasters were already generally supported in previous versions
but not every function was properly optimized to handle such
objects.
All the functions that return a raster
(pixel_metrics() and rasterize_*()) are raster
agnostic and can return rasters from raster,
terra or stars. They have an argument
pkg = "raster|terra|stars" to choose. The default is
terra but this can be changed globally using:
r options(lidR.raster.default = "stars")
New function catalog_map() that simplifies
catalog_apply() to a large degree. Yet it is not as
versatile as catalog_apply() but well suits around 80% of
use cases. Applying a user-defined function to a collection of LAS files
is now as simple as:
r my_fun <- function(las, ...) { # do something with the point cloud return(something) } res <- catalog_map(ctg, my_fun, param1 = 2, param2 = 5)
Operator [ on LAS object has been
overloaded to clip a point-cloud using a bbox or a
sfc r sub <- las[sfc]
rasterize_terrain() accepts an sfc as
argument to force interpolation within a defined area.
normalize_height() now always interpolates all
points. It is no longer possible to get an error that some points cannot
be interpolated. The problem of interpolating the DTM where there is no
data is still present but we opted for a nearest neighbour approach with
a warning instead of a failure. This prevents the method from failing
after hours of computation for special cases somewhere in the file
collection. This also means we removed the na.rm option
that is no longer relevant.
New functions header(), payload(),
phb(), vlr(), evlr() to get the
corresponding data from a LAS object.
New algorithm shp_hline and shp_vline
for segment_shapes() #499
New algorithm mcc for ground
classification.
The bounding box of the CHM computed with
rastertize_canopy() or grid_canopy() is no
longer affected by the subcircle tweak. See #518.
readLAS() can now read two or more files that do not
have the same point format (see #508)
plot() for LAS gains arguments
pal, breaks and nbreaks similar
to sf. Arguments trim and
colorPalette are deprecated
itot from stdmetrics_i which
generates troubles (see #463 #514) is now
double instead of intclassify_*, rasterize_*,
*_metrics, segment_* and
normalize_* were grouped.