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Fix: readLAScatalog()
was not working if package raster
was not installed.
Fix: regression of the stars
package makes rasterize_terrain()
extremely slow and blow up the RAM memory
New: catalog_intersects()
support a SpatExtent
Fix: lidR
can fully works without raster
ans sp
Fix: #742
Fix: local maximum filter lmf()
with a fixed windows is now 20 times faster.
point_eigenvalues
gained an argument coeff
to return the principal component coefficientspitfill_stonge2008()
. See references.readLAScatalog
can read a virtual point cloud file (.vpc)Following the retirement of rgdal
and sp
we removed the dependence to sp
and the strong dependence to raster
:
bbox
inherited from sp
raster
is now only suggested and lidR
no longer depends on it. extent
was removed in consequence of (3) because it was inherited from raster
and returned an object Extent
from raster
.crs
, crs<-
, projection
, projection<-
, wkt
and area
inherited from raster
are now generic. This may create clash with the raster
package but anyway raster
should no longer be used.silva2016
can load (not too big) ondisk chm on the fly.rasterize_terrain()
works in parallel with res = <SpatRaster>
.plot(las, mapview = TRUE)
.plot(las, breaks = <vector>)
now works with a vector of custom break points.crown_metrics
now always remove invalid polygons if any.readMSLAS
can now be written properly with writeLAS
.readMSLAS
accepts only two files.readMSLAS
converts ScanAngleRank
to ScanAngle
.add_lasnir()
.rgl::rgl.*
by rgl::*3d
functions #651readLAS
no longer checks for attribute names validity as they are necessarily correct #659plot_metrics()
no longer fails with a single plot #664unormalize_height()
removes extra_bytes in VLR.print(las)
works even when the CRS is not recognized by sf
.dsmtin
and pitfree
gain an argument highest
. This option was enabled by default in previous releases. There is now an option to disable it.normalize_height()
and segment_trees
work in parallel with SpatRaster
.crown_metrics()
now triggers a warning when invalid geometries are created and delineate_crowns()
remove these geometries before to convert to sp
.crown_metrics()
now works with func = NULL
and a LAScatalog
.*_metrics()
functions always returned NA
s for lastofmany
.dalponte2016
doc updated to use terra
.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 decompression
rgdal
and rgeos
will be retired on Jan 1st 2024. see twitter (https://twitter.com/RogerBivand/status/1407705212538822656), 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.
r
library(lidR)
projection(las) # works
library(raster)
projection(las) # no longer works
Serialized 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 int
classify_*
, rasterize_*
, *_metrics
, segment_*
and normalize_*
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