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lidR v4.1.2 (Release date: )

lidR v4.1.1 (Release date: 2024-02-03)

lidR v4.1.0 (Release date: 2024-01-31)

NEW features

  1. New: point_eigenvalues gained an argument coeff to return the principal component coefficients
  2. New function pitfill_stonge2008(). See references.
  3. New readLAScatalog can read a virtual point cloud file (.vpc)

Backward incompatibilities

Following the retirement of rgdal and sp we removed the dependence to sp and the strong dependence to raster:

  1. Change: remove function bbox inherited from sp
  2. Change: package raster is now only suggested and lidR no longer depends on it.
  3. Change: the function extent was removed in consequence of (3) because it was inherited from raster and returned an object Extent from raster.
  4. Change: functions 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.

Fixes

lidR v4.0.4 (Release date: 2023-09-07)

lidR v4.0.3 (Release date: 2023-03-09)

lidR v4.0.2 (Release date: 2022-11-28)

lidR v4.0.1 (Release date: 2022-05-03)

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.

  1. 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().

  2. 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.

  3. 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.

  4. 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.

  5. 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

lidR v4.0.0 (Release date: 2022-02-17)

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.

Backward inconpatibilites

  1. 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)

  2. 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

  3. 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

  4. 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"])

  5. 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 works

  6. 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)

  7. 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.

New modern functions

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.

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.

New features

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.

  1. New geometry functions st_convex_hull() and st_concave_hull() that return sfc

  2. New modern functions st_area(), st_bbox(), st_transform() and st_crs() inherited from sf for LAS* objects.

  3. New convenient functions nrow(), ncol(), dim(), names() inherited from base for LAS* objects

  4. New operators $, [[, $<- and [[<- on LASheader. The following are now valid statements: r header[["Version Major"]] header[["Z scale factor"]] <- 0.001

  5. 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

  6. RStudio now supports auto completion for operator $ in LAS objects. Yay!

  7. New functions template_metrics(), hexagon_metrics(), polygon_metrics() that extend the concept of metrics further to any kind of template.

  8. 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.

  9. 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.

  10. 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")

  11. 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)

  12. Operator [ on LAS object has been overloaded to clip a point-cloud using a bbox or a sfc r sub <- las[sfc]

  13. rasterize_terrain() accepts an sfc as argument to force interpolation within a defined area.

  14. 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.

  15. New functions header(), payload(), phb(), vlr(), evlr() to get the corresponding data from a LAS object.

  16. New algorithm shp_hline and shp_vline for segment_shapes() #499

  17. New algorithm mcc for ground classification.

Enhancement

  1. The bounding box of the CHM computed with rastertize_canopy() or grid_canopy() is no longer affected by the subcircle tweak. See #518.

  2. readLAS() can now read two or more files that do not have the same point format (see #508)

  3. plot() for LAS gains arguments pal, breaks and nbreaks similar to sf. Arguments trim and colorPalette are deprecated

Fix

  1. The metric itot from stdmetrics_i which generates troubles (see #463 #514) is now double instead of int

Documentation