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@Jean-Romain Jean-Romain released this 09 Jan 23:13
· 981 commits to master since this release
v2.2.0
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lidR v2.2.0 (Release date: 2020-01-06)

NEW FEATURES

  1. LAScatalog processing engine:

    • catalog_apply() gains an option automerge = TRUE. catalog_apply() used to return a list that had to be merged by the user. This new option allows for automatic merging. This is a fail-safe feature. In the worst case, if the user-defined function returns a non-supported list of objects that cannot be merged it falls back to the former behavior i.e. it returns a list. Thus there is no risk associated with adding the option automerge = TRUE but by defaut it is set to FALSE for retrocompatibility. This might be switched to TRUE in future releases.

    • opt_output_file() now interprets * as {ORIGINALFILENAME} for shorter syntax. The following is now accepted:

    opt_output_file(ctg) <- "/home/user/data/norm/*_norm"  # {*} is valid as well
# instead of
opt_output_file(ctg) <- "/home/user/data/norm/{ORIGINALFILENAME}_norm"
    • The engine now supports "alternative directories". This is a very specific and undocumented feature useful in a single case of remote computing. More details on the wiki page.
    ctg = readLAScatalog("~/folder/LASfiles/")
ctg@input_options$alt_dir = c("/home/Alice/data/", "/home/Bob/remote/project1/data/")
    • LAScatalog modification constraints are now relaxed. It is now possible to add or modify an attribute if this attribute has a name that is not reserved.
    ctg$newattr <- 1 # is now allowed
ctg$GUID <- TRUE # is still forbidden
#> Erreur : LAScatalog data read from standard files cannot be modified
    • The engine supports partial processing. It is possible to flag some files that will, or will not, be processed. These files are not removed from the LAScatalog. They are used to load a buffer, if required, for the files that are actually processed. To activate this option a new boolean attribute named processed can be added in the catalog.
    ctg$processed <- TRUE
ctg$processed[3:5] <- FALSE

  2. 3D rendering:

    • The argument colorPalette of the function plot() for LAS objects is now set to "auto" by default. This allows for this argument to not be specified even when plotting an attribute other than Z, and having an appropriate color palette by default. More interestingly, it will automatically apply a nice color scheme to the point cloud with the attribute 'Classification' following the ASPRS specifications. See #275.
    plot(las)
plot(las, color = "Intensity")
plot(las, color = "ReturnNumber")
plot(las, color = "Classification")
    • In plot.lasmetrics3d() the parameter trim is now set to Inf by default.

  3. New function point_metrics() - very similar to grid_metrics() but at the point level. The 'metrics' family is now complete. cloud_metrics() computes user-defined metrics at the point cloud level. grid_metrics() and hexbin_metrics() compute user-defined metrics at the pixel level. voxel_metrics computes user-defined metrics at the voxel level. point_metrics() computes user-defined metrics at the point level.

  4. lasnormalize():

    • Gains an argument use_class to control the points used as ground.
    • By default 'ground point' now includes points classified as water by default. This might be useful in regions with a lot of water because in this case lasnormalize() can take forever to run (see #295)).

  5. New function sensor_tracking() to retrieve the position of the sensor in the sky.

  6. New function lasrangecorrection() to normalize intensity using the sensor position (range correction)

  7. catalog_select now also allows files to process to be flagged interactively:

    ctg <- catalog_select(ctg, method = "flag_processed")
ctg <- catalog_select(ctg, method = "flag_unprocessed")

  8. grid_terrain()

    • Have a new argument use_class to control which points are considered as ground points
    • With a LAScatalog it now uses the filter -keep_class by default respecting the classes given in use_class.

CHANGES

  1. LAS() now rounds the values to 2 digits if no header is provided to fit with the default header automatically generated. This ensures that a perfectly valid LAS object is built out of external data. This change is made by reference, meaning that the original dataset is also rounded.

    pts <- data.frame(X = runif(10), Y = runif(10), Z = runif(10))
las <- LAS(pts) # 'las' contains rounded values but 'pts' as well to avoid data copying

  2. lasmetrics() is deprecated. All las* functions return LAS objects except lasmetrics(). For consistency across the package lasmetrics() becomes cloud_metrics().

  3. grid_metrics3d() and grid_hexametrics() are deprecated. They are renamed voxel_metrics() and hexbin_metrics() for naming consistency.

  4. The example dataset Topography.laz is now larger and include attributes gpstime, PointSourceID and some classified lakes.

ENHANCEMENTS

  1. Internally the package used a QuadTree as spatial index in versions <= 2.1.3. Spatial index has been rewritten and changed for a grid partition which is twice as fast as the former QuadTree. This change provides a significant boost (i.e. up to two times faster) to many algorithms of the package that rely on a spatial index. This includes lmf(), shp_*(), wing2015(), pmf(), lassmooth(), tin(), pitfree(). Benchmark on a Intel Core i7-5600U CPU @ 2.60GHz × 2.

    # 1 x 1 km, 13 pts/m², 13.1 million points
set_lidr_threads(n)
tree_detection(las, lmf(3))
#> v2.1: 1 core: 80s - 4 cores: 38s
#> v2.2: 1 core: 38s - 4 cores: 20s

# 500 x 500 m, 12 pt/m², 3.2 million points
lassnags(las, wing2015(neigh_radii = nr, BBPRthrsh_mat = bbpr_th))
#> v2.1: 1 core: 66s - 4 cores: 33s
#> v2.2: 1 core: 43s - 4 cores: 21s

# 250 x 250 m, 12 pt/m², 717.6 thousand points
lasdetectshape(las3, shp_plane())
#> v2.1 - 1 cores: 12s - 4 cores: 7s
#> v2.2 - 1 cores:  6s - 4 cores: 3s

  2. Internally the Delaunay triangulation has been rewritten with boost instead of relying on the geometry package. The Delaunay triangulation and the rasterization of the Delaunay triangulation are now written in C++ providing an important speed-up (up to three times faster) to tin(), dsmtin() and pitfree(). However, for this to work, the point cloud must be converted to integers. This implies that the scale factors and offset in the header must be properly populated, which might not be the case if users have modified these values manually or if using a point cloud coming from a format other than las/laz. Benchmark on an Intel Core i7-5600U CPU @ 2.60GHz × 2.

    # 1.7 million ground points
set_lidr_threads(n)
grid_terrain(las, 0.5, tin())
#> v2.1: 1 core: 48s - 4 cores: 37s
#> v2.2: 1 core: 22s - 4 cores: 20s

# 560 thousand first returns (1.6 pts/m²)
grid_canopy(las, res = 0.5, dsmtin())
#> v2.1: 1 core: 8s - 4 cores: 7s
#> v2.2: 1 core: 3s - 4 cores: 3s

# 560 thousand first returns (1.6 pts/m²)
grid_canopy(las, res = 0.5, pitfree(c(0,2,5,10,15), c(0, 1.5)))
#> v2.1: 1 core: 30s - 4 cores: 28s
#> v2.2: 1 core: 11s - 4 cores: 9s

  3. There are more than 100 new unit tests in testthat. The coverage increased from 68 to 87%.

  4. The vignette named Speed-up the computations on a LAScatalog gains a section about the possible additional speed-up using the argument select from readLAS().

  5. The vignette named LAScatalog formal class gains a section about partial processing.

  6. Harmonization and review of the sections 'Supported processing options' in the man pages.

FIXES

  1. Several minor fixes in lascheck() for very improbable cases of LAS objects likely to have been modified manually.

  2. Fix colorization of boolean data when plotting an object of class lasmetrics3d (returned by voxel_metrics()) #289

  3. The LAScatalog engine now calls raster::writeRaster() with NAflag = -999999 because it seems that the default -Inf generates a lot of trouble on windows when building a virtual raster mosaic with gdalUtils::gdalbuildvrt().

  4. plot.LAS() better handles the case when coloring with an attribute that has only two values: NA and one other value.

  5. lasclip() was not actually able to retrieve the attributes of the Spatial*DataFrame or sf equivalent when using opt_output_file(ctg) <- "/dir/{PLOTID}".

  6. lasmergespatial() supports 'on disk' rasters #285 #306

  7. opt_stop_early() was not actually working as expected. The processing was aborted without logs. It now prevent the catalog processing engine to stop
    even when an error occurs.

  8. In tree_detection() if no tree is found (e.g. in a lake) the function crashed. It now returns an empty SpatialPointDataFrame.

  9. The argument keep_lowest in grid_terrain returned dummy output full of NAs because NAs have the precedence on actual numbers.

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