lidR (version 4.1.2)

classify: Classify points

Description

Classify points that meet some criterion and/or that belong in a region of interest. The functions updates the attribute Classification of the LAS object according to las specifications

Usage

classify_ground(las, algorithm, last_returns = TRUE)
classify_noise(las, algorithm)
classify_poi(
  las,
  class,
  poi = NULL,
  roi = NULL,
  inverse_roi = FALSE,
  by_reference = FALSE
)

Arguments

las

An object of class LAS or LAScatalog.

algorithm

An algorithm for classification. lidR has has: sor, ivf for noise classification, and pmf, csf, mcc for ground classification (see respective documentation).

last_returns

logical. The algorithm will use only the last returns (including the first returns in cases of a single return) to run the algorithm. If FALSE all the returns are used. If the attributes 'ReturnNumber' or 'NumberOfReturns' are absent, 'last_returns' is turned to FALSE automatically.

class

The ASPRS class to attribute to the points that meet the criterion.

poi

a formula of logical predicates. The points that are TRUE will be classified class.

roi

A SpatialPolygons*, from sp or a sf/sfc_POLYGON from sf. The points that are in the region of interest delimited by the polygon(s) are classified class.

inverse_roi

bool. Inverses the roi. The points that are outside the polygon(s) are classified class.

by_reference

bool. Updates the classification in place (LAS only).

Non-supported LAScatalog options

The option select is not supported and not respected because it always preserves the file format and all the attributes. select = "*" is imposed internally.

Details

classify_noise

Classify points as 'noise' (outliers) with several possible algorithms. lidR has: sor, ivf. The points classified as 'noise' are assigned a value of 18.

classify_ground

Classify points as 'ground' with several possible algorithms. lidR has pmf, csf and mcc. The points classified as 'ground' are assigned a value of 2

classify_poi

Classify points that meet some logical criterion and/or that belong in a region of interest with class of choice.

Examples

# ===============
# Classify ground
# ===============

if (require(RCSF, quietly = TRUE))
{
LASfile <- system.file("extdata", "Topography.laz", package="lidR")
las <- readLAS(LASfile, select = "xyzrn", filter = "-inside 273450 5274350 273550 5274450")

# (Parameters chosen mainly for speed)
mycsf <- csf(TRUE, 1, 1, time_step = 1)
las <- classify_ground(las, mycsf)
#plot(las, color = "Classification")
}

# ===============
# Classify noise
# ===============

LASfile <- system.file("extdata", "Topography.laz", package="lidR")
las <- readLAS(LASfile, filter = "-inside 273450 5274350 273550 5274450")

# Add 20 artificial outliers
set.seed(314)
id = round(runif(20, 0, npoints(las)))
set.seed(42)
err = runif(20, -50, 50)
las$Z[id] = las$Z[id] + err

# Using IVF
las <- classify_noise(las, ivf(5,2))
#plot(las, color = "Classification")

# Remove outliers using filter_poi()
las_denoise <- filter_poi(las, Classification != LASNOISE)

# ===============
# Classify POI
# ===============

LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
shp <- system.file("extdata", "lake_polygons_UTM17.shp", package = "lidR")

las  <- readLAS(LASfile, filter = "-keep_random_fraction 0.1")
lake <- sf::st_read(shp, quiet = TRUE)

# Classifies the points that are NOT in the lake and that are NOT ground points as class 5
poi <- ~Classification != LASGROUND
las <- classify_poi(las, LASHIGHVEGETATION, poi = poi, roi = lake, inverse = TRUE)

# Classifies the points that are in the lake as class 9
las <- classify_poi(las, LASWATER, roi = lake, inverse = FALSE)

#plot(las, color = "Classification")