Summary

Summarize the dataset by counting the number of points, first returns and other metrics for the entire point cloud. It also produces an histogram of Z and Intensity attributes for the entiere point cloud. It can also compute some metrics for each file or chunk with the same metric engine than rasterize. This stage does not modify the point cloud. It produces a summary as a list.

Usage

summarise(zwbin = 2, iwbin = 50, metrics = NULL, filter = "")

Arguments

zwbin, iwbin

numeric. Width of the bins for the histograms of Z and Intensity.

metrics

Character vector. "min", "max" and "count" are accepted as well as many others (see metric_engine). If NULL nothing is computed. If something is provided these metrics are computed for each chunk loaded. A chunk might be a file but may also be a plot (see examples).

filter

the 'filter' argument allows filtering of the point-cloud to work with points of interest. For a given stage when a filter is applied, only the points that meet the criteria are processed. The most common strings are Classification == 2", "Z > 2", "Intensity < 100". For more details see filters.

Examples

f <- system.file("extdata", "Topography.las", package="lasR")
read <- reader()
pipeline <- read + summarise()
ans <- exec(pipeline, on = f)
ans
#> $npoints
#> [1] 73403
#> 
#> $nsingle
#> [1] 31294
#> 
#> $nwithheld
#> [1] 0
#> 
#> $nsynthetic
#> [1] 0
#> 
#> $npoints_per_return
#>     1     2     3     4     5     6 
#> 53538 15828  3569   451    16     1 
#> 
#> $npoints_per_class
#>     1     2     9 
#> 61347  8159  3897 
#> 
#> $z_histogram
#>   788   790   792   794   796   798   800   802   804   806   808   810   812 
#>     1   163   265   470   596   694  1610  4955  5510 13833  9974  9865  8076 
#>   814   816   818   820   822   824   826   828   830 
#>  6643  4682  2958  1715   830   390   146    26     1 
#> 
#> $i_histogram
#>   50  100  150  200  250  300  350  400  450  500  550  600  650  700  750  800 
#>   25  397 1041 1576 2842 2585 2249 2051 2341 2575 2301 2529 2483 2623 2505 2697 
#>  850  900  950 1000 1050 1100 1150 1200 1250 1300 1350 1400 1450 1500 1550 1600 
#> 2759 2865 2865 3242 3272 3556 3464 3231 2589 2472 3478 3747 1969  532  183  104 
#> 1650 1700 1750 1800 1850 1900 1950 2000 2050 2100 2150 2200 2250 2300 2350 2400 
#>   97   65   35   18    9   13    7    6    0    1    0    2    1    0    0    0 
#> 2450 
#>    1 
#> 
#> $crs
#> [1] "PROJCRS[\"NAD83(CSRS) / MTM zone 7\",BASEGEOGCRS[\"NAD83(CSRS)\",DATUM[\"NAD83 Canadian Spatial Reference System\",ELLIPSOID[\"GRS 1980\",6378137,298.257222101,LENGTHUNIT[\"metre\",1]]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"degree\",0.0174532925199433]],ID[\"EPSG\",4617]],CONVERSION[\"MTM zone 7\",METHOD[\"Transverse Mercator\",ID[\"EPSG\",9807]],PARAMETER[\"Latitude of natural origin\",0,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8801]],PARAMETER[\"Longitude of natural origin\",-70.5,ANGLEUNIT[\"degree\",0.0174532925199433],ID[\"EPSG\",8802]],PARAMETER[\"Scale factor at natural origin\",0.9999,SCALEUNIT[\"unity\",1],ID[\"EPSG\",8805]],PARAMETER[\"False easting\",304800,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8806]],PARAMETER[\"False northing\",0,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8807]]],CS[Cartesian,2],AXIS[\"easting (E(X))\",east,ORDER[1],LENGTHUNIT[\"metre\",1]],AXIS[\"northing (N(Y))\",north,ORDER[2],LENGTHUNIT[\"metre\",1]],USAGE[SCOPE[\"Engineering survey, topographic mapping.\"],AREA[\"Canada - Quebec - between 72°W and 69°W.\"],BBOX[45.01,-72,61.8,-69]],ID[\"EPSG\",2949]]"
#> 
#> $epsg
#> [1] 2949
#> 

# Compute metrics for each plot
read = reader_circles(c(273400, 273500), c(5274450, 5274550), 11.28)
metrics = summarise(metrics = c("z_mean", "z_p95", "i_median", "count"))
pipeline = read + metrics
ans = exec(pipeline, on = f)
ans$metrics
#>   count i_median   z_mean    z_p95
#> 1   291     1311 806.0330 807.2401
#> 2   185      731 804.0168 811.2172