View source: R/sits_get_probs.R
sits_get_probs | R Documentation |
Given a set of lat/long locations and a probability cube, retrieve the prob values of each point. This function is useful to estimate probability distributions and to assess the differences between classifiers.
sits_get_probs(cube, samples, window_size = NULL)
## S3 method for class 'csv'
sits_get_probs(cube, samples, window_size = NULL)
## S3 method for class 'shp'
sits_get_probs(cube, samples, window_size = NULL)
## S3 method for class 'sf'
sits_get_probs(cube, samples, window_size = NULL)
## S3 method for class 'sits'
sits_get_probs(cube, samples, window_size = NULL)
## S3 method for class 'data.frame'
sits_get_probs(cube, samples, window_size = NULL)
## Default S3 method:
sits_get_probs(cube, samples, window_size = NULL)
cube |
Probability data cube. |
samples |
Location of the samples to be retrieved. Either a tibble of class "sits", an "sf" object with POINT geometry, the location of a POINT shapefile, the location of csv file with columns "longitude" and "latitude", or a data.frame with columns "longitude" and "latitude" |
window_size |
Size of window around pixel (optional) |
A tibble of with columns <longitude, latitude, values> in case no windows are requested and <longitude, latitude, neighbors> in case windows are requested
There are four ways of specifying data to be retrieved using the
samples
parameter:
CSV: a CSV file with columns longitude
, latitude
.
SHP: a shapefile in POINT geometry.
sf object: An link[sf]{sf}
object with POINT geometry.
sits object: A valid tibble with sits
timeseries.
data.frame: A data.frame with longitude
and latitude
.
Gilberto Camara, gilberto.camara@inpe.br
if (sits_run_examples()) {
# create a random forest model
rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
# create a data cube from local files
data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
cube <- sits_cube(
source = "BDC",
collection = "MOD13Q1-6.1",
data_dir = data_dir
)
# classify a data cube
probs_cube <- sits_classify(
data = cube, ml_model = rfor_model, output_dir = tempdir()
)
# obtain the a set of points for sampling
ground_truth <- system.file("extdata/samples/samples_sinop_crop.csv",
package = "sits"
)
# get the classification values for a selected set of locations
probs_samples <- sits_get_probs(probs_cube, ground_truth)
}
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