gdalcubes (version 0.7.1)

predict.cube: Model prediction

Description

Apply a trained model on all pixels of a data cube.

Usage

# S3 method for cube
predict(object, model, ..., output_names = c("pred"), keep_bands = FALSE)

Arguments

object

a data cube proxy object (class cube)

model

model used for prediction (e.g. from caret or tidymodels)

...

further arguments passed to the model-specific predict method

output_names

optional character vector for output variable(s)

keep_bands

logical; keep bands of input data cube, defaults to FALSE, i.e. original bands will be dropped

Details

The model-specific predict method will be automatically chosen based on the class of the provided model. It aims at supporting models from the packages tidymodels, caret, and simple models as from lm or glm.

For multiple output variables or output in form of lists or data.frames, output_names must be provided and match names of the columns / items of the result object returned from the underlying predict method. For example, predictions using tidymodels return a tibble (data.frame) with columns like .pred_class (classification case). This must be explicitly provided as output_names. Similarly, predict.lm and the like return lists if the standard error is requested by the user and output_names hence should be set to c("fit","se.fit").

For more complex cases or when predict expects something else than a data.frame, this function may not work at all.

Examples

# \donttest{
# create image collection from example Landsat data only
# if not already done in other examples
if (!file.exists(file.path(tempdir(), "L8.db"))) {
  L8_files <- list.files(system.file("L8NY18", package = "gdalcubes"),
                         ".TIF", recursive = TRUE, full.names = TRUE)
  create_image_collection(L8_files, "L8_L1TP", file.path(tempdir(), "L8.db"), quiet = TRUE)
}

v = cube_view(extent=list(left=388941.2, right=766552.4,
                          bottom=4345299, top=4744931, t0="2018-04", t1="2018-06"),
              srs="EPSG:32618", nx = 497, ny=526, dt="P3M")

L8.col = image_collection(file.path(tempdir(), "L8.db"))

x = sf::st_read(system.file("ny_samples.gpkg", package = "gdalcubes"))

raster_cube(L8.col, v) |>
  select_bands(c("B02","B03","B04","B05")) |>
  extract_geom(x) -> train

x$FID = rownames(x)  
train = merge(train, x, by = "FID")
train$iswater = as.factor(train$class == "water")

log_model <- glm(iswater ~ B02 + B03 + B04 + B05, data = train, family = "binomial")

raster_cube(L8.col, v) |>
  select_bands(c("B02","B03","B04","B05")) |>
  predict(model=log_model, type="response") |>
  plot(key.pos=1)
# }