Creates a task to export a FeatureCollection to Google Cloud Storage.

Source: R/ee_download.R

Creates a task to export a FeatureCollection to Google Cloud Storage. This function is a wrapper around ee$batch$Export$table$toCloudStorage(...).

ee_table_to_gcs(
  collection,
  description = "myExportTableTask",
  bucket = NULL,
  fileNamePrefix = NULL,
  timePrefix = TRUE,
  fileFormat = NULL,
  selectors = NULL
)

Arguments

collection

The feature collection to be exported.

description

User-friendly name of the task.

bucket

The name of a Cloud Storage bucket for the export.

fileNamePrefix

Cloud Storage object name prefix for the export. Defaults to the name of the task.

timePrefix

Prefixes the current date and time to the exported files.

fileFormat

The output format: "CSV" (default), "GeoJSON", "KML", "KMZ", "SHP", or "TFRecord".

selectors

The list of properties to include in the output, as a list of strings or a comma-separated string. By default, all properties are included. **kwargs: Holds other keyword arguments that may have been deprecated such as 'outputBucket'.

Value

An unstarted Task that exports the table to Google Cloud Storage.

See also

Other vector export task creator: ee_table_to_asset(), ee_table_to_drive()

Examples

if (FALSE) { # \dontrun{
library(rgee)
library(stars)
library(sf)

ee_users()
ee_Initialize(gcs = TRUE)

# Define study area (local -> earth engine)
# Communal Reserve Amarakaeri - Peru
rlist <- list(xmin = -71.13, xmax = -70.95,ymin = -12.89, ymax = -12.73)
ROI <- c(rlist$xmin, rlist$ymin,
         rlist$xmax, rlist$ymin,
         rlist$xmax, rlist$ymax,
         rlist$xmin, rlist$ymax,
         rlist$xmin, rlist$ymin)
ee_ROI <- matrix(ROI, ncol = 2, byrow = TRUE) %>%
  list() %>%
  st_polygon() %>%
  st_sfc() %>%
  st_set_crs(4326) %>%
  sf_as_ee()

amk_fc <- ee$FeatureCollection(
  list(ee$Feature(ee_ROI, list(name = "Amarakaeri")))
)

task_vector <- ee_table_to_gcs(
  collection = amk_fc,
  bucket = "rgee_dev",
  fileFormat = "SHP",
  fileNamePrefix = "geom_Amarakaeri"
)
task_vector$start()
ee_monitoring(task_vector) # optional
amk_geom <- ee_gcs_to_local(task = task_vector)
plot(sf::read_sf(amk_geom[3]), border = "red", lwd = 10)
} # }