rgee (version 0.5.0)

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

Description

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

Usage

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

Human-readable 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

Add current date and time as a prefix to files to export.

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.

Examples

# NOT RUN {
library(rgee)
library(stars)
library(sf)

ee_users()
ee_reattach() # reattach ee as a reserved word
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$geometry(), list(name = "Amarakaeri")))
)

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