ee_table_to_gcs {rgee} | R Documentation |
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 |
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
## Not run:
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)
## End(Not run)