Move results from Google Cloud Storage to a local directory

Source: R/ee_download.R

Move results of an EE task saved in Google Cloud Storage to a local directory.

ee_gcs_to_local(
  task,
  dsn,
  public = FALSE,
  metadata = FALSE,
  overwrite = TRUE,
  quiet = FALSE
)

Arguments

task

List generated after finished an EE task correctly. See details.

dsn

Character. Output filename. If missing, a temporary file (i.e. tempfile()) is assigned.

public

Logical. If TRUE, a public link to Google Cloud Storage resource is created.

metadata

Logical. If TRUE, export the metadata related to the Google Cloud Storage resource. See details.

overwrite

A boolean argument that indicates indicating whether "filename" should be overwritten. By default TRUE.

quiet

Logical. Suppress info message

Value

If metadata is FALSE, will return the filename of the Google Cloud Storage resource on their system. Otherwise, a list with two elements (dns and metadata) is returned.

Details

The task argument needs "COMPLETED" task state to work due to that the parameters necessaries to locate the file into Google Cloud Storage are obtained from
ee$batch$Export$*$toCloudStorage(...)$start()$status().

If the argument metadata is TRUE, a list with the following elements is exported join with the output filename (dsn):

  • ee_id: Name of the Earth Engine task.

  • gcs_name: Name of the Table in Google Cloud Storage.

  • gcs_bucket: Name of the bucket.

  • gcs_fileFormat: Format of the table.

  • gcs_public_link: Download link to the table.

  • gcs_URI: gs:// link to the table.

See also

Other generic download functions: ee_drive_to_local()

Examples

if (FALSE) {
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()


# Get the mean annual NDVI for 2011
cloudMaskL457 <- function(image) {
  qa <- image$select("pixel_qa")
  cloud <- qa$bitwiseAnd(32L)$
    And(qa$bitwiseAnd(128L))$
    Or(qa$bitwiseAnd(8L))
  mask2 <- image$mask()$reduce(ee$Reducer$min())
  image <- image$updateMask(cloud$Not())$updateMask(mask2)
  image$normalizedDifference(list("B4", "B3"))
}

ic_l5 <- ee$ImageCollection("LANDSAT/LT05/C01/T1_SR")$
  filterBounds(ee$FeatureCollection(ee_ROI))$
  filterDate("2011-01-01", "2011-12-31")$
  map(cloudMaskL457)

# Create simple composite
mean_l5 <- ic_l5$mean()$rename("NDVI")
mean_l5 <- mean_l5$reproject(crs = "EPSG:4326", scale = 500)
mean_l5_Amarakaeri <- mean_l5$clip(ee_ROI)

# Move results from Earth Engine to Drive
task_img <- ee_image_to_gcs(
   image = mean_l5_Amarakaeri,
   bucket = "rgee_dev",
   fileFormat = "GEO_TIFF",
   region = ee_ROI,
   fileNamePrefix = "my_image_demo"
)

task_img$start()
ee_monitoring(task_img)

# Move results from Drive to local
img <- ee_gcs_to_local(task = task_img)
}