rgee (version 1.0.7)

ee_gcs_to_local: Move results from Google Cloud Storage to a local directory

Description

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

Usage

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

Arguments

task

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

dsn

Character. Output filename. If missing, a temporary file will be assigned.

public

Logical. If TRUE, a public link to the image will be created.

metadata

Logical. If TRUE, export the metadata related to the image.

overwrite

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

quiet

Logical. Suppress info message

Value

filename character vector.

Details

The task argument needs "COMPLETED" task state to work, since the parameters necessaries to locate the file into google cloud storage are obtained from ee$batch$Export$*$toCloudStorage(...)$start()$status().

See Also

Other generic download functions: ee_drive_to_local()

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()


# 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)
# }