R/ee_download.R
ee_gcs_to_local.Rd
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
)
List generated after finished an EE task correctly. See details.
Character. Output filename. If missing, a temporary
file (i.e. tempfile()
) is assigned.
Logical. If TRUE, a public link to Google Cloud Storage resource is created.
Logical. If TRUE, export the metadata related to the Google Cloud Storage resource. See details.
A boolean argument that indicates indicating whether "filename" should be overwritten. By default TRUE.
Logical. Suppress info message
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.
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.
Other generic download functions:
ee_drive_to_local()
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)
}