I am trying to process some raster layers from Global Forest Change (https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.4.html, data produced by Hansen et al., 2013). However, even though I am working in a 32 Gb RAM memory station, everything goes extremely slow.

I am aware that the data I am working with is heavy. Each raster has 40000x40000 raster cells and, while some of rasters only weight some 20 Mb, others go as high as 600 Mb.

Then, procedures such as reclassify() and aggregate() take as long as ~20-30 minutes for the 20 Mb layers. Taking into account that I need to download and process some 2x500 tiles... it is going to take ages.

Is there an efficient way to deal with this kind of data? Is there something more efficient than the raster library (which is awesome, by the way) for this particular workflow?

The rasters are downloaded as separate tiles that form a larger grid spanning the whole world, and there is a separate link for each block (e.g.: the first one is https://storage.googleapis.com/earthenginepartners-hansen/GFC-2016-v1.4/Hansen_GFC-2016-v1.4_treecover2000_00N_000E.tif). This is the part of my code that processes each tile:


down_links <- read.table("https://storage.googleapis.com/earthenginepartners-hansen/GFC-2016-v1.4/treecover2000.txt")

x <- 1
temp_tc <- tempfile()
download.file(as.character(down_links[x,]), destfile = temp_tc) # down_links is a table with the links mentioned above)
tc <- raster(file.path(temp_tc))

# Reclassify raster
tc2 <- tc
matrix <- structure(list(from = c(0L, 20L), to = c(20L, 100L), becomes = 0:1), .Names = c("from", 
"to", "becomes"), class = "data.frame", row.names = c(NA, -2L
tc2 <- reclassify(tc2, matrix)

# Calculate area
layer_area <- area(tc2)
tc3 <- tc2 * layer_area

# Aggregate raster to a larger pixel size
tc4 <- aggregate(tc3, fact = 32, fun = sum)
  • I don't use r so I won't make an answer, but I do the same thing--download global rasters (30m resolution) from GEE then manipulate them. There's no magic bullet here, I found. You just have to process them in tiles. Read about blocksize for optimizing I/O. Other alternative is to upgrade hardware. I did both (well, got access to supercomputer) and now I can crank through a global dataset in a matter of a couple hours, depending on what I'm doing to it.
    – Jon
    Feb 5, 2018 at 16:15
  • 2
    You could try something like the velox package but, processing 1.6 billion cell is going to take time. You could also write your own function that takes advantage of multithreading. Keep in mind that 32GB is a trivial amount of RAM for processing a problem this large, you should be thinking in terms of 256GM. I was once told that you should spend as much time analyzing your data as collecting it. In these days of large data this is even more relevant. This type of processing use to not even be remotely possible and now that it is one should not expect immediate gratification. Feb 5, 2018 at 16:16
  • 1
    I would also suggest that you do as much processing on GEE as possible. Can you not reclassify, compute areas, and downsample on their platform?
    – Jon
    Feb 5, 2018 at 16:19
  • Thanks @Jon for the GEE advice, I should give it a try soon. I thought I might be able to do it in R, which I already know. But, at the end, there is always need to learn new things! Feb 5, 2018 at 17:55
  • 1
    Note that your 20-600MB rasters are compressed. When you read them into memory, they are uncompressed to about 12GB (40000 * 40000 * 8bits) / 1024^3. No wonder that it's slow.
    – user2856
    Feb 6, 2018 at 9:53

1 Answer 1


One approach that helps to prevent overloading your RAM when working with large files with the raster package is to write your transformed rasters to file ('writeRaster()' function) and then read them back into the workspace ('raster("path")'). So for example, where you have assigned tc2, tc3 and tc4, those entire objects are only held in memory, whereas when read from file, only the data structure is read, while all of the cell values are not held in memory but are just looked up as needed.

However, applying functions like 'aggregate()' etc may still be slow on these objects!

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