Q: Reclassify very high resolution raster with R/GDAL.
I am currently processing a new crop type map for Europe, which can be downloaded from here: https://data.jrc.ec.europa.eu/dataset/15f86c84-eae1-4723-8e00-c1b35c8f56b9 (~ 6GB). The map depicts the locations of 19 crops at a resolution of 10x10m. I want to convert it into 19 maps, where each pixel shows the share of a specific crop on a 250x250 grid. I am looking for a workable solution that does this for one crop. With this available I can create a function and loop it over all crops with lapply.
…the dataset is to large for a inline processing with R.
This is an interesting but very large data set. I’m afraid you have to partition the data set in some way. If I try to access the raster in a “sequential” table form, I get the following message:
as.data.frame(rst.large)
Error: cannot allocate vector of size 654.7 GbBased on your country approach I’ve written a small R-Sketch with a
moderate runtime behavior. For Luxembourg the calculation needs about
3 minutes, to aggregate (sum) a set of crops in 250x250 m cells.
You can run this over the complete EU and could use a different
partition polygons for larger countries. Aftermath the several country
results could be merged with the gdal_merge.py tool.
You will find the sketch in ./mk-crop-class.R.
All rights reserved by (C) 2021 A. Weidauer <alex.weidauer@huckfinn.de> 2021, is licensed by the 3-clause BSD license.