[R-sig-Geo] Spatial layers for Europe at 30-m available as Cloud Optimized GeoTiffs (corrected)

Mon Mar 15 16:40:56 CET 2021

Hi Tom, great work!

When reading this either with terra or stars, as in

in.tif = 
"/vsicurl/http://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_landcover.hcl_lucas.corine.rf_p_30m_0..0cm_2019_eumap_epsg3035_v0.1.tif"

library(terra)
# terra version 1.1.5
plot(rast(in.tif))

#library(raster)
#plot(raster(in.tif))

library(stars)
# Loading required package: abind
# Loading required package: sf
# Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1
plot(read_stars(in.tif))

I see that both plots show a continuous raster, not a categorical. We 
spent quite a bit of time recently in stars & tmap development to get 
handling and plotting of categorical rasters right (including those 
having a color table), but this file doesn't give proper access to the 
categories.

The metadata tags do have them:

stars::gdal_metadata(in.tif)
#  [1] "1=111 - Urban fabric"
#  [2] "10=212 - Permanently irrigated arable land"
#  [3] "11=213 - Rice fields"
#  [4] "12=221 - Vineyards"
#  [5] "13=222 - Fruit trees and berry plantations"
#  [6] "14=223 - Olive groves"
#  [7] "15=231 - Pastures"
#  [8] "16=311 - Broad-leaved forest"
#  [9] "17=312 - Coniferous forest"
# [10] "18=321 - Natural grasslands"
# [11] "19=322 - Moors and heathland"
# [12] "2=122 - Road and rail networks and associated land"
# [13] "20=323 - Sclerophyllous vegetation"
# [14] "21=324 - Transitional woodland-shrub"
# [15] "22=331 - Beaches, dunes, sands"
# [16] "23=332 - Bare rocks"
# [17] "24=333 - Sparsely vegetated areas"
# [18] "25=334 - Burnt areas"
# [19] "26=335 - Glaciers and perpetual snow"
# [20] "27=411 - Inland wetlands"
# [21] "28=421 - Maritime wetlands"
# [22] "29=511 - Water courses"
# [23] "3=123 - Port areas"
# [24] "30=512 - Water bodies"
# [25] "31=521 - Coastal lagoons"
# [26] "32=522 - Estuaries"
# [27] "33=523 - Sea and ocean"
# [28] "4=124 - Airports"
# [29] "5=131 - Mineral extraction sites"
# [30] "6=132 - Dump sites"
# [31] "7=133 - Construction sites"
# [32] "8=141 - Green urban areas"
# [33] "9=211 - Non-irrigated arable land"
# [34] "AREA_OR_POINT=Area"

but GDAL doesn't give access to those in a programmatic way. I've tried 
to add a .aux.xml file with the table, this worked locally (for both 
stars - after using droplevels() - and terra) and might as well work 
over the /vsicurl connection. File attached.

Many regards,

On 02/03/2021 18:30, Tomislav Hengl wrote:
> 
> We have mapped land cover classes for the 2000-2019 period for 
> continental Europe at 30-m resolution using spatiotemporal Machine
> Learning (we used R and python for modeling). Explore the dynamic EU 
> landscapes on your palm using the ODS-Europe viewer: 
> https://maps.opendatascience.eu
> 
> To access almost 10TB of data using R you use the terra or similar
> packages e.g.:
> 
> R> library(terra)
> R> in.tif = 
> "/vsicurl/http://s3.eu-central-1.wasabisys.com/eumap/lcv/lcv_landcover.hcl_lucas.corine.rf_p_30m_0..0cm_2019_eumap_epsg3035_v0.1.tif" 
> 
> R> tif = rast(in.tif)
> 
>  From here you can use any native operation e.g. to crop some polygon
> or resample / aggregate values (there is no need to download whole
> data sets). A detailed tutorial on how to work with Cloud Optimized
> GeoTiffs is available here: 
> https://gitlab.com/openlandmap/global-layers/-/blob/master/tutorial/OpenLandMap_COG_tutorial.md. 
> 
> 
> Complete list of Cloud Optimized GeoTiffs we produced so far for Europe
> is available here: 
> https://gitlab.com/geoharmonizer_inea/eumap/-/blob/master/gh_raster_layers.csv 
> 
> 
> If not otherwise specified, the data available on this portal is 
> licensed under the Open Data Commons Open Database License 
> <https://opendatacommons.org/licenses/odbl/> (ODbL) and/or Creative
> Commons Attribution-ShareAlike 4.0 
> <https://creativecommons.org/licenses/by-sa/4.0/legalcode> and/or 
> Creative Commons Attribution 4.0 
> <https://creativecommons.org/licenses/by/4.0/legalcode> International
> license (CC BY).
> 
> Read more in: 
> https://opengeohub.medium.com/europe-from-above-space-time-machine-learning-reveals-our-changing-environment-1b05cb7be520 
> 
> 
> If you experience any technical problems or if you discover a bug,
> please report via: 
> https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues
> 
> T. Hengl
> https://opengeohub.org/about
> 
> _______________________________________________
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> R-sig-Geo using r-project.org
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-- 
Edzer Pebesma
Institute for Geoinformatics
Heisenbergstrasse 2, 48151 Muenster, Germany
Phone: +49 251 8333081

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