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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Heisenbergstrasse 2, 48151 Muenster, Germany
Phone: +49 251 8333081
<PAMDataset>
<Metadata>
<MDI key="1">111 - Urban fabric</MDI>
<MDI key="10">212 - Permanently irrigated arable land</MDI>
<MDI key="11">213 - Rice fields</MDI>
<MDI key="12">221 - Vineyards</MDI>
<MDI key="13">222 - Fruit trees and berry plantations</MDI>
<MDI key="14">223 - Olive groves</MDI>
<MDI key="15">231 - Pastures</MDI>
<MDI key="16">311 - Broad-leaved forest</MDI>
<MDI key="17">312 - Coniferous forest</MDI>
<MDI key="18">321 - Natural grasslands</MDI>
<MDI key="19">322 - Moors and heathland</MDI>
<MDI key="2">122 - Road and rail networks and associated land</MDI>
<MDI key="20">323 - Sclerophyllous vegetation</MDI>
<MDI key="21">324 - Transitional woodland-shrub</MDI>
<MDI key="22">331 - Beaches, dunes, sands</MDI>
<MDI key="23">332 - Bare rocks</MDI>
<MDI key="24">333 - Sparsely vegetated areas</MDI>
<MDI key="25">334 - Burnt areas</MDI>
<MDI key="26">335 - Glaciers and perpetual snow</MDI>
<MDI key="27">411 - Inland wetlands</MDI>
<MDI key="28">421 - Maritime wetlands</MDI>
<MDI key="29">511 - Water courses</MDI>
<MDI key="3">123 - Port areas</MDI>
<MDI key="30">512 - Water bodies</MDI>
<MDI key="31">521 - Coastal lagoons</MDI>
<MDI key="32">522 - Estuaries</MDI>
<MDI key="33">523 - Sea and ocean</MDI>
<MDI key="4">124 - Airports</MDI>
<MDI key="5">131 - Mineral extraction sites</MDI>
<MDI key="6">132 - Dump sites</MDI>
<MDI key="7">133 - Construction sites</MDI>
<MDI key="8">141 - Green urban areas</MDI>
<MDI key="9">211 - Non-irrigated arable land</MDI>
</Metadata>
<PAMRasterBand band="1">
<CategoryNames>
<Category></Category>
<Category>111 - Urban fabric</Category>
<Category>212 - Permanently irrigated arable land</Category>
<Category>213 - Rice fields</Category>
<Category>221 - Vineyards</Category>
<Category>222 - Fruit trees and berry plantations</Category>
<Category>223 - Olive groves</Category>
<Category>231 - Pastures</Category>
<Category>311 - Broad-leaved forest</Category>
<Category>312 - Coniferous forest</Category>
<Category>321 - Natural grasslands</Category>
<Category>322 - Moors and heathland</Category>
<Category>122 - Road and rail networks and associated land</Category>
<Category>323 - Sclerophyllous vegetation</Category>
<Category>324 - Transitional woodland-shrub</Category>
<Category>331 - Beaches, dunes, sands</Category>
<Category>332 - Bare rocks</Category>
<Category>333 - Sparsely vegetated areas</Category>
<Category>334 - Burnt areas</Category>
<Category>335 - Glaciers and perpetual snow</Category>
<Category>411 - Inland wetlands</Category>
<Category>421 - Maritime wetlands</Category>
<Category>511 - Water courses</Category>
<Category>123 - Port areas</Category>
<Category>512 - Water bodies</Category>
<Category>521 - Coastal lagoons</Category>
<Category>522 - Estuaries</Category>
<Category>523 - Sea and ocean</Category>
<Category>124 - Airports</Category>
<Category>131 - Mineral extraction sites</Category>
<Category>132 - Dump sites</Category>
<Category>133 - Construction sites</Category>
<Category>141 - Green urban areas</Category>
<Category>211 - Non-irrigated arable land</Category>
</CategoryNames>
<Metadata>
<MDI key="STATISTICS_APPROXIMATE">YES</MDI>
<MDI key="STATISTICS_MAXIMUM">32</MDI>
<MDI key="STATISTICS_MEAN">14.261915053291</MDI>
<MDI key="STATISTICS_MINIMUM">1</MDI>
<MDI key="STATISTICS_STDDEV">8.3957313545462</MDI>
<MDI key="STATISTICS_VALID_PERCENT">84.13</MDI>
</Metadata>
</PAMRasterBand>
</PAMDataset>
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