gdal org
@GdalOrg
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No longer here. Find us at @[email protected] GDAL is a translator library for raster and vector geospatial data formats.
Joined April 2021
By the way, we've moved away from this platform as everybody should, right? Find us at
mastodon.social
83 Posts, 3 Following, 1.01K Followers · GDAL is a translator library for raster and vector geospatial data formats that is released under an X/MIT style Open Source license https://gdal.org
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RT @mouthofmorrison: Just sent another $300+ to OSGeo! Reminder: all proceeds go to support the maintainers of GDAL! And it’s not just shir….
my-store-9f5dbe.creator-spring.com
This is a product description
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RT @howardbutler: @blaynechard from @LandInfoNZ did a bunch of useful research to show that NZ would have no problem using @Esri's LERC com….
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A GDAL 3.7.0 release candidate is now available for your testing: Lots of exciting new stuff !.
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If you use GDAL with Conda, see for instructions to upgrade your existing environment to recent GDAL builds that now use libjpeg-turbo.
github.com
I might be wrong, but it seems to me that the GDAL build uses IJG libjpeg, and not libjpeg turbo (https://github.com/conda-forge/libjpeg-turbo-feedstock). The later is what has been used for years ...
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RT @howardbutler: @EvenRouault posted a nice summary of the work he has done for the @GdalOrg sponsorship program to the mailing list https….
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Due to recent move, GDAL Docker images are migrated to :tagname} :
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Thanks to Kikitte Lee for contributing a new raster->vector polygonizer algorithm, that is up to 10 times faster in some cases:
github.com
What does this PR do? The Two-Arm Chains EdgeTracing Algorithm does a faster, memory saving, and robust polygonize job. It is described in Junhua Teng, Fahui Wang, Yu Liu, An Efficient Algorithm fo...
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Not yet adopted, but we are already mentioned as a reference implementation!.
Seeking public comment on FlatGeobuf becoming an OGC Community Standard. FlatGeobuf is a performant binary encoding for geographic data that works well as a “cloud native” lossless format for vector data #OGCPubCom
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All that made possible by us. And practicality of working directly from #shizzle will increase again when @sozipOrg reaches your GDAL build.
Did you know, #geopandas can read and write directly to and from zipped #shapefiles, a.k.a #shizzle files (.shz). #gischat . . . H/t
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RT @sozipOrg: Try me now on @condaforge with @qgis !.conda create --name sozip_test.conda activate sozip_test.conda install -c gdal-maste….
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There are limits to our good will that some have "precisely" exceeded :-) And our abstraction model doesn't necessarily capture all specialized data models.
@berttemme Core issue from the article, ". but nothing fit our particular needs. So like Google, we embarked upon creating our own specification. ", like everyone else. Should a spatial format reach critical mass, standard or not, the great homogenizer @GdalOrg will handle it.
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Relax @EvenRouault . At the end of the day, all those rather ephemeral mind constructions look pretty much the same after a gdal_translate -of COG or ogr2ogr -f GeoPackage (the later being best supplemented with @sozipOrg ).
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