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#gdal

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Oslandia

👋 #introduction

🗺 Oslandia is a French SME specializing in OpenSource Geographical Systems ( #GIS ). We design and implement IT systems to manage geographical data.

🎓 We are an opensource editor of #QGIS, experts of #PostGIS, #GDAL, #Python, Web 3D visualization & more …

👩‍💻 Our team of ~30 (great) people is distributed all over France & full remote is at the heart of our organization.

🔗 More info : oslandia.com and linkedin

➡️ We will post company updates and GIS-related infos

OslandiaOSLANDIA(Fr) Formations Postgresql Postgis QGis Web Data et 3D. Prestations de développement sur mesure. Audit et conseil en SIG libre.
Philippe Massicotte

I've added a new experimental branch to the #rstats rnaturalearth package to test reading data directly from the #GDAL virtual file system, supporting both raster and vector data.

This enhancement allows reading the data without needing to download and unzip it into a temporary folder.

You can try it here:

github.com/ropensci/rnaturalea

Feel free to give it a try, and let me know how it works for you!

GitHubGitHub - ropensci/rnaturalearth at feat/use-virtual-gdalAn R package to hold and facilitate interaction with natural earth map data :earth_africa: - GitHub - ropensci/rnaturalearth at feat/use-virtual-gdal
#spatial#gis
Hugh Graham

This {vrtility} 📦 project is still a WIP but I think the potential and flexibility of the approach is huge - basically just let #gdal do all the work. I've finally got my head around how the sequence of things should fit together but it is still missing a few important features. If you're interested in #rspatial #remotesensing I'd love your thoughts and feedback. powered by #gdalraster 🚀
github.com/Permian-Global-Rese

Continued thread
Michael Sumner

we ran a multi-decade extraction of points-in-time for 46000 points 1993-2024 (at bottom level, it's variable so we indexed the level upfront for each point) ran this the "traditional way" using #terra #GDAL to extract points from relevant layers (point-sets grouped by date,level) for salt,temp,u,v,w,mld - ran on 28cpus with #furrr/#future took ~80min

will get a public dataset to repeat the example for illustration (elephant seals I hope)

Javier Jimenez Shaw

Do you want to contribute to an open source project?
Do you like/enjoy/love #GIS , #maps , #cartography , #surveying , #topography or #geodesy ?
Do you know C/C++? (this is optional)

You are very welcome to the communities of #GDAL or PROJ developers.
Join us!

gdal.org
proj.org

Wait a second: are you already coding in C++ or Python using these libraries? What are you waiting for!?

Hint: mailing lists are a good starting point ;)

gdal.orgGDAL — GDAL documentation
#cpp#gischat
*
Michael Sumner

{gdalraster} is heading to a big release with a new class GDALVector #rstats

github.com/USDAForestService/g

It's really tight, really general and insanely powerful, low level features of #GDAL itself at your fingertips

Check it out, install from GitHub and throw it at your vector spatial data (what *you" choose to try first is incredibly valuable to the maintainers, don't hesitate to share your experiences and ideas)

GitHubGitHub - USDAForestService/gdalraster: R Bindings to GDAL (Geospatial Data Abstraction Library)R Bindings to GDAL (Geospatial Data Abstraction Library) - USDAForestService/gdalraster
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