OSM vector tiles in the Esri ecosystem: Riccardo Klinger demonstrates how to build an independent #basemap pipeline from #OpenStreetMap data using #GDAL/#OGR and integrate the resulting #vectorTiles into the #Esri ecosystem. Open-source and proprietary tools turn out to complement each...
https://spatialists.ch/posts/2026/04/03-osm-vector-tiles-in-the-esri-ecosystem/ #GIS #GISchat #geospatial #SwissGIS
OSM vector tiles in the Esri ecosystem – Spatialists – geospatial news

Riccardo Klinger demonstrates how to build an independent #basemap pipeline from #OpenStreetMap data using #GDAL/#OGR and integrate the resulting #vectorTiles into the #Esri ecosystem. Open-source and proprietary tools turn out to complement each other seamlessly.

Spatialists – geospatial news
Let's Hop #6: Pixel für Pixel. Rasterdatenprozessierung in Apache Hop. https://blog.sogeo.services/blog/2026/03/30/lets-hop-06.html #Java #ApacheHop #GDAL #FME

New CRAN release of R package gdalraster, comprehensive API bindings to GDAL:
https://firelab.github.io/gdalraster/news/index.html

#rstats #rspatial #gdal

Changelog

Introduction to the Geospatial Abstraction Library (GDAL) with the new CLI

https://videos.qwast-gis.com/w/8zCe8texdFBhWVzpYR9wrA

Introduction to the Geospatial Abstraction Library (GDAL) with the new CLI

PeerTube

🚀 QGIS2VectorTiles v2.3 is here — with a huge performance boost!

✅ Preprocessing now runs in parallel using multiple QGIS tasks.
🕜 No more waiting on endless rules exports.

Scale your resources, generate stunning web maps faster and do not forget to say goodbye to ESRI’s VTPK.

Please share and help spread the word.
Go #FOSS ! 🙂

https://gallpeters.github.io/QGIS2VectorTiles/

#maplibre #qgis #gdal #cartography #gis #webmapping

I am excited about this tech and where it's going but I can't help try to point *away from* the thing that just didn't bring the tech *already* but obstinately blocked it for reasons unknown

https://sedona.apache.org/latest/blog/2026/03/01/sedonadb-030-release/#r-gdalogr-read-support here's some examples of less-amazing alternative: https://gist.github.com/mdsumner/fc16a9f98ed35dbfcd944124da02fcf5 supported A VERY LONG TIME by #GDAL

SedonaDB 0.3.0 Release - Apache Sedona

Apache Sedona is a cluster computing system for processing large-scale spatial data. Sedona extends existing cluster computing systems, such as Apache Spark, Apache Flink, and Snowflake, with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines.

Oh no, remote #GeoParquet is much less fun in #QGIS than I thought. These few polygons took ~50 Megabytes to fetch. I guess it is not enough to just use #GDAL defaults.