Our own @stefdegreef has fully covered the entire Belgian capital #Brussels by bike to create street-level imagery in 360° using one of our GoPro Hero Max's.
All footage is available on @mapillary .com under the CC-BY-SA license.
Massive applause👏👏👏
https://t.co/JrfLcNmj6j
@pietercolpaert @Joe_GISc@Wikimedia_BE@brinkwoman @underdarkGIS @postgis@GeoserverO@openlayers As others have pointed out, proj4js may work for you but yes, CRS-es are quite complex and hard to maintain in full generality. As a hint for what could be so hard, check out the release notes of the C++ PROJ library https://t.co/pQZSK1XuEQ in the last few years.
Fresh from the oven: QGIS 3.26, STAC and COPC nicely working together!
Join us today in a QGIS Open Day session from 13:00 UTC where we will discuss the latest news about point clouds and 3D!
https://t.co/TdzyhfbKAF
PDAL 2.4.0 is released. It includes https://t.co/yq8z4QDkCa read and write support, time queries in @tiledb, comment support in JSON pipelines, and embedded always-on LAZ support due to LASzip Apache relicensing. Release notes at https://t.co/pJQl6k5rJe
The first release of GeoParquet is out! This is a new geospatial vector data (point, line, polygon) format that is built on Parquet, an awesome columnar-storage format. See https://t.co/e4Hd0A8qN9 for more information and join the growing community! (1/4)
Thanks @tiledb for contributing PDAL's enhanced Python API. Thanks to this, you can now compose PDAL operations in Python instead of just passing JSON to PDAL. It should open up huge possibilities for people mixing point clouds with Python's ML machinery.
Cloud Optimized Point Cloud, or COPC, seeks to live in the same niche as COG does for raster data, which allows for single-file storage of a the most common container format with support for spatially accelerated incremental remote access. Read up on it at https://t.co/xXQcuV4Eyx
With support from @NumFOCUS and @GdalOrg's sponsors https://t.co/cBgz0hSWwJ, we took a big step yesterday in fixing things so the GDAL guy in France isn't in this spot all alone. https://t.co/qWavtcVJYt
We are often asked to comment on the differences between Dask and Vaex. We hope you will find this useful!
https://t.co/ILGG3toj0k
#Python#Vaex#Dask#DataScience
Next week Thursday, at the #DaskSummit2021, we're having a workshop on "Scaling geospatial vector data" with dask. Feel welcome to join!
May 20, 11:00-13:00 UTC, see https://t.co/2mdQux8ERk
The program for #openbelgium21 is live 🚀🚀! Check out the line-up on https://t.co/dWlohrMZIT and register for FREE for your favorite sessions. #OpenKnowledge#OpenData
I just published "The templating system of nbconvert 6".
Many thanks to @davidbrochart@maartenbreddels@codeseal, and all other contributors to this release! 🙏
https://t.co/icQeWkTH03
Presented at @GeoPythonConf about the ongoing developments to improve spatial processing of vector data with PyGEOS and Shapely 2.0. Some nice improvements are coming!
See slides at https://t.co/Iyc6zRjI1d
Help us shape the future of @geopandas development. We're launching our first GeoPandas User Survey!
https://t.co/05maOC1HT4
It takes 5-10 minutes. After we close it in about a month, we will share the results with you.
Thank you!
The Gentoo and Ubuntu of datascience: thinking about a source distribution based on mamba, quetz, boa and conda-packages, so users can recompile with custom compiler flags and use special build-features. I'll get a prototype of this into boa soon!
https://t.co/QOihvDHnRX
Yay! @pandas_dev officially added consistent missing data handling to its roadmap! 🎉
https://t.co/SOifk8MYmg
If you'd like to see this sooner rather than later and want to help with making it actually happen (and not only words on a roadmap ;-)), don't hesitate to contact me
We just released @ApacheArrow 1.0.0, the first formally "stable columnar format" release with a move to SemVer for the libraries. We have a much improved website, too. Read more about what's new
https://t.co/j24VdxqFTL