The third and final blog post in the GeoTIFFs to GPUs trilogy is finally out! From TIFF data in CUDA memory handled by Rust, how do we zero-copy it to Python? The answer is with the DLPack protocol of course!

Once the data is in Python (as a CuPy array), can we make it more user-friendly? Yep, by putting it into an xarray.DataArray that labels the channel/width/height dimensions.

You make it sound so easy, are there any caveats? Lots! But I'll let you read the full blog and navigate the many links within for the details 😝

https://weiji14.xyz/blog/geotiffs-to-gpus-part-3:-the-last-stage-via-dlpack-into-the-world/

#GeoTIFF #nvTIFF #rustlang #DLPack #xarray #CuPy #cog3pio

Will we finally get nice html representations of @movingpandas Trajectories and TrajectoryCollections?

Inspired by #pydata #xarray 🤩

WIP 👩‍💻 : https://github.com/movingpandas/movingpandas/issues/293

Any feedback / ideas welcome!

#GISChat #MovementDataAnalysis

new GDAL in-dev backend for #xarray coupled directly for Multidim *and* Classic raster (and actually works, pretty good improv this week) #python

https://github.com/mdsumner/gdx

Please try it out with your most | least favourite Zarr! If you have connection issues just ask I know enough to be dangerous and I'm pretty good with #GDAL would love any feedback 🙏

RE: https://wisskomm.social/@ioer/115899330915763542

I really took a deep dive into #datashader with this map: Locals & Tourists in Germany, as derived from 67 Million Geo-Social Media Posts (2007-2022) in Germany. The data includes public shared posts from Instagram, Flickr, Twitter and iNaturalist.

I always wanted to create such a map, following the footsteps of Eric Fisher's Locals & Tourists dataset from 2011 [1].

I shared the code for producing this map here [2]. The repository is available here [3]. This includes some neat methods for various #geospatial processing tasks in #Python, such as exporting a datashader map to a #GeoTiff [4] with the help of #Xarray and #Rasterio.

Finally, all of this was created in a privacy-preserving way using #HyperLogLog, which allowed me to share the code and abstracted data publicly for full reproducibility and transparency. [6] #FAIR

Below you'll find the link to the (quite succinct) publication in Natur und Landschaft in Karten (#NuL).

[1]: https://www.flickr.com/photos/walkingsf/albums/72157624209158632
[2]: https://code.ad.ioer.info/wip/digital_traces_map/html/03_visualization.html
[3]: https://gitlab.hrz.tu-chemnitz.de/ad/digital_traces_map/
[4]: https://gitlab.hrz.tu-chemnitz.de/s7398234--tu-dresden.de/base_modules/-/blob/main/raster.py?ref_type=heads#L78
[5]: https://www.nul-online.de/article-7301410-1111/landschaft-und-natur-in-karten-.html
[6]: https://doi.org/10.71830/VDMUWW

🚀 PolSARpro update released!

This new version includes:

• a new tutorial showing how to run PolSARpro on BIOMASS data directly in the cloud via the ESA MAAP platform (no local download needed)
• access to an ALOS-1 sample dataset to get started with the tutorials
• bug fixes and stability improvements

📦 Available on conda-forge
🔗 Code: https://github.com/satim-co/PolSARpro
📘 Docs: https://polsarpro.readthedocs.io

#PolSAR #SAR #RemoteSensing #EarthObservation #OpenSource #Python #ESA #MAAP #BIOMASS #Dask #Xarray

Sorry, that I misuse this platform for my silly question:

I have a  dataset
Dimensions: (time: 1, band: 4, y: 8192, x: 4096)
Coordinates:
* time datetime64[ns] 8B 2023-06-15
* band <U5 80B 'Red' 'Green' 'Blue' 'Alpha'
* y float32 33kB 7.064e+06 7.064e+06
* x float32 16kB 1.194e+06 1.194e+06
Data variables:
band_data (time, band, y, x) uint8 134MB dask.array<chunksize=(1, 4, 1024, 1024), meta=np.ndarray>
spatial_ref int64 8B ...

1/2 #xarray #gdal #zarr

Does anybody have experience with #xarray for #numpy labeled arrays?

My use case is rather simple with a 2d ndarray and I'm mostly after easier development for myself and contributors if it's possible to switch from ndarray[:, 0] and ndarray[:, 1] to ndarray[fieldname]

#python