Can I speed up the spatialdata.polygon_query when dealing with really big datasets?

Hello,

thank you for providing the community with such a great tool!

I was wondering if you could help or advise on the following.

I have a very large xenium dataset (~2 million cells). I have read it and saved it as a zarr file and I have read it back in on my notebook. At the same time I have used QuPath to create annotations for my images, I have saved them as geojson files and read them back in as a geodataframes and subsequently converted them into polygons.

I would like to add this type of metadata information in my sdata object, so that I can annotate and filter my sdata.table based on that annotation. The way that I am currently trying to do this is by using the following piece of code, really in an effort to create subsets of my dataset based on polygons which I can then assign the annotation.

from spatialdata import polygon_query # type: ignore

cropped_sdata2 = polygon_query(
    sdata,
    polygon=polygon,
    target_coordinate_system="global"
)

This piece of code is extremely slow because of the size of the dataset, so I was wondering if there is a way to further speed up this function (I am already running this analysis on a server), or if there is a simpler way incorporate my annotation to the sdata object.

Thanks a lot for your help!

Cheers,
Anastasia

Start at the public spatialdata.polygon_query entry point and reproduce the reported slowdown with a large Xenium dataset, a Zarr-backed sdata object, and the GeoJSON-derived polygon. Measure the current query behavior and identify a concrete, testable performance target or simpler annotation workflow; the issue does not name implementation files or tests.