Summary

We currently use geopandas as our backend for reading and storing geometry data. While geopandas is great, it's known to be quite slow, compared to DuckDB and more recently SedonaDB, when performing spatial filtering operations on a single node when the number of geometries grows large.

SedonaDB performed a benchmark comparison using SpatialBench.

We should support more than just geopandas as a backend. I propose we implement a torchgeo.datasets.SedonaDBDataset for loading vector dataset into SedonaDB dataframes and performing spatial filtering on them.

cc: @jiayuasu @rbavery @paleolimbot