Aside from OSM specifics, performance friendly formats for spatial data that support spatial indexing can make huge impact on usability and productivity of applications. e.g. trying to view a large dataset in QGIS that has been saved as KMZ (zipped XML) can make QGIS basically hang for minutes, while the same dataset saved as something like flatgeobuf [1] can be loaded instantly.

[1] https://flatgeobuf.org/

Can anyone recommend me a method of meshing LIDAR point clouds? The sparseness of the data on building walls & other near-vertical surfaces combined with a lack of point normals leads to degenerate solutions with all the common approaches (poisson/ball pivot/vcg in meshlab) not to mention extremely slow perf. Tree canopies and overhanging parapets make a simple heightmap approach less-than desirable (though ultimately acceptable if I can't find anything better). I'm trying to turn 90 billion lidar points into maybe 30-50 million triangles, hopefully without spending months developing a custom pipeline.

https://media.jochentopf.com/media/2022-08-15-study-evolutio...

https://github.com/osmlab/osm-data-model

https://blog.openstreetmap.org/2023/01/04/reminder-call-for-...

Resolving the coordinates to node references in current data model is such a nuisance as it's slow and requires lots of RAM.