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If generalized to 2D (and over the unit sphere), you'd get Google's S2 library (https://s2geometry.io/).
In 3D, the same can be done with morton codes (or a 3D hilbert curve) to build an implicit octree. Some raytracing systems use this for fast BVH construction.
In either case, this approach differs from mipmaps in that the underlying data can be sparse, and is simply stored in a sorted array.
We built a general 2D zooming visualization system which supports data in PostgreSQL: https://github.com/tracyhenry/kyrix. Under the hood, it uses PostgreSQL quad tree index to fetch data on demand.
We have also tested with Citus, which helped us scaled to billions of objects. Demo: https://youtu.be/ccES97ni_vI
Check out http://btrdb.io/ which is a similar idea. It's a distributed database as well. It was designed for storing billions or even trillions of records for electrical data from hundreds of sources at once.
It was written by a couple of friends of mine and others, so I'm not sure how it compares to the article link, as I'm not a databases expert.