Honestly, I think it's actually https://www.uber.com/en-CA/blog/h3/
Makes one wonder if the YOLO algorithm would work better with hexagons.
"Hexagons were an important choice because people in a city are often in motion, and hexagons minimize the quantization error introduced when users move through a city. Hexagons also allow us to approximate radiuses easily, such as in this example using Elasticsearch."
[Edit]Maybe https://www.researchgate.net/publication/372766828_YOLOv8_fo...
Could you elaborate on this? I experimented with h3 a bit for queries but I have never used it in production. Obviously, it's very effective for Uber but I wonder if you have any other experience with it