In part 1 I covered the hard problems: COG multi-resolution, SDF raster smoothing, and Barbapapa topology-preserving Laplacian smoothing. Freddie could generate good-looking 10m land cover vector tiles. This post is about what happened next: killing the algorithm I spent a week perfecting, solving a surprisingly dumb storage problem, and wiring the tiles all the way through to the map SDK.

Barbapapa is dead

I killed Barbapapa a few weeks after shipping it.

Serving vector tiles from 10-meter resolution land cover data. What breaks when you 100x the input resolution, and how topology-preserving smoothing fixes the ugly parts.

Background

Freddie is our tile server for land cover data. It reads GeoTIFF raster files (pixels classified as wood, grass, built-up, water, etc.) and serves them as vector tiles or raster tiles over HTTP. Nothing fancy. Read pixels, make shapes, encode, serve.

The original setup used ESA WorldCover at ~100m resolution. Native resolution sits around z8 in Web Mercator, so serving z0–z8 tiles was straightforward. Sample pixels, run-length encode, merge adjacent faces of the same class, encode to MVT. Done. It just worked, and I didn’t think much about it.