tippecanoe VS titiler

This is awesome. Such a great use case for nix.

I do a lot of geospatial processing in the cloud and I've been using Tippecanoe a lot to create vector tiles. It pairs well with PM Tiles for storing on the cloud. It seriously increases the web app performance for massive data sets. I queue these up with ECS tasks to process our json/csv/parquet input and create optimize vector tile outputs.

https://github.com/felt/tippecanoe

https://github.com/protomaps/PMTiles

Tippecanoe would be a great addition to your nix packages. I've been thinking more and more about how Nix could fit into this pipeline.

Great work!

I'm the author of a few of the tools mentioned in this post!

A convenient new development is instead of using tippecanoe -> go-pmtiles to create PMTiles archives, you can now output .pmtiles directly:

tippecanoe -o bks2.pmtiles mainroad.geojson ...

This is available in Tippecanoe (https://github.com/felt/tippecanoe) v2.17 and later.

Thanks to Felt (https://felt.com) for supporting this open source work.

tippecanoe

Things I have tried so far: - Just using native vector tile conversion as it involves feature simplification. Doesn't work since smallest feature just disappear, resulting in blank regions instead of "averaged" regions. - Using tippecanoe's built in features to drop/merge in densest zones. Results are disappointing because of unexpected (and too big) differences between each zoom level. - Rasterizing the map, sieving, then vectorizing with smoothing. Doesn't work because pixel information are mixed. I would need a way to rasterize while preserving the land-cover category (with some kind of majority filter ?), but haven't find a way to do this with any QGis built-in or plugin feature.

Take a look at Tippecanoe, which is under active development again[0]. The original developer, Erica Fischer (who is wonderful to work with), has a fork[1] where new work is happening.

[0] https://felt.com/blog/erica-fischer-tippecanoe-at-felt

[1] https://github.com/felt/tippecanoe

https://felt.com/blog/erica-fischer-tippecanoe-at-felt

There's a lot to like about GDAL+Rasterio, although I've found having all HTTP requests go through GDAL's C API does result in some limitations on concurrency and multithreading. GDAL's configuration being based entirely on env vars also has its downsides: https://github.com/developmentseed/titiler/issues/186

TiMVT and TiFeatures, along with TiTiler for raster services, complete a trio of python-FastAPI services. The current roadmap includes merging TiFeatures & TiMVT, the vector-based services, into a single project to reduce code redundancy.

COGs are great, but from my experience micro services like titiler don't support complex styling logic. Is there a possibility to create a serverless WMS service with complex styling requirements?

Consider something like titiler if you want to serve the pile of geotiffs out to the web.

We were able to host 10gb six band rasters as well as 2cm RGB split into 100m tiles on S3 using COGS, mosaic.json and the TiTiler project which is powered by AWS lambda functions