gdal VS planetiler

TiTiler is a dynamic tile server built on FastAPI and Rasterio/GDAL. Its main features include support for Cloud Optimized GeoTIFF(COG), multiple projection methods, various output formats (JPEG, JP2, PNG, WEBP, GTIFF, NumpyTile), WMTS, and virtual mosaic. It also provides Lambda and ECS deployment environments using AWS CDK.

GDAL is the real heart, the python aspect is mostly wrappers around that I'm fairly sure. I love python for the record, the only reason I bring it up, is cause python haters accuse it of being slow, but QGIS drops down to C++ when speed is necessary, like most modern packages do.

gdal 0.15 (repo, docs), a set of Rust bindings for the GDAL library, used for access to geo-spatial data formats, is now out!

GDAL (Geospatial Data Abstraction Library) is a the geospatial data processing library. It handles a lot of Raster/Vector analysis and alteration. gdal_contour and gdal_rasterize which would be used to create isolines (contour lines) . There's more complex processing and analyses than that. More common is reprojecting multiple layers, and some that being as Vector files, into different coordiatne systems.

Access: GDAL

As an example we use GDAL heavily through its C# binds. We do all the additional data processing, that isn't done by the C++ GDAL, in C#. The final results are both Data (held in memory or temp exported to a file), and a normalized Raster Texture that we can display on a TextureRect. Most of the C# data processing scripts aren't even Inheriting from any Godot Class.

I think this would be the best way to handle this. GDAL is what you should look into for this project.

Worth mentioning this project (https://github.com/onthegomap/planetiler) that lets you create osm mbtiles and pmtiles pretty easy!

For a self-hosted vector tile stack you can have a look into https://github.com/onthegomap/planetiler I found it very easy to get started and when you know the other stacks it is also very fast to create these vector tiles even for planet-scale.

(note, that I'm not affiliated with them, but they use some source code from us for the efficient import and also contributed to GraphHopper, but this did not influence my experience ;) )

> I wonder why so many seem to be moving away from raster tiles to vector data.

The flexibility of styling. And you can easily serve customers that need different default languages. This makes maps also more accessible for countries without Latin alphabet.

I used https://github.com/onthegomap/planetiler and https://download.geofabrik.de, maybe it helps.

I spent a while optimizing sqlite inserts for planetiler, this is what I came up with:

https://github.com/onthegomap/planetiler/blob/db0ab02263baaa...

It batches inserts into bulk statements and is able to do writes in the 500k+ per second range, and reads are 300-400k/s using those settings.

Checkout https://github.com/onthegomap/planetiler.

Super easy way to generate a MBTiles, which you can then serve directly, or further convert to PMTiles, which can be used to host vector tiles for client-side rendering using MapLibre (or other renderers).

Raster tiles are a lot harder because you have to generate them on the server, and that's a lot more resource intensive.

I built planetiler (https://github.com/onthegomap/planetiler) for this purpose. The output up to z14 is ~80gb and depending on how big of a machine you have it takes from 30 minutes up to a few hours - no DB required, just java or docker. If you are only going to z11-12, it should be quite a bit faster/smaller.

Brandon from Protomaps is also helping add pmtiles output natively to planetiler, so you won't need a conversion step afterwards!

It's not raster directly, but you could use planetiler ( https://github.com/onthegomap/planetiler ) to build a full planet vector map . Then you could use something like TileServer-GL to server the vector map with a style. TileServer-gl ( https://github.com/maptiler/tileserver-gl) would provide a raster source that displays in the style you set on your vector map.

I recently left Twitter after 9 years, most recently serving as tech lead for the knowledge graph group (was 45 people). I helped apply the KG to drive a large portion of Twitter’s revenue and new product launches. In my spare time I do data visualization and web mapping, most recently https://github.com/onthegomap/planetiler