tippecanoe
pygeoapi
tippecanoe | pygeoapi | |
---|---|---|
7 | 9 | |
772 | 446 | |
5.2% | 0.4% | |
8.0 | 9.3 | |
23 days ago | 1 day ago | |
C++ | Python | |
BSD 2-clause "Simplified" License | MIT License |
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tippecanoe
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Geospatial Nix – create, use and deploy today
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!
- Protomaps – A free and open source map of the world
- How The Post is replacing Mapbox with open source solutions
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Self-Hosted Vector Tiles
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.
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COMTiles (Cloud Optimized Map Tiles) hosted on Amazon S3 and Visualized with MapLibre GL JS
tippecanoe
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How would you generalize a very high density vector map for various zoom levels ?
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.
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OpenStreepMap 2012 vs. 2022
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
pygeoapi
- On the fly conversion of raster to vector spatial index (h3)
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How The Post is replacing Mapbox with open source solutions
If you're just looking for a WFS (geojson/data) and not WMS/raster output, you might look at PyGeoAPI. (https://pygeoapi.io/) I haven't used it, but have looked at it a bit for a potential project to export geo data as an API.
Geoserver does fill a pretty big hole in the capabilities space -- it's pretty easy to get going with a bunch of layers and style them, but ultimately they're implementing a RDBMS in xml files, and it's a big, complicated, java system that's been one of the more troublesome portions of the stack (IME).
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GeoServer is an open source server for sharing geospatial data
I would also like to share https://pygeoapi.io/ which relies on the new OGC API standard to share geospatial data.
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Volunteering for FOSS4G
python: https://pygeoapi.io - the whole project is ogcapi stuff
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My Raster and vectors to an API
This may be a good start, without having to write much code. https://pygeoapi.io/
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Anyone know a good Python OGC client?
I believe pygeoapi https://pygeoapi.io/ is what you're looking for. It implements the OGC API suite of standards, and is in active development. I haven't used it yet, but hope to pretty soon within a Django project. If you do go with it I'd love to hear what you think.
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How easy is it to set up a QGIS server on Ubuntu?
https://pygeoapi.io/ link to the project. The devs are really active and helpful in Gitter of you ever have any issues
- How-to share geospatial data on the web
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Sharing Geospatial Data with OGC API, pygeoapi and MongoDB
In order to publish the dataset using the OGC API Features standard, we need a software which implements the standard. In this tutorial we will use pygeoapi, which is a python server implementation, released under a FOSS (MIT) license. pygeoapi needs a backend to store the data. For that we will use the MongoDB document oriented database. In order to make deployment easier, the complete stack was virtualised into a set of docker containers, and orchestrated using docker-compose.
What are some alternatives?
planetiler - Flexible tool to build planet-scale vector tilesets from OpenStreetMap data fast
qwc2 - QGIS Web Client 2 Components
tippecanoe - Build vector tilesets from large collections of GeoJSON features.
lizmap-web-client - Transfer a QGIS project on a server, Lizmap is providing the web interface to browse it
tilemaker - Make OpenStreetMap vector tiles without the stack
valhalla - Open Source Routing Engine for OpenStreetMap
go-pmtiles - Single-file executable tool for working with PMTiles archives
OpenLayers3 - OpenLayers
maputnik - An open source visual editor for the 'MapLibre Style Specification'
Docker Compose - Define and run multi-container applications with Docker
osmium-tool - Command line tool for working with OpenStreetMap data based on the Osmium library.
react-leaflet - React components for Leaflet maps