com-tiles
geoparquet
| com-tiles | geoparquet | |
|---|---|---|
| 2 | 3 | |
| 84 | 723 | |
| - | 4.1% | |
| 3.1 | 5.5 | |
| 6 months ago | 3 days ago | |
| TypeScript | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
com-tiles
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COMTiles (Cloud Optimized Map Tiles) hosted on Amazon S3 and Visualized with MapLibre GL JS
This time I examined COMTiles(Cloud Optimized Map Tiles). COMTiles are often compared to PMTiles, but I have the impression that PMTiles are becoming more popular these days. The technology of COMTiles is excellent, but it is not yet easy to introduce because the library is not published in “npm,” and the documentation is incomplete. One of the reasons for PMTiles' popularity is due in part to its extensive documentation. This article will be of some help to those who are considering introducing COMTiles!
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Show HN: Mbtiles-S3-server – serve vector map tiles directly from mbtiles on S3
Com-tiles[1] also looks pretty good, a bit more optimized than pmtiles.
The whole cloud optimized vector tile/data space is next after COGs (cloud optimized geotiffs). Point-clouds are also getting the index-range-request treatment as COPC[2].
[1] https://github.com/mactrem/com-tiles
geoparquet
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Friends don't let friends export to CSV
That's why I'm working on the GeoParquet spec [0]! It gives you both compression-by-default and super fast reads and writes! So it's usually as small as gzipped CSV, if not smaller, while being faster to read and write than GeoPackage.
Try using `GeoDataFrame.to_parquet` and `GeoPandas.read_parquet`
[0]: https://github.com/opengeospatial/geoparquet
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COMTiles (Cloud Optimized Map Tiles) hosted on Amazon S3 and Visualized with MapLibre GL JS
GeoParquet
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Postgres and Parquet in the Data Lke
> "Generating Parquet"
It is also useful for moving data from Postgres to BigQuery! ( batch load )
https://cloud.google.com/bigquery/docs/loading-data-cloud-st...
Thanks for the "ogr2ogr" trick! :-)
I hope the next blog post will be about GeoParquet and storing complex geometries in parquet format :-)
https://github.com/opengeospatial/geoparquet
What are some alternatives?
L7 - 🌎 Large-scale WebGL-powered Geospatial Data Visualization analysis engine.
mbtiles-spec - specification documents for the MBTiles tileset format
mod_mbtiles - Serve tiles with Apache directly from an .mbtiles file
odbc2parquet - A command line tool to query an ODBC data source and write the result into a parquet file.
mbtiles-s3-server - Python server to on-the-fly extract and serve vector tiles from an mbtiles file on S3
geemap - A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
ngx_http_mbtiles_module - A nginx module to serve map tiles directly from mbtiles container files
flatgeobuf - A performant binary encoding for geographic data based on flatbuffers
planetiler - Flexible tool to build planet-scale vector tilesets from OpenStreetMap data fast
postgres_vectorization_test - Vectorized executor to speed up PostgreSQL
athena-sqlite - A SQLite driver for S3 and Amazon Athena 😳
BlenderGIS - Blender addons to make the bridge between Blender and geographic data