Tools for working with open building datasets
- Free software: Apache Software License 2.0
- Documentation: https://opengeos.github.io/open-buildings
- Creator: Chris Holmes
This repo is intended to be a set of useful scripts for getting and converting Open Building Datasets using Cloud Native Geospatial formats. Initially the focus is on Google's Open Buildings dataset and Overture's building dataset.
The main tool that most people will be interested in is the get_buildings command, that
lets you supply a GeoJSON file to a command-line interface and it'll download all buildings
in the area supplied, output in common GIS formats (GeoPackage, FlatGeobuf, Shapefile, GeoJSON and GeoParquet).
The tool works by leveraging partitioned GeoParquet files, using DuckDB to just query exactly what is needed. This is done without any server - DuckDB on your computer queries, filter and downloads just the rows that you want. Right now you can query two datasets, that live on Source Cooperative, see here for Google and here for Overture. The rest of the CLI's and scripts were used to create those datasets, with some additions for benchmarking performance.
This is basically my first Python project, and certainly my first open source one. It is only possible due to ChatGPT, as I'm not a python programmer, and not a great programmer in general (coded professionally for about 2 years, then shifted to doing lots of other stuff). So it's likely not great code, but it's been fun to iterate on it and seems like it might be useful to others. And contributions are welcome! I'm working on making the issue tracker accessible, so anyone who wants to try out some open source coding can jump in.
Install with pip:
pip install open-buildingsThis should add a CLI that you can then use. If it's working then:
obWill print out a help message. You then will be able run the CLI (download 1.json:
ob tools get_buildings 1.json my-buildings.geojson --country_iso RWYou can also stream the json in directly in one line:
curl https://data.source.coop/cholmes/aois/1.json | ob get_buildings - my-buildings.geojson --country_iso RW
The main tool for most people is get_buildings. It queries complete global
building datasets for the GeoJSON provided, outputting results in common geospatial formats. The
full options and explanation can be found in the --help command:
% ob get_buildings --help
Usage: ob get_buildings [OPTIONS] [GEOJSON_INPUT] [DST]
Tool to extract buildings in common geospatial formats from large archives
of GeoParquet data online. GeoJSON input can be provided as a file or piped
in from stdin. If no GeoJSON input is provided, the tool will read from
stdin.
Right now the tool supports two sources of data: Google and Overture. The
data comes from Cloud-Native Geospatial distributions on
https://source.coop, that are partitioned by admin boundaries and use a
quadkey for the spatial index. In time this tool will generalize to support
any admin boundary partitioned GeoParquet data, but for now it is limited to
the Google and Overture datasets.
The default output is GeoJSON, in a file called buildings.json. Changing the
suffix will change the output format - .shp for shapefile .gpkg for
GeoPackage, .fgb for FlatGeobuf and .parquet for GeoParquet, and .json or
.geojson for GeoJSON. If your query is all within one country it is strongly
recommended to use country_iso to hint to the query engine which country to
query, as this will speed up the query significantly (5-10x). Expect query
times of 5-10 seconds for a queries with country_iso and 30-60 seconds
without country_iso.
You can look up the country_iso for a country here:
https://github.com/lukes/ISO-3166-Countries-with-Regional-
Codes/blob/master/all/all.csv If you get the country wrong you will get zero
results. Currently you can only query one country, so if your query crosses
country boundaries you should not use country_iso. In future versions of
this tool we hope to eliminate the need to hint with the country_iso.
Options:
--source [google|overture] Dataset to query, defaults to Overture
--country_iso TEXT A 2 character country ISO code to filter the
data by.
-s, --silent Suppress all print outputs.
--overwrite Overwrite the destination file if it already
exists.
--verbose Print detailed logs with timestamps.
--help Show this message and exit.
Note that the get_buildings operation is not very robust, there are likely a number of ways to break it. #13
is used to track it, but if you have any problems please report them in the issue tracker
to help guide how we improve it.
We do hope to eliminate the need to supply an iso_country for fast querying, see #29 for that tracking issue. We also hope to add more building datasets, starting with the Google-Microsoft Open Buildings by VIDA, see #26 for more info.
In the google portion of the CLI there are two functions:
converttakes as input either a single CSV file or a directory of CSV files, downloaded locally from the Google Buildings dataset. It can write out as GeoParquet, FlatGeobuf, GeoPackage and Shapefile, and can process the data using DuckDB, GeoPandas or OGR.benchmarkruns the convert command against one or more different formats, and one or more different processes, and reports out how long each took.
A sample output for benchmark, run on 219_buildings.csv, a 101 mb CSV file is:
Table for file: 219_buildings.csv
╒═══════════╤═══════════╤═══════════╤═══════════╤═══════════╕
│ process │ fgb │ gpkg │ parquet │ shp │
╞═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡
│ duckdb │ 00:02.330 │ 00:00.000 │ 00:01.866 │ 00:03.119 │
├───────────┼───────────┼───────────┼───────────┼───────────┤
│ ogr │ 00:02.034 │ 00:07.456 │ 00:01.423 │ 00:02.491 │
├───────────┼───────────┼───────────┼───────────┼───────────┤
│ pandas │ 00:18.184 │ 00:24.096 │ 00:02.710 │ 00:20.032 │
╘═══════════╧═══════════╧═══════════╧═══════════╧═══════════╛
The full options can be found with --help after each command, and I'll put them here for reference:
Usage: open_buildings convert [OPTIONS] INPUT_PATH OUTPUT_DIRECTORY
Converts a CSV or a directory of CSV's to an alternate format. Input CSV's
are assumed to be from Google's Open Buildings
Options:
--format [fgb|parquet|gpkg|shp]
The output format. The default is FlatGeobuf (fgb)
--overwrite Whether to overwrite any existing output files.
--process [duckdb|pandas|ogr] The processing method to use. The default is
pandas.
--skip-split-multis Whether to keep multipolygons as they are
without splitting into their component polygons.
--verbose Whether to print detailed processing
information.
--help Show this message and exit.
Usage: open_buildings benchmark [OPTIONS] INPUT_PATH OUTPUT_DIRECTORY
Runs the convert function on each of the supplied processes and formats,
printing the timing of each as a table
Options:
--processes TEXT The processing methods to use. One or more of duckdb,
pandas or ogr, in a comma-separated list. Default is
duckdb,pandas,ogr.
--formats TEXT The output formats to benchmark. One or more of fgb,
parquet, shp or gpkg, in a comma-separated list.
Default is fgb,parquet,shp,gpkg.
--skip-split-multis Whether to keep multipolygons as they are without
splitting into their component polygons.
--no-gpq Disable GPQ conversion. Timing will be faster, but not
valid GeoParquet (until DuckDB adds support)
--verbose Whether to print detailed processing information.
--output-format TEXT The format of the output. Options: ascii, csv, json,
chart.
--help Show this message and exit.
Warning - note that --no-gpq doesn't actually work right now, see opengeos#4 to track. It is just always set to true, so DuckDB times with Parquet will be inflated (you can change it in the Python code in a global variables). Note also that the ogr process does not work with --skip-split-multis, but will just report very minimal times since it skips doing anything, see opengeos#5 to track.
I'm mostly focused on GeoParquet and FlatGeobuf, as good cloud-native geo formats. I included GeoPackage and Shapefile mostly for benchmarking purposes. GeoPackage I think is a good option for Esri and other more legacy software that is slow to adopt new formats. Shapefile is total crap for this use case - it fails on files bigger than 4 gigabytes, and lots of the source S2 Google Building CSV's are bigger, so it's not useful for translating. The truncation of field names is also annoying, since the CSV file didn't try to make short names (nor should it, the limit is silly).
GeoPackage is particularly slow with DuckDB, it's likely got a bit of a bug in it. But it works well with Pandas and OGR.
When I was processing V2 of the Google Building's dataset I did most of the initial work with GeoPandas, which was awesome, and has the best GeoParquet implementation. But the size of the data made its all in memory processing untenable. I ended up using PostGIS a decent but, but near the end of that process I discovered DuckDB, and was blown away by it's speed and ability to manage memory well. So for this tool I was mostly focused on those two.
Note also that currently DuckDB fgb, gpkg and shp output don't include projection information, so if you want to use the output then you'd need to run ogr2ogr on the output. It sounds like that may get fixed pretty soon, so I'm not going to add a step that includes the ogr conversion.
OGR was added later, and as of yet does not yet do the key step of splitting multi-polygons, since it's just using ogr2ogr as a sub-process and I've yet to find a way to do that from the CLI (though knowing GDAL/OGR there probably is one - please let me know). To run the benchmark with it you need to do --skip-split-multis or else the times on it will be 0 (except for Shapefile, since it doesn't differentiate between multipolygons and regular polygons). I hope to add that functionality and get it on par, which may mean using Fiona. But it seems like that may affect performance, since Fiona doesn't use the GDAL/OGR column-oriented API.
There are 3 options that you can set as global variables in the Python code, but are not yet CLI options. These are:
RUN_GPQ_CONVERSION- whether GeoParquet from DuckDB by default runs gpq on the DuckDB Parquet output, which adds a good chunk of processing time. This makes it so the DuckDB processing output is slower than it would be if DuckDB natively wrote GeoParquet metadata, which I believe is on their roadmap. So that will likely emerge as the fastest benchmark time. In the code you can setRUN_GPQ_CONVERSIONin the python code to false if you want to get a sense of it. In the above benchmark running the Parquet with DuckDB without GPQ conversion at the end resulted in a time of .76 seconds.PARQUET_COMPRESSION- which compression to use for Parquet encoding. Note that not all processes support all compression options, and also the OGR converter currently ignores this option.SKIP_DUCK_GPKG- whether to skip the GeoPackage conversion option on DuckDB, since it takes a long time to run.
All contributions are welcome, I love running open source projects. I'm clearly just learning to code Python, so there's no judgement about crappy code. And I'm super happy to learn from others about better code. Feel free to sound in on the issues, make new ones, grab one, or make a PR. There's lots of low hanging fruit of things to add. And if you're just starting out programming don't hesitate to ask even basic things in the discussions.