open-buildings

Tools for working with open building datasets

• Free software: Apache Software License 2.0
• Documentation: https://opengeos.github.io/open-buildings
• Creator: Chris Holmes

Introduction

This repo is intended to be a set of useful scripts for working with Google's Open Buildings dataset, specifically to help translate it into Cloud Native Geospatial formats. The outputs will live at https://beta.source.coop/cholmes/google-open-buildings so most people can just make use of those directly. But these are intended to show the process, and then they've expanded to be a way to benchmark performance. It's an odd mix right now, if I have time I'll try to factor out an independent 'performance' CLI to compare processes without being specific to google open buildings and mixing in functionality like splitting multipolygons. The repo is now named 'open-buildings', to allow it to potentially grow to be a set of useful scripts to work with other open buildings datasets.

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.

Installation

Install with pip:

pip install open-buildings

This should add a CLI that you can then use. If it's working then:

open_buildings

Should print out a help message. You then should be able run the CLI:

open_buildings benchmark 36b_buildings.csv test-output-dir --format parquet

The only CSV files that will work are those from Google's Open Buildings dataset.

Functionality

So far there is just one 'tool', a CLI built with click that performs two functions:

• convert takes 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.
• benchmark runs 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 https://github.com/opengeos/open-buildings/issues/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 https://github.com/opengeos/open-buildings/issues/5 to track.

Format Notes

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.

Process Notes

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.

Code customizations

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 set RUN_GPQ_CONVERSION in 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.

Roadmap

The next tool to write is to add country and admin level 1 attributes from GeoBoundaries. This was the trickiest step in processing v2 buildings. This will be an interesting to benchmark, with the options being more like DuckDB and PostGIS (pandas could try but may not work on the biggest ones), and potentially even big query. The next functionality to add after that will be do spatial partitioning, and perhaps after that add Iceberg and Delta Lake and compare those two (I didn't get to that step with the v2 buildings). And perhaps I'll also add a tool to easily grab any data from the partitioned geoparquet on source.coop and get it in the format you want.

Contributing

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.