3DBM

3D Building Metrics. Elevating geometric analysis for urban morphology, solar potential, CFD etc to the next level 😉

Installation

You need to install all dependencies first:

pip install -r requirements.txt

Then take your time and install pymesh.

Wat is het?

A cool script that computes a lot metrics from 3D geometries (mostly intended for buildings).

The following metrics are computed:

Type	Metrics
Geometric Properties	Number of vertices, Number of surfaces, Number of vertices by semantic type (i.e. ground, roof, wall), Number of surfaces by semantic type (i.e. ground, roof, wall), Min/Max/Range/Mean/Median/Std/Mode height
Derived Properties	Footprint perimeter, Volume, Volume of convex hull, Volume of Object-Oriented Bounding Box, Volume of Axis-Oriented Bounding Box, Volume of voxelised building, Length and width of the Object-Oriented Bounding Box, Surface area, Surface area by semantic surface, Horizontal elongation, Min/Max vertical elongation, Form factor
Spatial distribution	Shared walls, Nearest neighbour
Shape indices	Circularity/Hemisphericality^, Convexity 2D/3D^, Fractality 2D/3D^, Rectangularity/Cuboidness^, Squareness/Cubeness^, Cohesion 2D/3D^, Proximity 2D/3D⁺, Exchange 2D/3D⁺, Spin 2D/3D⁺, Perimeter/Circumference^, Depth 2D/3D⁺, Girth 2D/3D⁺, Dispersion 2D/3D^x, Range 2D/3D^, Equivalent Rectangular/Cuboid^*, Roughness^x

^* formula-based index, size-independent by definition
⁺ index based on interior grid points (discretised), normalised
^x index based on surface grid points (discretised), normalised

Omg, how amazing! Any issues?

Yeah:

It works with only MultiSurface and Solid (the latter, only for the first shell)
It only parses the first geometry
Expects semantic surfaces

How?

Running it, saving it, and including a val3dity report:

python cityStats.py [file_path] -o [output.csv] [-v val3dity_report.json]

Default is single-threaded, define the number of threads with:

python cityStats.py [file_path] -j [number]

Visualising a specific building, which can help with troubleshooting:

python cityStats.py [file_path] -p -f [unique_id]

Running multiple files in a folder and checking with val3dity (make sure you have val3dity installed):

for i in *.json; do val3dity $i --report "${i%.json}_v3.json"; python cityStats.py $i -o "${i%.json}.csv" -v "${i%.json}_v3.json"; done

Can I visualise a model?

Tuurlijk! Just:

python cityPlot.py [file_path]

Tutorial please!

Download or git clone this repository.
Install all dependencies: pip install -r requirements.txt.
Download a tile from 3D BAG: wget --header='Accept-Encoding: gzip' https://data.3dbag.nl/cityjson/v210908_fd2cee53/3dbag_v210908_fd2cee53_5910.json
Run the stats on the data: python cityStats.py 3dbag_v210908_fd2cee53_5910.json -o 5910.csv
The resutling file 5910.csv contains all metrics computed for this tile.

You may also run this with a val3dity report. You may download the val3dity report as a json file from the aforementioned website. Assuming the report's filename is report.json you can run:

python cityStats.py 3dbag_v210908_fd2cee53_5910.json -v report.json -o 5910.csv

Then the result will contain more info related to the validation of geometries.

If you use 3DBM in a scientific context, please cite this article:

Anna Labetski, Stelios Vitalis, Filip Biljecki, Ken Arroyo Ohori & Jantien Stoter (2023): 3D building metrics for urban morphology. International Journal of Geographical Information Science, 37(1): 36-67. DOI: 10.1080/13658816.2022.2103818

Article available here.

@article{Labetski2023,
	Author = {Anna Labetski and Stelios Vitalis and Filip Biljecki and Ken {Arroyo Ohori} and Jantien Stoter},
	Title = {{3D} building metrics for urban morphology},
	Journal = {International Journal of Geographical Information Science},
	Volume = {37},
	Number = {1},
	Pages = {36-67},
	Year  = {2023},
	Publisher = {Taylor & Francis},
	Doi = {10.1080/13658816.2022.2103818}

tudelft3d / 3d-building-metrics