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Material for the presentation given at FOSS4G 2019 – Working with 3D city models in Python

Tutorial notebook here

Slides here

Recording of the talk: https://doi.org/10.5446/43345

Magyar nyelvű diák itt

Working with 3D city models in Python

Abstract

Working with 3D city models is a pain. Think CityGML. This limits the adoption of the data model by software developers and these data sets end up sitting under-utilized in governmental repositories. We developed CityJSON and cjio which makes it a breeze to operate on 3D city models with Python and extract more value from the data.

Description

Semantic 3D city models are one of the cornerstones of the so-called "smart city" applications, yet they are very difficult to manipulate/edit/update. While they are relatively easy to generate, their use and maintenance is limited by the available software and the cumbersome data model. Have you ever tried to write a CityGML file? And to parse one? We did, and we didn't like it. Therefore we created a developer-friendly JSON implementation of the CityGML data model. This talk will introduce CityJSON its processing software cjio, which can be used as a CLI to chain operations, or its API can be used to generate features for machine learning.

Contents

The Jupyter notebook cjio_tutorial.ipynb is the main document. The slides are generated from the notebook and can be viewed online.

I recommend to run the notebook locally and play around with cjio a bit, since not all code is included in the slides. In order to execute the notebook, the easiest if you set up a virtual environment with Pipenv. After downloading this repository, navigate into the directory, create a virtual environment, install the packages and activate the virtual environment.

$ pipenv --python 3.6

$ pipenv install

$ pipenv shell

Run the notebook from the virtual environment.

jupyter lab cjio_tutorial.ipynb

I use the convert.sh script to convert the notebook into slides.

$ sh convert.sh cjio_tutorial.ipynb

Data

The minimal sample data sets are in the ./data directory, the images in the notebook are in ./figures.

The Zürich data set which is used in the notebook can be downloaded from here.

Additional city models in CityJSON format can be found at https://www.cityjson.org/datasets/