# CityJSON: a compact and easy-to-use encoding of the CityGML data model

**Authors:** Hugo Ledoux, Ken Arroyo Ohori, Kavisha Kumar, Bal\'azs Dukai, and Anna Labetski, Stelios Vitalis

arXiv: 1902.09155 · 2020-01-27

## TL;DR

CityJSON is a new JSON-based format for city models that is more developer-friendly, compact, and suitable for web and mobile applications compared to the traditional CityGML XML format.

## Contribution

The paper introduces CityJSON, a simplified, efficient, and extensible JSON encoding of CityGML, addressing the drawbacks of the XML-based format.

## Key findings

- CityJSON achieves around six times compression on real-world datasets.
- It is easier to implement and use than CityGML for developers.
- Supports extensions for customizable data modeling.

## Abstract

The international standard CityGML is both a data model and an exchange format to store digital 3D models of cities. While the data model is used by several cities, companies, and governments, in this paper we argue that its XML-based exchange format has several drawbacks. These drawbacks mean that it is difficult for developers to implement parsers for CityGML, and that practitioners have, as a consequence, to convert their data to other formats if they want to exchange them with others. We present CityJSON, a new JSON-based exchange format for the CityGML data model (version 2.0.0). CityJSON was designed with programmers in mind, so that software and APIs supporting it can be quickly built. It was also designed to be compact (a compression factor of around six with real-world datasets), and to be friendly for web and mobile development. We argue that it is considerably easier to use than the CityGML format, both for reading and for creating datasets. We discuss in this paper the main features of CityJSON, briefly present the different software packages to parse/view/edit/create files (including one to automatically convert between the JSON and GML encodings), analyse how real-world datasets compare to those of CityGML, and we also introduce \emph{Extensions}, which allow us to extend the core data model in a documented manner.

## Full text

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## Figures

11 figures with captions in the complete paper: https://tomesphere.com/paper/1902.09155/full.md

## References

27 references — full list in the complete paper: https://tomesphere.com/paper/1902.09155/full.md

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Source: https://tomesphere.com/paper/1902.09155