CityJSON: a compact and easy-to-use encoding of the CityGML data model
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Abstract (arXiv)
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 Extensions, which allow us to extend the core data model in a documented manner.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2019.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2; 2025: 3; 2026: 2.
8 papers.
- 2021Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million buildings of the Netherlandscited by 14
- 2025TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Datasetcited by 5
- 2024Analyzing the impact of semantic LoD3 building models on image-based vehicle localizationcited by 3
- 2025CM2LoD3: Reconstructing LoD3 Building Models Using Semantic Conflict Mapscited by 2
- 2024Enriching thermal point clouds of buildings using semantic 3D building modelscited by 1
- 20253dSAGER: Geospatial Entity Resolution over 3D Objects (Technical Report)cited by 1
- 2026GS4City: Hierarchical Semantic Gaussian Splatting via City-Model Priorscited by 0
- 2026Remote-Sensing City Layout Extraction with MLLMcited by 0
- 2026Remote-Sensing City Layout Extraction with MLLM17 Aug 2026
- 2026GS4City: Hierarchical Semantic Gaussian Splatting via City-Model Priors13 Apr 2026
- 20253dSAGER: Geospatial Entity Resolution over 3D Objects (Technical Report)9 Nov 2025
- 2025CM2LoD3: Reconstructing LoD3 Building Models Using Semantic Conflict Maps21 Aug 2025
- 2025TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset12 May 2025
- 2024Enriching thermal point clouds of buildings using semantic 3D building models31 Jul 2024
- 2024Analyzing the impact of semantic LoD3 building models on image-based vehicle localization31 Jul 2024
- 2021Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million buildings of the Netherlands30 Dec 2021