CityJSON Building Generation from Airborne LiDAR 3D Point Clouds

Nys, Gilles-Antoine; Poux, Florent; Billen, Roland

doi:10.3390/ijgi9090521

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CityJSON Building Generation from Airborne LiDAR 3D Point Clouds

Nys, Gilles-Antoine; Poux, Florent; Billen, Roland

2020 • In ISPRS International Journal of Geo-Information, 9 (521)

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https://hdl.handle.net/2268/250504

DOI
10.3390/ijgi9090521

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Keywords :

LiDAR; 3D city models; CityJSON; Smart Cities; Point Cloud; Segmentation; 3D Modeling

Abstract :

[en] The relevant insights provided by 3D City models greatly improve Smart Cities and their management policies. In the urban built environment, buildings frequently represent the most studied and modeled features. CityJSON format proposes a lightweight and developer-friendly alternative to CityGML. This paper proposes an improvement to the usability of 3D models providing an automatic generation method in CityJSON, to ensure compactness, expressivity, and interoperability. In addition to a compliance rate in excess of 92% for geometry and topology, the generated model allows the handling of contextual information, such as metadata and refined levels of details (LoD), in a built-in manner. By breaking down the building-generation process, it creates consistent building objects from the unique source of Light Detection and Ranging (LiDAR) point clouds.

Research center :

Geomatics Unit

Disciplines :

Computer science

Author, co-author :

Nys, Gilles-Antoine ; Université de Liège - ULiège > Département de géographie > Unité de Géomatique - Topographie et géométrologie

Poux, Florent ; Université de Liège - ULiège > Département de géographie > Unité de Géomatique - Topographie et géométrologie

Billen, Roland ; Université de Liège - ULiège > Département de géographie > Unité de Géomatique - Topographie et géométrologie

Language :

English

Title :

CityJSON Building Generation from Airborne LiDAR 3D Point Clouds

Publication date :

31 August 2020

Journal title :

ISPRS International Journal of Geo-Information

eISSN :

2220-9964

Publisher :

MDPI AG, Basel, Switzerland

Special issue title :

Automatic Feature Recognition from Point Clouds

Volume :

Issue :

521

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 01 September 2020

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