Cityjson + Web = Ninja

Vitalis, Stelios; Labetski, Anna; Boersma, Freek; Dahle, F.; Li, X.; Ohori, G.A.K. Arroyo; Ledoux, Hugo; Stoter, J.E.

doi:10.5194/isprs-annals-vi-4-w1-2020-167-2020

Cited by 11 publications

(7 citation statements)

References 11 publications

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“…For visualisation purposes, we inspect the original model in LoD2.2 using ninja (Vitalis et al, 2020) (Figure 5).…”

Section: Resultsmentioning

confidence: 99%

3d Change Detection for Semi-Automatic Update of Buildings in 3d City Models

Tamort,

Kharroubi,

Hajji

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Automatic update of 3D city models has become a crucial operation in the context of urban digital twins. It commonly relies on a reconstruction from ALS point clouds. However, frequent reconstructions due to a dynamic urban environment are resource-intensive and lack change information about the scene. In this paper, we present a novel framework which aims to combine an instance change detection approach based on a distance-computing algorithm, geometric features and thresholding with a building reconstruction algorithm to ensure an efficient geometric, semantic and thematic (change labeling) update of an existing 3D city model. This approach comprises three stages spanning from data preparation to the integration of change results in the updated model. First, we prepare our input data. Next, we assess the changes between the two epochs. This process involves two stages. New and lost buildings are extracted in the first, and the changed and unchanged in the other. The results of the change detection are evaluated using standard evaluation metrics. The evaluation results are encouraging considering the various sources of errors. Finally, unchanged buildings are kept in the model, while the changed and new ones are reconstructed using Geoflow3D. The final model is semantically augmented using a change attribute. Since the 3D city model undergoes an update rather than complete reconstruction, the tracking of both geometric and semantic changes of some buildings can be made possible through a versioning system. The change information can be leveraged in multiple applications like 3D cadastre, urban inventory, urban planning…

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“…For visualisation purposes, we inspect the original model in LoD2.2 using ninja (Vitalis et al, 2020) (Figure 5).…”

Section: Resultsmentioning

confidence: 99%

3d Change Detection for Semi-Automatic Update of Buildings in 3d City Models

Tamort,

Kharroubi,

Hajji

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…It emphasizes information like the length, area, and volume of the elements and the placement of the building on the earth. While visualization of the CityGML file could be performed with FZK viewer [54], JSON-based encoding is visualized and validated using another open-source tool, NINJA [57], in Figure 14.…”

Section: Quality Assessmentmentioning

confidence: 99%

Low-Cost Data, High-Quality Models: A Semi-Automated Approach to LOD3 Creation

Harshit,

Chaurasia,

Zlatanova

et al. 2024

IJGI

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In the dynamic realm of digital twin modeling, where advancements are swiftly unfolding, users now possess the unprecedented ability to capture and generate geospatial data in real time. This article delves into a critical exploration of this landscape by presenting a meticulously devised workflow tailored for the creation of Level of Detail 3 (LOD3) models. Our research methodology capitalizes on the integration of Apple LiDAR technology alongside photogrammetric point clouds acquired from Unmanned Aerial Vehicles (UAVs). The proposed process unfolds with the transformation of point cloud data into Industry Foundation Classes (IFC) models, which are subsequently refined into LOD3 Geographic Information System (GIS) models leveraging the Feature Manipulation Engine (FME) workbench 2022.1.2. This orchestrated synergy among Apple LiDAR, UAV-derived photogrammetric point clouds, and the transformative capabilities of the FME culminates in the development of precise LOD3 GIS models. Our proposed workflow revolutionizes this landscape by integrating multi-source point clouds, imbuing them with accurate semantics derived from IFC models, and culminating in the creation of valid CityGML LOD3 buildings through sophisticated 3D geometric operations. The implications of this technical innovation are profound. Firstly, it elevates the capacity to produce intricate infrastructure models, unlocking new vistas for modeling digital twins. Secondly, it extends the horizons of GIS applications by seamlessly integrating enriched Building Information Modeling (BIM) components, thereby enhancing decision-making processes and facilitating more comprehensive spatial analyses.

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“…Ninja Viewer is a lightweight and interactive web viewer for CityJSON files (Vitalis et al, 2020). It uses Three.js library to create 3D scenes in a web browser, allowing users to visualize and explore the geometry and semantics of 3D city models.…”

Section: Related Workmentioning

confidence: 99%

3d Modeling of Road Infrastructures According to Citygml 3.0 and Its Cityjson Encoding

Yarroudh,

Nys,

Hajji

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This study proposes a systematic approach for standardized 3D modeling of road infrastructures based on CityGML 3.0 and its CityJSON encoding. The approach involves generating a LoD2 3D model of the road infrastructure based on a semi-automatic extraction of linear features from mobile mapping LiDAR data. This enables geometric and semantic modeling of the roads. A codification system is proposed to assign predefined codes to each linear feature, allowing each section and intersection and each road surface to be modeled separately. The resulting model is converted to a CityJSON file, stored in a document-oriented database and visualized through a web application. The proposed approach provides a cost-effective alternative to traditional manual modeling methods while maintaining a high level of accuracy and consistency. It also considers the validation of data schema and geometric primitives to ensure that any non-conformity with CityJSON schemas, and any topological and/or geometric errors can be detected and then corrected. This is important since schema changes in new versions of CityJSON can result in compatibility issues, while geometric and topological errors can affect the accuracy of 3D models and ultimately lead to inaccurate simulation outcomes or analysis results.

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Cityjson + Web = Ninja

Cited by 11 publications

References 11 publications

3d Change Detection for Semi-Automatic Update of Buildings in 3d City Models

3d Change Detection for Semi-Automatic Update of Buildings in 3d City Models

Low-Cost Data, High-Quality Models: A Semi-Automated Approach to LOD3 Creation

3d Modeling of Road Infrastructures According to Citygml 3.0 and Its Cityjson Encoding

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