AI4GEO: A Path From 3D Model to Digital Twin

Brunet, Pierre-Marie; Baillarin, Simon; Lassalle, Pierre; Weissgerber, Flora; Vallet, Bruno; Christophe, Triquet; Foulon, Gilles; Romeyer, Gaelle; Souille, Gwenael; Gabet, Laurent; Ferrero, Cedrik; Huynh, Thanh-Long; Lavergne, Emeric

doi:10.1109/igarss46834.2022.9883433

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…By acquiring land areas within two years, providing 4 bands (Blue, Green, Red, Near Infra Red) at 50 cm, the objective is to produce a global Digital Surface Model (DSM) with 1 m relative altimetric error (CE90) at 1 m ground sampling distance (GSD) as target accuracy. The worldwide production of this 3D information will notably make a real contribution to the creation of digital twins (Brunet et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Geometrically guided and confidence-based point cloud denoising

Youssefi,

Derksen,

Migel-Arachchige

et al. 2024

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

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Abstract. The generation of photogrammetric point clouds from satellite images is often based on image correlation techniques. Correlation errors can arise for a wide variety of reasons: transient objects, homogeneous areas, shadows, and surface discontinuities. Therefore, a simple 3D Gaussian distribution at the point cloud level is not an appropriate model. In this paper, we propose a new point cloud denoising method integrated into the Multiview Stereo Pipeline CARS, dedicated to satellite imagery. Building upon bilateral filtering principles, our approach introduces a novel utilization of color information, confidence estimation and geometric constraints alongside point positions and normals. While the use of point color increases the level of detail, the addition of geometric constraints and confidence awareness guides processing towards a realistic solution. We propose an ablation study and compare our solution against a previously established bilateral filter with LiDAR data as ground truth.

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Section: Introductionmentioning

confidence: 99%

Geometrically guided and confidence-based point cloud denoising

Youssefi,

Derksen,

Migel-Arachchige

et al. 2024

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

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Models for Digital Twins of Spatially Distributed Objects

Verkhova,

Akimov

2023

2023 v International Conference on Control in Technical Systems (CTS)

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The Future Possibilities and Security Challenges of City Digitalization

et al. 2022

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To adapt to current changes, such as globalization, climate change, and demographic growth, modern cities must embrace the digitalization of city management. In this paper, we examine a concept for digitalizing a city based on Rotterdam’s digital twin showcase. Data-processing frameworks for different sources of data are presented. Security risks and the potential of smart cities for military usage are discussed. Lastly, using an example of available datasets for air quality and public lighting consumption, functions of the Rotterdam digital twin are compared with the Brno city digital platform. It was found that every city has its approach to digitalization, and it is probably impossible to unify every city’s digitalization process. This means that the digitalization of the city is very individual. Both systems in their current form make it possible to visualize the collected data about the city. So far, however, these systems do not use advanced functions such as AI-assisted decision-making and prediction of various events in the city. Even so, they are a source of very interesting data that can be used by third parties.

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AI4GEO: A Path From 3D Model to Digital Twin

Cited by 3 publications

References 2 publications

Geometrically guided and confidence-based point cloud denoising

Geometrically guided and confidence-based point cloud denoising

Models for Digital Twins of Spatially Distributed Objects

The Future Possibilities and Security Challenges of City Digitalization

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