2024 • In Kolbe, Thomas H.; Donaubauer, Andreas; Beil, Christof (Eds.) Recent Advances in 3D Geoinformation Science - Proceedings of the 18th 3D GeoInfo Conference
Digital twin; Semantic point cloud; Semantic segmentation; 3D city model; Urban simulations
Abstract :
[en] Digital Twins (DTs) for cities represent a new trend for city planning and management, enhancing three-dimensional modeling and simulation of cities. While progress has been made in this research field, the current scientific literature mainly focuses on the use of semantically segmented point clouds to develop 3D city models for DTs. However, this study discusses a new reflection that argues on directly integrating the results of semantic segmentation to create the skeleton of the DTs and uses enriched semantically segmented point clouds to perform targeted simulations without generating 3D models. The paper discusses to what extent enriched semantic 3D point clouds can replace semantic 3D city models in the DTs scope. Ultimately, this research aims to reduce the cost and complexity of 3D modeling to fit some DTs requirements and address its specific needs. New perspectives are set to tackle the challenges of using semantic 3D point clouds to implement DTs for cities.
Ballouch, Zouhair ✱; Université de Liège - ULiège > Sphères ; College of Geomatic Sciences and Surveying Engineering, Hassan II Institute of Agronomy and Veterinary Medicine, Rabat, Morocco
Hajji, Rafika ; Université de Liège - ULiège > Département de géographie > Geospatial Data Science and City Information Modelling (GeoScITY) ; College of Geomatic Sciences and Surveying Engineering, Hassan II Institute of Agronomy and Veterinary Medicine, Rabat, Morocco
Billen, Roland ; Université de Liège - ULiège > Département de géographie > Geospatial Data Science and City Information Modelling (GeoScITY)
✱ These authors have contributed equally to this work.
Language :
English
Title :
Enriched Semantic 3D Point Clouds: An Alternative to 3D City Models for Digital Twin for Cities?
Publication date :
2024
Event name :
International 3D GeoInfo Conference
Event organizer :
Technical University of Munich
Event date :
12-09-2023 => 14-09-2023
Audience :
International
Main work title :
Recent Advances in 3D Geoinformation Science - Proceedings of the 18th 3D GeoInfo Conference
Editor :
Kolbe, Thomas H.
Donaubauer, Andreas
Beil, Christof
Publisher :
Springer Science and Business Media Deutschland GmbH
Alexiou E et al (2017) Towards subjective quality assessment of point cloud imaging in augmented reality. In: 2017 IEEE 19th international workshop on multimedia signal processing (MMSP), pp 1–6. https://doi.org/10.1109/MMSP.2017.8122237
Alva P et al (2022) Use cases for district-scale urban digital twins. Int Arch Photogramm Remote Sens Spatial Inf Sci XLVIII-4/W4-2022:5–12. https://doi.org/10.5194/isprs-archives-XLVIII-4-W4-2022-5-2022.
Bacher U (2022) Hybrid aerial sensor data as basis for a geospatial digital twin. Int Arch Photogramm Remote Sens Spatial Inf Sci XLIII-B4-2022:653–659. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-653-2022
Badenko V et al (2020) Airborne LIDAR data processing for smart city modelling. In: Anatolijs B et al (eds) Proceedings of EECE 2019. Springer International Publishing, Cham, pp 245–252. https://doi.org/10.1007/978-3-030-42351-3_21
Ballouch Z et al (2022) Toward a deep learning approach for automatic semantic segmentation of 3D lidar point clouds in urban areas. In: Barramou F et al (eds) Geospatial intelligence: applications and future trends. Springer International Publishing, Cham, pp 67–77
Ballouch Z, Hajji R (2021) Semantic segmentation of airborne LiDAR data for the development of an urban 3D model. In: Building information modeling for a smart and sustainable urban space. John Wiley & Sons, Ltd., pp 113–130. https://doi.org/10.1002/9781119885474.ch7
Ballouch Z et al (2022) A prior level fusion approach for the semantic segmentation of 3D point clouds using deep learning. Remote Sens 14(14):3415. https://doi.org/10.3390/rs14143415
Beil C et al (2021) Integration of 3D point clouds with semantic 3D city models—providing semantic information beyond classification. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci VIII-4/ W2-2021:105–112. https://doi.org/10.5194/isprs-annals-VIII-4-W2-2021-105-2021
Biljecki F et al (2015) Applications of 3D city models: state of the art review. IJGI 4(4):2842–2889. https://doi.org/10.3390/ijgi4042842
Deng T et al (2021b) A systematic review of a digital twin city: a new pattern of urban governance toward smart cities. J Manage Sci Eng 6(2):125–134. https://doi.org/10.1016/j.jmse.2021.03.003
Deng M et al (2021a) From BIM to digital twins: a systematic review of the evolution of intelligent building representations in the AEC-FM industry. J Inf Technol Constr 26:58–83. https://doi. org/10.36680/j.itcon.2021.005
Dimitrov H, Petrova-Antonova D (2021) 3D city model as a first step towards digital twin of Sofia City. Presented at the international archives of the photogrammetry, remote sensing and spatial information sciences—ISPRS archives. https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-23-2021
Döllner J (2020) Geospatial artificial intelligence: potentials of machine learning for 3D point clouds and geospatial digital twins. PFG 88(1):15–24. https://doi.org/10.1007/s41064-020-00102-3
Dukai B et al (2020) Generating, storing, updating and disseminating a countrywide 3D model. Int Arch Photogramm Remote Sens Spat Inf Sci XLIV-4/W1-2020:27–32. https://doi.org/10.5194/isprs-archives-XLIV-4-W1-2020-27-2020
Dukai B et al (2019) A multi-height lod1 model of all buildings in the Netherlands. ISPRS Ann Photogram Remote Sens Spat Inf Sci IV-4-W8:51–57. https://doi.org/10.5194/isprs-annals-IV-4-W8-51-2019
Dukai B et al (2021) Quality assessment of a nationwide data set containing automatically reconstructed 3D building models. Int Arch Photogram Remote Sens Spat Inf Sci XLVI-4-W4-2021:17–24. https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-17-2021
Ellul C et al (2022) Location-enabled digital twins—understanding the role of NMCAS in a European context. ISPRS Ann Photogramm Remote Sens Spat Inf Sci X-4/W2-2022:53–60. https://doi.org/10.5194/isprs-annals-X-4-W2-2022-53-2022
Eriksson H et al (2020) Requirements, development, and evaluation of a national building standard— a Swedish case study. ISPRS Int J Geo-Inf 9(2):78. https://doi.org/10.3390/ijgi9020078
Ferré-Bigorra J et al (2022) The adoption of urban digital twins. Cities 131:103905. https://doi.org/10.1016/j.cities.2022.103905
Girindran R et al (2020) On the reliable generation of 3D city models from open data. Urb Sci 4(4):47. https://doi.org/10.3390/urbansci4040047
Gobeawan L et al (2018) Modeling trees for virtual Singapore: from data acquisition to CITYGML models. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci XLII-4/W10:55–62. https://doi.org/10.5194/isprs-archives-XLII-4-W10-55-2018
Guth PL et al (2021) Digital elevation models: terminology and definitions. Remote Sens 13(18):3581. https://doi.org/10.3390/rs13183581
Hackel T et al (2017) Semantic3D.net: a new large-scale point cloud classification benchmark. arXiv:1704.03847 [cs]
Hämäläinen M (2021) Urban development with dynamic digital twins in Helsinki city. IET Smart Cities 3(4):201–210. https://doi.org/10.1049/smc2.12015
Hellmuth R (2022) Update approaches and methods for digital building models—literature review. ITcon 27:191–222. https://doi.org/10.36680/j.itcon.2022.010
Hristov PO et al (2022) Enabling city digital twins through urban living labs. Int Arch Photogramm Remote Sens Spat Inf Sci XLIII-B1-2022:151–156. https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-151-2022
Hu Q et al (2020) RandLA-Net: efficient semantic segmentation of large-scale point clouds. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR). IEEE, Seattle, WA, USA, pp 11105–11114. https://doi.org/10.1109/CVPR42600.2020.01112
Hu Q et al (2021) Towards semantic segmentation of urban-scale 3D point clouds: a dataset, benchmarks and challenges. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR). IEEE, Nashville, TN, USA, pp 4975–4985. https://doi.org/10.1109/CVPR46437. 2021.00494
Huang J et al (2022) City3D: large-scale building reconstruction from airborne LiDAR Point clouds. Remote Sens 14:9. https://doi.org/10.3390/rs14092254
Ketzler B et al (2020) Digital twins for cities: a state of the art review. Built Environ 46:547–573. https://doi.org/10.2148/benv.46.4.547
Kharroubi A et al (2022) Three dimensional change detection using point clouds: a review. Geomatics 2(4):457–485. https://doi.org/10.3390/geomatics2040025
Kong G et al (2022) Automatic building outline extraction from ALS point cloud data using generative adversarial network. Geocarto Int 37(27):15964–15981. https://doi.org/10.1080/10106049. 2022.2102246
Kutzner T et al (2020) CityGML 3.0: new functions open up new applications. PFG 88(1):43–61. https://doi.org/10.1007/s41064-020-00095-z
Ledoux H et al (2021) 3dfier: automatic reconstruction of 3D city models. J Open Source Softw 6(57):2866. https://doi.org/10.21105/joss.02866
Lehner H, Dorffner L (2020) Digital geoTwin Vienna: towards a digital twin city as Geodata hub. PFG J Photogramm Remote Sens Geoinf Sci 88(1):63
Lehtola VV et al (2022) Digital twin of a city: review of technology serving city needs. Int J Appl Earth Observ Geoinf 102915. https://doi.org/10.1016/j.jag.2022.102915
Lei B et al (2022) Assessing and benchmarking 3D city models. Int J Geogr Inf Sci. https://doi.org/10.1080/13658816.2022.2140808
León-Sánchez C et al (2021) Testing the new 3D bag dataset for energy demand estimation of residential buildings. Int Arch Photogramm Remote Sens Spat Inf Sci XLVI-4/W1-2021:69–76. https://doi.org/10.5194/isprs-archives-XLVI-4-W1-2021-69-2021
Li Y et al (2021) Deep learning for LiDAR point clouds in autonomous driving: a review. IEEE Trans Neural Netw Learn Syst 32(8):3412–3432. https://doi.org/10.1109/TNNLS.2020.3015992
Liamis T, Mimis A (2022) Establishing semantic 3D city models by GRextADE: the case of the Greece. J Geovis Spat Anal 6(1):15. https://doi.org/10.1007/s41651-022-00114-0
Lu Q et al (2019) Developing a dynamic digital twin at building and city levels: A case study of the West Cambridge campus. J Manage Eng 36. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000763
Masoumi H et al (2023) City digital twins: their maturity level and differentiation from 3D city models. Big Earth Data 0(0):1–46. https://doi.org/10.1080/20964471.2022.2160156
Mylonas G et al (2021) Digital twins from smart manufacturing to smart cities: a survey. IEEE Access, 1–1. https://doi.org/10.1109/ACCESS.2021.3120843
Naserentin V, Logg A (2022) Digital twins for city simulation: automatic, efficient, and robust mesh generation for large-scale city modeling and simulation. http://arxiv.org/abs/2210.05250
Nguyen SH, Kolbe TH (2022) Path-tracing semantic networks to interpret changes in semantic 3D city models. ISPRS Ann Photogramm Remote Sens Spat Inf Sci X-4/W2-2022:217–224. https://doi.org/10.5194/isprs-annals-X-4-W2-2022-217-2022
Nurunnabi A et al (2022) Robust techniques for building footprint extraction in aerial laser scanning 3D point clouds. Int Arch Photogramm Remote Sens Spat Inf Sci XLVIII-3-W2-2022:43–50. https://doi.org/10.5194/isprs-archives-XLVIII-3-W2-2022-43-2022
Nys G-A et al (2020) Automatic 3D buildings compact reconstruction from Lidar point clouds. In: Presented at the international archives of the photogrammetry, remote sensing and spatial information sciences—ISPRS archives. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-473-2020
Oosterom P et al (2015) Massive point cloud data management: Design, implementation and execution of a point cloud benchmark. Comput Graph 49. https://doi.org/10.1016/j.cag.2015. 01.007
Ortega S et al (2021) Generating 3D city models from open LiDAR point clouds: advancing towards smart city applications. In: Mobasheri A (ed) Open source geospatial science for urban studies: the value of open geospatial data. Springer International Publishing, Cham, pp 97–116. https://doi.org/10.1007/978-3-030-58232-6_6
Pa-den I et al (2022) Towards automatic reconstruction of 3D city models tailored for urban flow simulations. Front Built Environ 8
Park Y, Guldmann J-M (2019) Creating 3D city models with building footprints and LIDAR point cloud classification: a machine learning approach. Comput Environ Urban Syst 75:76–89. https://doi.org/10.1016/j.compenvurbsys.2019.01.004
Perez-Perez Y et al (2021) Scan2BIM-NET: deep learning method for segmentation of point clouds for scan-to-BIM. J Constr Eng Manage 147(9):04021107. https://doi.org/10.1061/(ASCE)CO. 1943-7862.0002132
Peters R et al (2015) Visibility analysis in a point cloud based on the medial axis transform. In: Eurographics workshop on urban data modelling and visualization, 6 pages. https://doi.org/10. 2312/UDMV.20151342
Peters R et al (2022) Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million buildings of the Netherlands. Photogramm Eng Remote Sens 88(3):165–170. https://doi.org/10. 14358/PERS.21-00032R2
Poux F (2019) The smart point cloud: structuring 3D intelligent point data. https://doi.org/10.13140/RG.2.2.20457.75367
Poux F, Billen R (2019) Voxel-based 3D point cloud semantic segmentation: unsupervised geometric and relationship featuring vs deep learning methods. ISPRS Int J Geo-Inf 8(5):213. https://doi. org/10.3390/ijgi8050213
Pružinec F, Ďuračiová R (2022) A point-cloud solar radiation tool. Energies 15:7018. https://doi. org/10.3390/en15197018
Richter R (2018) Concepts and techniques for processing and rendering of massive 3D point clouds
Scalas A et al (2022) Potential of the geometric layer in urban digital twins. ISPRS Int J Geo-Inf 11(6):343. https://doi.org/10.3390/ijgi11060343
Schrotter G, Hürzeler C (2020) The digital twin of the city of Zurich for urban planning. PFG J Photogramm Rem Sens Geoinf Sci 88. https://doi.org/10.1007/s41064-020-00092-2
Soilán M et al (2020) 3D point cloud to BIM: semi-automated framework to define IFC alignment entities from MLS-acquired LiDAR data of highway roads. Remote Sens 12(14):2301. https://doi.org/10.3390/rs12142301
Soon KH, Khoo VHS (2017) CITYGML modelling for Singapore 3D national mapping. Int Arch Photogramm Remote Sens Spat Inf Sci XLII-4/W7:37–42. https://doi.org/10.5194/isprs-arc hives-XLII-4-W7-37-2017
Stojanovic V et al (2018) Towards the generation of digital twins for facility management based on 3D point clouds. Presented at the (2018)
Stoter JE et al (2020) State of the art in 3D city modelling: Six challenges facing 3D data as a platform. In: GIM international: the worldwide magazine for geomatics, vol 34
Stoter JE et al (2021) Digital twins: a comprehensive solution or hopeful vision? In: GIM international: the worldwide magazine for geomatics
Su Z et al (2022) Semantic segmentation of 3D point clouds based on high precision range search network. Remote Sens 14(22):5649. https://doi.org/10.3390/rs14225649
Toth C, Jóźków G (2016) Remote sensing platforms and sensors: a survey. ISPRS J Photogramm Remote Sens 115:22–36. https://doi.org/10.1016/j.isprsjprs.2015.10.004
Tutzauer P et al (2019) Semantic urban mesh enhancement utilizing a hybrid model. ISPRS Ann Photogramm Remote Sens Spat Inf Sci IV-2-W7:175–182. https://doi.org/10.5194/isprs-annals-IV-2-W7-175-2019
Wang Y et al (2019) A survey of mobile laser scanning applications and key techniques over urban areas. Remote Sens 11:13. https://doi.org/10.3390/rs11131540
Wilk Ł et al (2022) Semantic urban mesh segmentation based on aerial oblique images and point clouds using deep learning. Int Arch Photogramm Remote Sens Spat Inf Sci XLIII-B2-2022:485–491. https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-485-2022
Willenborg B et al (2018) Integration of semantic 3D city models and 3D mesh models for accuracy improvements of solar potential analyses. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci XLII-4/W10:223–230. https://doi.org/10.5194/isprs-archives-XLII-4-W10-223-2018
Würstle P et al (2022) Viability testing of game engine usage for visualization of 3D geospatial data with OGC standards. ISPRS Ann Photogramm Remote Sens Spat Inf Sci X-4/W2-2022:281– 288. https://doi.org/10.5194/isprs-annals-X-4-W2-2022-281-2022
Xue F et al (2020) From LiDAR point cloud towards digital twin city: clustering city objects based on Gestalt principles. ISPRS J Photogramm Remote Sens 167:418–431. https://doi.org/10.1016/j.isprsjprs.2020.07.020
Yan J et al (2019) Integration of 3D objects and terrain for 3D modelling supporting the digital twin. ISPRS Ann Photogramm Remote Sens Spat Inf Sci IV-4/W8:147–154. https://doi.org/10. 5194/isprs-annals-IV-4-W8-147-2019
Zhang G et al (2017) Point cloud based visibility analysis: first experimental results. In: Proceedings of the 20th AGILE conference on geographic information science
Zhou Y et al (2023) Sampling-attention deep learning network with transfer learning for large-scale urban point cloud semantic segmentation. Eng Appl Artif Intell 117:105554. https://doi.org/10. 1016/j.engappai.2022.105554
