From LiDAR point cloud towards digital twin city: Clustering city objects based on Gestalt principles

Xue, Fan; Lu, Weisheng; Chen, Zhe; Webster, Chris

doi:10.1016/j.isprsjprs.2020.07.020

Cited by 122 publications

(42 citation statements)

References 61 publications

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“…In addition, Austin et al (2020) pointed out the benefits of combining machine learning techniques with semantic modeling to better handle large-sized and heterogeneous data [33]. While generating a model for the city scale is time-consuming, Xue et al proposed a method to reduce the time of developing the model out of LiDAR point clouds, but their method cannot be processed on unsymmetric objects, in addition to having geometric errors [39]. Utilizing point clouds for modeling conveys both potentials and challenges.…”

Section: Data Managementmentioning

confidence: 99%

“…In data management, for instance, one of the biggest concerns is the large-sized, complex, and heterogeneous nature of the city data [13,33,35,39,59]. In light of that, data acquisition and processing are threatened by the requirements of higher levels of computing powers and interoperability among the huge and various sets of data.…”

Section: Challenges To the Full Utilization Of City Digital Twin Potementioning

confidence: 99%

“…Visualizing the city scale in the digital twin is still lacking the comprehensiveness of the city information [66]. Several digital twin cities were developed that lack model accuracy, completeness, and graphical representation qualities [34,39,40,47]. In addition, utilizing participatory sensing and crowdsourced data to supplement limitations of sensory information revealed some localization errors and untrusted data [38,56,57].…”

Section: Challenges To the Full Utilization Of City Digital Twin Potementioning

confidence: 99%

See 2 more Smart Citations

City Digital Twin Potentials: A Review and Research Agenda

2021

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The city digital twin is anticipated to accurately reflect and affect the city’s functions and processes to enhance its realization, operability, and management. Although research on the city digital twin is still in its infancy, the advancement of the digital twin technology is growing fast and providing viable contributions to augmenting smart city developments. This study reviews the literature to identify the current and prospective potentials and challenges of digital twin cities. A research agenda is also proposed to guide future research on the city digital twincity digital twin to reach the utmost level of a comprehensive and complete city digital twin. Enhancing the efficiency of data processing, promoting the inclusion of socio-economic components of the city, and developing mutual integration between the two counterparts of the digital twin are proposed to be the future research directions to achieve and utilize a completely mirrored city digital twin.

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Section: Data Managementmentioning

confidence: 99%

Section: Challenges To the Full Utilization Of City Digital Twin Potementioning

confidence: 99%

Section: Challenges To the Full Utilization Of City Digital Twin Potementioning

confidence: 99%

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City Digital Twin Potentials: A Review and Research Agenda

2021

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“…It is a virtual representation of a device or a specific application scenario that can interact with the target environment to collect data continuously for real-time decision-making. Several successful research attempts include a disaster city DT [27,28] , energy management [29] , and city-scale Light Detection and Ranging (LiDAR) point clouds [30] . Furthermore, Singapore [31] and Germany [32] have launched the cityscale DT to monitor and improve utilities, which enhance the transparency, sustainability, and availability of a DT.…”

Section: Related Workmentioning

confidence: 99%

Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution

Pang

Huang

Xie

et al. 2021

Tsinghua Sci. Technol.

145

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The novel coronavirus, COVID-19, has caused a crisis that affects all segments of the population. As the knowledge and understanding of COVID-19 evolve, an appropriate response plan for this pandemic is considered one of the most effective methods for controlling the spread of the virus. Recent studies indicate that a city Digital Twin (DT) is beneficial for tackling this health crisis, because it can construct a virtual replica to simulate factors, such as climate conditions, response policies, and people's trajectories, to help plan efficient and inclusive decisions.However, a city DTsystem relies on long-term and high-quality data collection to make appropriate decisions, limiting its advantages when facing urgent crises, such as the COVID-19 pandemic. Federated Learning (FL), in which all clients can learn a shared model while retaining all training data locally, emerges as a promising solution for accumulating the insights from multiple data sources efficiently. Furthermore, the enhanced privacy protection settings removing the privacy barriers lie in this collaboration. In this work, we propose a framework that fused city DT with FL to achieve a novel collaborative paradigm that allows multiple city DTs to share the local strategy and status quickly. In particular, an FL central server manages the local updates of multiple collaborators (city DTs), providing a global model that is trained in multiple iterations at different city DT systems until the model gains the correlations between various response plans and infection trends. This approach means a collaborative city DT paradigm fused with FL techniques can obtain knowledge and patterns from multiple DTs and eventually establish a "global view" of city crisis management. Meanwhile, it also helps improve each city's DT by consolidating other DT's data without violating privacy rules. In this paper, we use the COVID-19 pandemic as the use case of the proposed framework. The experimental results on a real dataset with various response plans validate our proposed solution and demonstrate its superior performance.

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“…In recent years, lidars have been widely used in autonomous driving [ 1 , 2 , 3 ], aerospace [ 4 ], three-dimensional modeling [ 5 , 6 , 7 , 8 ], and other fields. In these applications, lidars provide the range map of the concerned object or environment, which can be further processed for geological mapping [ 9 , 10 , 11 ], simultaneous localization and mapping (SLAM) [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ], as well as 3D modeling and reconstruction, even for the modeling of human organs, detection and positioning of necrotic tissues, and other aspects in medicine [ 19 ]. Generally, the 3D structure of the concerned object is scanned by lidar and restored after point cloud scan registration, segmentation, and reconstruction.…”

Section: Introductionmentioning

confidence: 99%

PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data

Cheng

Zhao

Zhang

et al. 2021

Sensors

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Due to the complexity of surrounding environments, lidar point cloud data (PCD) are often degraded by plane noise. In order to eliminate noise, this paper proposes a filtering scheme based on the grid principal component analysis (PCA) technique and the ground splicing method. The 3D PCD is first projected onto a desired 2D plane, within which the ground and wall data are well separated from the PCD via a prescribed index based on the statistics of points in all 2D mesh grids. Then, a KD-tree is constructed for the ground data, and rough segmentation in an unsupervised method is conducted to obtain the true ground data by using the normal vector as a distinctive feature. To improve the performance of noise removal, we propose an elaborate K nearest neighbor (KNN)-based segmentation method via an optimization strategy. Finally, the denoised data of the wall and ground are spliced for further 3D reconstruction. The experimental results show that the proposed method is efficient at noise removal and is superior to several traditional methods in terms of both denoising performance and run speed.

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From LiDAR point cloud towards digital twin city: Clustering city objects based on Gestalt principles

Cited by 122 publications

References 61 publications

City Digital Twin Potentials: A Review and Research Agenda

City Digital Twin Potentials: A Review and Research Agenda

Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution

PCA-Based Denoising Algorithm for Outdoor Lidar Point Cloud Data

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