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Smart Data Smart Cities & 3D GeoInfo
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Smart Data Smart Cities & 3D GeoInfo

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 6346

Special Issue Editors

Dr. Cynthia Changxin Wang

E-Mail Website
Guest Editor
School of Built Environment, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
Interests: BIM; ICT applications in construction industry; digital twins; construction sustainability; women in construction; construction health and safety
Special Issues, Collections and Topics in MDPI journals
Dr. Johnson Xuesong Shen

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
Interests: laser scanning technology; integrated systems; robotics and automation; automation and control engineering; construction engineering
Dr. Mitko Aleksandrov

E-Mail Website
Guest Editor
School of Built Environment, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
Interests: 3D GIS; 3D modelling; navigation; data structures; computational geometry

Special Issue Information

Dear Colleagues,

Data and data science play key role in informed decision-making for various businesses. 3D location data and 3D data analysis add more advantages for identification of problems, patterns and trends by location and time to make smarter decisions for our cities. Smart cities get benefits from data, data science, location data and analytics to provide accurate information for better decision making at local and regional scale. Sensing technologies and real time data collectors are also great tools for collecting different dimensions of data. In a smart cities era, all these data and information require appropriate collaborative and communication technologies for better management and sharing of data and information.

We invite contributions on (but not limited to) the following topics:

  • GIS, urban informatics, and smart cities 
  • Sensing technologies, laser scanning and smart cities 
  • BIM and infrastructure 
  • Data science, visualisation and City Analytics 
  • Mobility data and visualisation  
  • 3D/4D modelling of cities  
  • BIM/GIS integration and digital twins 
  • Derving insights from digital twins 
  • Digital twins levels of maturity   
  • Spatio-temporal patterns 
  • Smart cities during and after Covid-19  
  • Big data/big spatial data analysis and management   
  • ICT and smart cities 
  • Smart transportation  
  • Smart construction
  • Net zero emission cities
  • Smart homes 
  • Realtime/web based/interactive data visualisation 
  • Cities’ dashboards 
  • Smart Energy efficiency solutions  
  • Data and analytics for circular economy in cities 
  • Participation and empowerment 
  • Privacy, data security challenges in digital twins and smart cities 
  • Blockchain technology for municipal management 
  • Open data and open urban platforms 
  • Crowdsourcing data collection and analytics 
  • Monitoring systems 
  • Disaster management/warning systems 
  • Application of Artificial Intelligence (AI) and machine learning in smart cities 
  • Drones for monitoring/inspecting cities and construction sites 

Dr. Cynthia Changxin Wang
Dr. Johnson Xuesong Shen
Dr. Mitko Aleksandrov
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

20 pages, 5475 KiB  
Article
Progressive Model-Driven Approach for 3D Modeling of Indoor Spaces
by Ali Abdollahi, Hossein Arefi, Shirin Malihi and Mehdi Maboudi
Sensors 2023, 23(13), 5934; https://doi.org/10.3390/s23135934 - 26 Jun 2023
Cited by 1 | Viewed by 1032
Abstract
This paper focuses on the 3D modeling of the interior spaces of buildings. Three-dimensional point clouds from laser scanners can be considered the most widely used data for 3D indoor modeling. Therefore, the walls, ceiling and floor are extracted as the main structural [...] Read more.
This paper focuses on the 3D modeling of the interior spaces of buildings. Three-dimensional point clouds from laser scanners can be considered the most widely used data for 3D indoor modeling. Therefore, the walls, ceiling and floor are extracted as the main structural fabric and reconstructed. In this paper, a method is presented to tackle the problems related to the data including obstruction, clutter and noise. This method reconstructs indoor space in a model-driven approach using watertight predefined models. Employing the two-step implementation of this process, the algorithm is able to model non-rectangular spaces with an even number of sides. Afterwards, an “improvement” process increases the level of details by modeling the intrusion and protrusion of the model. The 3D model is formed by extrusion from 2D to 3D. The proposed model-driven algorithm is evaluated with four benchmark real-world datasets. The efficacy of the proposed method is proved by the range of [77%, 95%], [85%, 97%] and [1.7 cm, 2.4 cm] values of completeness, correctness and geometric accuracy, respectively. Full article
(This article belongs to the Special Issue Smart Data Smart Cities & 3D GeoInfo)
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14 pages, 4799 KiB  
Article
Quantification of Structural Defects Using Pixel Level Spatial Information from Photogrammetry
by Youheng Guo, Xuesong Shen, James Linke, Zihao Wang and Khalegh Barati
Sensors 2023, 23(13), 5878; https://doi.org/10.3390/s23135878 - 25 Jun 2023
Cited by 2 | Viewed by 945
Abstract
Aging infrastructure has drawn increased attention globally, as its collapse would be destructive economically and socially. Precise quantification of minor defects is essential for identifying issues before structural failure occurs. Most studies measured the dimension of defects at image level, ignoring the third-dimensional [...] Read more.
Aging infrastructure has drawn increased attention globally, as its collapse would be destructive economically and socially. Precise quantification of minor defects is essential for identifying issues before structural failure occurs. Most studies measured the dimension of defects at image level, ignoring the third-dimensional information available from close-range photogrammetry. This paper aims to develop an efficient approach to accurately detecting and quantifying minor defects on complicated infrastructures. Pixel sizes of inspection images are estimated using spatial information generated from three-dimensional (3D) point cloud reconstruction. The key contribution of this research is to obtain the actual pixel size within the grided small sections by relating spatial information. To automate the process, deep learning technology is applied to detect and highlight the cracked area at the pixel level. The adopted convolutional neural network (CNN) achieves an F1 score of 0.613 for minor crack extraction. After that, the actual crack dimension can be derived by multiplying the pixel number with the pixel size. Compared with the traditional approach, defects distributed on a complex structure can be estimated with the proposed approach. A pilot case study was conducted on a concrete footpath with cracks distributed on a selected 1500 mm × 1500 mm concrete road section. Overall, 10 out of 88 images are selected for validation; average errors ranging from 0.26 mm to 0.71 mm were achieved for minor cracks under 5 mm, which demonstrates a promising result of the proposed study. Full article
(This article belongs to the Special Issue Smart Data Smart Cities & 3D GeoInfo)
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15 pages, 9715 KiB  
Article
Occlusion Handling for Mobile AR Applications in Indoor and Outdoor Scenarios
by Muhammad Alfakhori, Juan Sebastián Sardi Barzallo and Volker Coors
Sensors 2023, 23(9), 4245; https://doi.org/10.3390/s23094245 - 24 Apr 2023
Cited by 1 | Viewed by 2040
Abstract
When producing an engaging augmented reality (AR) user experience, it is crucial to create AR content that mimics real-life objects’ behavior to the greatest extent possible. A critical aspect to achieve this is ensuring that the digital objects conform to line-of-sight rules and [...] Read more.
When producing an engaging augmented reality (AR) user experience, it is crucial to create AR content that mimics real-life objects’ behavior to the greatest extent possible. A critical aspect to achieve this is ensuring that the digital objects conform to line-of-sight rules and are either partially or completely occluded, just like real-world objects would be. The study explores the concept of utilizing a pre-existing 3D representation of the physical environment as an occlusion mask that governs the rendering of each pixel. Specifically, the research aligns a Level of Detail (LOD) 1 building model and a 3D mesh model with their real-world counterparts and evaluates the effectiveness of occlusion between the two models in an outdoor setting. Despite the mesh model containing more detailed information, the overall results do not show improvement. In an indoor scenario, the researchers leverage the scanning capability of HoloLens 2.0 to create a pre-scanned representation, which helps overcome the limited range and delay of the mesh reconstruction. Full article
(This article belongs to the Special Issue Smart Data Smart Cities & 3D GeoInfo)
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21 pages, 9407 KiB  
Article
Absolute IOP/EOP Estimation Models without Initial Information of Various Smart City Sensors
by Namhoon Kim, Sangho Baek and Gihong Kim
Sensors 2023, 23(2), 742; https://doi.org/10.3390/s23020742 - 09 Jan 2023
Cited by 2 | Viewed by 1696
Abstract
In smart cities, a large amount of optical camera equipment is deployed and used. Closed-circuit television (CCTV), unmanned aerial vehicles (UAVs), and smartphones are some examples of such equipment. However, additional information about these devices, such as 3D position, orientation information, and principal [...] Read more.
In smart cities, a large amount of optical camera equipment is deployed and used. Closed-circuit television (CCTV), unmanned aerial vehicles (UAVs), and smartphones are some examples of such equipment. However, additional information about these devices, such as 3D position, orientation information, and principal distance, is not provided. To solve this problem, the structured mobile mapping system point cloud was used in this study to investigate methods of estimating the principal point, position, and orientation of optical sensors without initial given values. The principal distance was calculated using two direct linear transformation (DLT) models and a perspective projection model. Methods for estimating position and orientation were discussed, and their stability was tested using real-world sensors. When the perspective projection model was used, the camera position and orientation were best estimated. The original DLT model had a significant error in the orientation estimation. The correlation between the DLT model parameters was thought to have influenced the estimation result. When the perspective projection model was used, the position and orientation errors were 0.80 m and 2.55°, respectively. However, when using a fixed-wing UAV, the estimated result was not properly produced owing to ground control point placement problems. Full article
(This article belongs to the Special Issue Smart Data Smart Cities & 3D GeoInfo)
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