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Study of Building Detection, Assessment, and Management: Based on Computer...
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Study of Building Detection, Assessment, and Management: Based on Computer and Information Technologies

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 20 October 2024 | Viewed by 1782

Special Issue Editors

Dr. Ting-Kwei Wang

E-Mail Website
Guest Editor
Department of Civil Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan
Interests: building information modeling; computer vision; augmented reality; generative design; blockchain
Dr. Pin-Chao Liao

E-Mail Website
Guest Editor
Department of Construction Management, School of Civil Engineering, Tsinghua University, Beijing 100084, China
Interests: safety and quality; neuromanagement; construction risks
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaowei Luo

E-Mail Website
Guest Editor
Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong 999077, China
Interests: construction digitalization; safety management; digital twin; construction automation; building energy management; life-cycle assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are excited to announce a Special Issue of Buildings that focuses specifically on the application of computer and information technologies in the field of building detection, assessment, and management.

Computer and information technology can automate the process of building detection, assessment, and management, which is essential for ensuring safety and efficiency in various domains. However, applying these technologies in complex and dynamic environments poses new challenges and risks that require innovative solutions and methods. To address these issues, this Special Issue invites original research on the utilization of computer and information technology in building detection, assessment, and management, with a focus on the progress, methodologies, and practical applications that drive innovation in this particular field.

Topics of interest include, but are not limited to, the following:

  • Automated building detection;
  • Advanced building assessment techniques;
  • Intelligent building management systems;
  • Data integration and decision support systems;
  • Real-time monitoring and emergency response.

Dr. Ting-Kwei Wang
Dr. Pin-Chao Liao
Dr. Xiaowei Luo
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. Buildings is an international peer-reviewed open access monthly 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.

Keywords

  • computer vision and remote sensing
  • building detection
  • deep learning
  • LiDAR
  • aerial images
  • mobile robot
  • performance assessment
  • sustainable building
  • building information modeling

Published Papers (2 papers)

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Research

12 pages, 9804 KiB  
Article
A Fast and Robust Safety Helmet Network Based on a Mutilscale Swin Transformer
by Changcheng Xiang, Duofen Yin, Fei Song, Zaixue Yu, Xu Jian and Huaming Gong
Buildings 2024, 14(3), 688; https://doi.org/10.3390/buildings14030688 - 05 Mar 2024
Viewed by 568
Abstract
Visual inspection of the workplace and timely reminders of unsafe behaviors (e.g, not wearing a helmet) are particularly significant for avoiding injuries to workers on the construction site. Video surveillance systems generate large amounts of non-structure image data on site for this purpose; [...] Read more.
Visual inspection of the workplace and timely reminders of unsafe behaviors (e.g, not wearing a helmet) are particularly significant for avoiding injuries to workers on the construction site. Video surveillance systems generate large amounts of non-structure image data on site for this purpose; however, they require real-time recognition automation solutions based on computer vision. Although various deep-learning-based models have recently provided new ideas for identifying helmets in traffic monitoring, few solutions suitable for industry applications have been discussed due to the complex scenarios of construction sites. In this paper, a fast and robust network based on a mutilscale Swin Transformer is proposed for safety helmet detection (FRSHNet) at construction sites, which contains the following contributions. Firstly, MAE-NAS with the variant of MobileNetV3’s MobBlock as a basic block is applied to implement feature extraction. Simultaneously, a multiscale Swin Transformer module is utilized to obtain the spatial and contexture relationships in the multiscale features. Subsequently, in order to meet the scheme requirements of real-time helmet detection, efficient RepGFPN are adopted to integrate refined multiscale features to form a pyramid structure. Extensive experiments were conducted on the publicly available Pictor-v3 and SHWD datasets. The experimental results show that FRSHNet consistently provided a favorable performance, outperforming the existing state-of-the-art models. Full article
(This article belongs to the Special Issue Study of Building Detection, Assessment, and Management: Based on Computer and Information Technologies)
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26 pages, 10994 KiB  
Article
Robust Building Identification from Street Views Using Deep Convolutional Neural Networks
by Robin Roussel, Sam Jacoby and Ali Asadipour
Buildings 2024, 14(3), 578; https://doi.org/10.3390/buildings14030578 - 21 Feb 2024
Viewed by 772
Abstract
Street view imagery (SVI) is a rich source of information for architectural and urban analysis using computer vision techniques, but its integration with other building-level data sources requires an additional step of visual building identification. This step is particularly challenging in architecturally homogeneous, [...] Read more.
Street view imagery (SVI) is a rich source of information for architectural and urban analysis using computer vision techniques, but its integration with other building-level data sources requires an additional step of visual building identification. This step is particularly challenging in architecturally homogeneous, dense residential streets featuring narrow buildings, due to a combination of SVI geolocation errors and occlusions that significantly increase the risk of confusing a building with its neighboring buildings. This paper introduces a robust deep learning-based method to identify buildings across multiple street views taken at different angles and times, using global optimization to correct the position and orientation of street view panoramas relative to their surrounding building footprints. Evaluating the method on a dataset of 2000 street views shows that its identification accuracy (88%) outperforms previous deep learning-based methods (79%), while methods solely relying on geometric parameters correctly show the intended building less than 50% of the time. These results indicate that previous identification methods lack robustness to panorama pose errors when buildings are narrow, densely packed, and subject to occlusions, while collecting multiple views per building can be leveraged to increase the robustness of visual identification by ensuring that building views are consistent. Full article
(This article belongs to the Special Issue Study of Building Detection, Assessment, and Management: Based on Computer and Information Technologies)
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