Research Topic

Big Visual Data Analytics in Built Environment Information Modelling

  • Submission closed.

About this Research Topic

Big data analytics appears as a prospective solution for tackling complex engineering and management problems in a transdisciplinary efficient way, yet it has been less adopted in current practices in the built environment. Recent interdisciplinary research and development focusing on the collection, process, and use of visual data has increased the scope of big data analysis with regard to integration with building/infrastructure information models. For example, big visual data can be used to generate as-built building information models, as well as to reflect the dynamic status of construction projects; and these can also support comparisons with as-designed building/infrastructure information models on discrepancies. In the meantime, challenges in the use of big visual data for built environment information modeling have been noticed, and these include
• It may take much time and effort on data filtering and processing if the dataset is very big,
• Cost-effectiveness needs to be well considered in terms of different data collection approaches and algorithms for data reconstruction,
• There is a lack of semantic information in data reconstructed model,
• The capacity and limitations of data fusion methods in dealing with different data sources, and
• The establishment of lifecycle-oriented business cases about solving problems on both engineering and management side.
The latest technological development has been providing new opportunities and possibilities for professionals to visually capture, identify and describe the actual status of the built environment. However, the captured images and videos may become less valuable if they are not properly used in a structured, organized, and localized way to form information models such as the building information model (BIM) for buildings and civil infrastructures. These models can support decisions and actions through tracking dynamic construction processes and activities, checking quantities and quality, identifying risks, in addition to dealing with other relevant issues on site. These models can also provide useful information for the design of the built environment upon repair, restoration, renewal, and redevelopment. Recent interdisciplinary development in the built environment and computer vision provides researchers with new opportunities to use big visual data for three-dimensional (3D) reconstruction of the built environment across its lifecycle stages. Since 3D reconstruction methods have been utilized to describe the geometric properties of buildings and civil infrastructures, it looks that big visual data analysis can form a promising methodology to enhance the design, construction, and operation of the built environment against technical challenges.

This Research Topic aims to bring original research into applied big visual data analytics to reflect the current status of technical adoption so as to support further research and development in the digital transformation on both engineering and management for the built environment.

With regard to the purpose of this Research Topic to inform academics and practitioners with the state-of-the-art research findings and advanced practical solutions to apply big visual data analytics, this Research Topic focuses on both engineering and management issues relating to technical enhancements. Potential topics include, but are not limited to:
• The use of visual data for BIM at various work stages,
• Practice-oriented evaluation of visual data capture technologies,
• The adoption of computer vision technologies in engineering and management,
• Visual data fusion in planning, design, construction, and operation (monitoring, assessment, and maintenance) of the built environment (buildings and civil infrastructures),
• The adoption of semantic approaches in big visual data analysis,
• Methodological research into semantic enrichment in built environment information models, and
• Recent advances in research and development.


Keywords: Big data analytics, Big visual data, Built environment, Computer vision, Digital built environment, Information modelling


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Big data analytics appears as a prospective solution for tackling complex engineering and management problems in a transdisciplinary efficient way, yet it has been less adopted in current practices in the built environment. Recent interdisciplinary research and development focusing on the collection, process, and use of visual data has increased the scope of big data analysis with regard to integration with building/infrastructure information models. For example, big visual data can be used to generate as-built building information models, as well as to reflect the dynamic status of construction projects; and these can also support comparisons with as-designed building/infrastructure information models on discrepancies. In the meantime, challenges in the use of big visual data for built environment information modeling have been noticed, and these include
• It may take much time and effort on data filtering and processing if the dataset is very big,
• Cost-effectiveness needs to be well considered in terms of different data collection approaches and algorithms for data reconstruction,
• There is a lack of semantic information in data reconstructed model,
• The capacity and limitations of data fusion methods in dealing with different data sources, and
• The establishment of lifecycle-oriented business cases about solving problems on both engineering and management side.
The latest technological development has been providing new opportunities and possibilities for professionals to visually capture, identify and describe the actual status of the built environment. However, the captured images and videos may become less valuable if they are not properly used in a structured, organized, and localized way to form information models such as the building information model (BIM) for buildings and civil infrastructures. These models can support decisions and actions through tracking dynamic construction processes and activities, checking quantities and quality, identifying risks, in addition to dealing with other relevant issues on site. These models can also provide useful information for the design of the built environment upon repair, restoration, renewal, and redevelopment. Recent interdisciplinary development in the built environment and computer vision provides researchers with new opportunities to use big visual data for three-dimensional (3D) reconstruction of the built environment across its lifecycle stages. Since 3D reconstruction methods have been utilized to describe the geometric properties of buildings and civil infrastructures, it looks that big visual data analysis can form a promising methodology to enhance the design, construction, and operation of the built environment against technical challenges.

This Research Topic aims to bring original research into applied big visual data analytics to reflect the current status of technical adoption so as to support further research and development in the digital transformation on both engineering and management for the built environment.

With regard to the purpose of this Research Topic to inform academics and practitioners with the state-of-the-art research findings and advanced practical solutions to apply big visual data analytics, this Research Topic focuses on both engineering and management issues relating to technical enhancements. Potential topics include, but are not limited to:
• The use of visual data for BIM at various work stages,
• Practice-oriented evaluation of visual data capture technologies,
• The adoption of computer vision technologies in engineering and management,
• Visual data fusion in planning, design, construction, and operation (monitoring, assessment, and maintenance) of the built environment (buildings and civil infrastructures),
• The adoption of semantic approaches in big visual data analysis,
• Methodological research into semantic enrichment in built environment information models, and
• Recent advances in research and development.


Keywords: Big data analytics, Big visual data, Built environment, Computer vision, Digital built environment, Information modelling


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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