Urban densification continues unabated, even as the possible consequences for users’ eye-level experiences remain unknown. This study addresses these consequences. In a laboratory setting, images of the NDSM wharf were shown to university students primed for one of three user groups: residents, visitors and passers-by. Their visual experiences were recorded using eye-tracking and analyzed in combination with surveys on self-reported appreciation and restorativeness. On-site surveys were also administered among real users. The results reveal distinct eye-movement patterns that point to the influence of environmental roles and tasks and how architectural qualities steer people’s visual experience, valence and restoration.
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Buildings with innovative technologies and architectural solutions are needed as a means of support for future nursing homes alongside adequate care services. This study investigated how various groups of stakeholders from healthcare and technology envision the nursing home of the future in the presumed perspective of residents, care professionals and technical staff. This qualitative study gathered data via ten simultaneous monodisciplinary focus group sessions with 95 professional stakeholders. The sessions yielded eight main themes: person and well-being; relatives and interaction; care technology; safety and security; interior design, architecture and the built environment; vision and knowledge; communication; and maintenance and operation. These themes can be used for programming future nursing homes, and for prioritising design and technological solutions. The views between the groups of stakeholders are to a large extent similar, and the personal needs of the residents are the most prominent factor for practice.
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This project assists architects and engineers to validate their strategies and methods, respectively, toward a sustainable design practice. The aim is to develop prototype intelligent tools to forecast the carbon footprint of a building in the initial design process given the visual representations of space layout. The prediction of carbon emission (both embodied and operational) in the primary stages of architectural design, can have a long-lasting impact on the carbon footprint of a building. In the current design strategy, emission measures are considered only at the final phase of the design process once major parameters of space configuration such as volume, compactness, envelope, and materials are fixed. The emission assessment only at the final phase of the building design is due to the costly and inefficient interaction between the architect and the consultant. This proposal offers a method to automate the exchange between the designer and the engineer using a computer vision tool that reads the architectural drawings and estimates the carbon emission at each design iteration. The tool is directly used by the designer to track the effectiveness of every design choice on emission score. In turn, the engineering firm adapts the tool to calculate the emission for a future building directly from visual models such as shared Revit documents. The building realization is predominantly visual at the early design stages. Thus, computer vision is a promising technology to infer visual attributes, from architectural drawings, to calculate the carbon footprint of the building. The data collection for training and evaluation of the computer vision model and machine learning framework is the main challenge of the project. Our consortium provides the required resources and expertise to develop trustworthy data for predicting emission scores directly from architectural drawings.
