Built Environment Design Under the Pressure of GenAI: Context Beyond the Traditional Dataset
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Architectural Design, Urban Science, and Real Estate".
Deadline for manuscript submissions: 10 August 2027 | Viewed by 58
Editors
Interests: architecture; GenAI; computational design; special justice; inclusivity in the built environment; design under uncertainty; sustainability
Interests: architecture; sustainability; building engineering; sustainability assessment; sustainable development; GenAI
Special Issue Information
Dear Colleagues,
GenAI systems are trained on built environments that are formally documented, georeferenced, and stable. That describes a minority of the contexts in which design decisions are most urgently needed.
This Special Issue examines GenAI's application at the edge of legibility, spatial conditions where the site is under-documented, unstable, politically contested, or disappearing. Informal settlements and slums exist outside the data registers machine learning pipelines consume. Post-disaster and climate-affected environments are physically transformed faster than documentation can follow. Conflict urbanism renders spatial data restricted, incomplete, or weaponized. Climate displacement and managed retreat require designing destinations for communities whose origin sites will cease to exist. Economic precarity and urban shrinkage produce rapid informalization of formerly documented fabric. In each case, the pressure to design is highest precisely where AI's generative capacity is least calibrated.
The risks are consistent across conditions: standardized forms imposed on culturally specific communities, spatial proposals generated with algorithmic confidence from distorted or absent data, and the severing of relationships between spatial practice and social identity. Therefore a core tension arises between the speed that these conditions demand and the cultural continuity they require.
Papers are invited on case studies across these contexts, ethical frameworks for AI-assisted design under uncertainty, alternative data practices including counter-mapping and community-generated datasets, and design methodologies that operate where conventional training data ends.
Dr. Morteza Hazbei
Dr. Sherif Goubran
Dr. Gideon Abagna Azunre
Guest Editors
Manuscript Submission Information
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Keywords
- generative AI
- built environment
- informal settlements
- spatial data gaps
- post-disaster reconstruction
- conflict urbanism
- context-sensitive design
- climate displacement
- counter-mapping
- community-generated data
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