Place identity: a generative AI’s perspective
Bibliographic record
Abstract
Do cities have a collective identity? The latest advancements in generative artificial intelligence (AI) models have enabled the creation of realistic representations learned from vast amounts of data. In this study, we test the potential of generative AI as the source of textual and visual information in capturing the place identity of cities assessed by filtered descriptions and images. We asked questions on the place identity of 64 global cities to two generative AI models, ChatGPT and DALL·E2. Furthermore, given the ethical concerns surrounding the trustworthiness of generative AI, we examined whether the results were consistent with real urban settings. In particular, we measured similarity between text and image outputs with Wikipedia data and images searched from Google, respectively, and compared across cases to identify how unique the generated outputs were for each city. Our results indicate that generative models have the potential to capture the salient characteristics of cities that make them distinguishable. This study is among the first attempts to explore the capabilities of generative AI in simulating the built environment in regard to place-specific meanings. It contributes to urban design and geography literature by fostering research opportunities with generative AI and discussing potential limitations for future studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".