Architectural Heat Maps: A Workflow for Synthesizing Data
Bibliographic record
Abstract
Over the last 5 years, large-scale ‘data dumps’ of architectural production have been made available online through project-specific websites (mainly competitions) and architectural aggregation/dissemination sites like Architizer, Suckerpunch, and Archinect. This reinforces the broader context of Ubiquitous Simultaneity, in which large amounts of data are continuously updated and easily accessed through a dizzying array of mobile devices. This condition is being exploited by sports leagues and financial speculators through the development of tools that collect, visualize, and analyze historical data for the purpose of producing speculative predictive simulations that could lead to strategies for enhanced performance. We explore the development of a workflow for deploying computer vision, SIFT algorithms, image aggregation, and heteromorphic deformation as a design strategy. These techniques have all been developed separately for various applications and here we combine them in such a way as to allow for the embedding of the historical and speculative artifacts of architectural production into newly formed three-dimensional architectural bodies. This work builds on past research, which resulted in a more two-dimensional image-based mapping and translation process found in existing imaging protocols for projects like Google Earth, and transitions towards the production of data-rich formal assemblies. Outliers and concentrations of visual data are exploited as a means to encourage innovation within the production of architecture.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
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".