A 360-degree imagery-multisensor system for visualizing environmental parameters in architecture and urban spaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research has designed a 360-degree imagery-multisensor system aiming to capture and visualize environmental parameters in architecture and urban spaces. Unlike existing tools, this system enables simultaneous recording of both imagery and non-imagery environmental data, including lighting, thermal, air quality, sound, and physical space parameters, within a 360-degree field of view. Lighting conditions are captured using panoramic high dynamic range imagery, complemented by a 360-degree array of sensors measuring illuminance levels and spectral power distribution. Thermal and air quality conditions are recorded using 360-degree thermal imagery, combined with hygrometers and air particle meters. Sound levels are also monitored across the full 360-degree field. The system is built using 3D printing technologies and Raspberry Pi computers, equipped with various sensor modules. Custom Python scripts enable real-time data capture, processing, and visualization. This cost-effective, easy-to-manufacture, programmable, and customizable innovation is aimed at students and educators in design and architecture, as well as building engineers. Furthermore, integrating imagery and sensor data supports the development of immersive virtual and augmented reality applications, offering new opportunities for education and the exploration of effective design solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it