Synthesis of an Open Building Dataset Enabling Accurate Millimetre-Wave Propagation Simulation
Bibliographic record
Abstract
This paper introduces a method for creating a comprehensive, free, and open 3D vector building dataset, removing the financial barriers of procuring expensive commercially available geodata. The maps generated by this method were used to enhance simulation of millimetre-wave propagation by more accurately modelling the physical environment. To generate this dataset, several free and open sources of building data were evaluated and compared with commercially available geodata derived from aerial LiDAR scanning. The data from these sources were combined such that data from the most precise source available was used everywhere possible, and the gaps in these data sets were filled in with data from the next-best available source. The building data was then used to perform millimetre-wave path loss predictions using the ITU-R P. 1411 propagation model, the results of which were compared to path loss measurements. It was found that the path loss predictions using the free building dataset produced similar levels of error relative to measurement as the predictions using the commercial geodata.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".