Assessing OSM building completeness for almost 13,000 cities globally
Bibliographic record
Abstract
OpenStreetMap (OSM) is an essential source for acquiring building data, although such data may suffer from quality issues. Many studies have focused on assessing OSM building data quality but few have been carried out on a global scale. This study aims to assess OSM building completeness (a quality measure) for 12,975 cities across the globe. This was achieved by employing population grid data as a proxy for reference building data. Not only the completeness of each city but also that of the grids within that city was assessed. The assessment results were evaluated based on calculating the overall accuracy and the r-square value between estimated and reference OSM building completeness values. Results showed that for 75% of cities, the completeness is lower than 20%; no more than 9% of cities have an estimated completeness higher than 80%. The overall accuracies of most countries were higher than 80%. The estimated completeness was also highly correlated with the reference completeness, which verifies the effectiveness of our approach. These results may be useful for acquiring and updating building data in OSM. A global and open dataset related to OSM building completeness has been made available for public use.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".