Assessing OSM building completeness for almost 13,000 cities globally
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
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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