A framework for performance analysis of OpenStreetMap data in navigation applications: the case of a well-developed road network in Australia
Bibliographic record
Abstract
Although much effort has been put into assessing and improving OpenStreetMap (OSM) data quality, further research is required to determine its reliability and robustness for real-world applications. This study introduces a framework, built on open-source geospatial tools, for analysing the performance of OSM road data across different navigation applications. We tested this framework on an extensive 41,000 km road network in Australia. While our findings generally supported the quality of the OSM dataset, analyses of census data revealed two key relationships: first, a significant link between a city’s population and the quality of its OSM data, and second, a strong influence of Information and Communications Technology (ICT) infrastructure on OSM data development. Furthermore, while navigation tests showed that OSM road networks performed reasonably well, scenario analyses highlighted several issues: a strong correlation between data quality and navigation accuracy; a negative impact of distance on OSM-based route accuracy for long inner- and inter-city routes due to accumulated errors; and the tendency of OSM to suggest sub-optimal paths for routes to isolated locations. This framework offers valuable benefits to a wide range of users. The OSM community can use it to assess data quality before application; individuals and businesses can easily evaluate its utility for navigation and route-planning; and local governments can benefit from improved quality control, particularly for projects involving Connected and Automated Vehicles (CAVs). Finally, the framework’s capacity to design and analyse various routing scenarios provides new insights into overall road network quality.
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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 | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".