Quality Assessment of the OpenStreetMap Road Network in Calgary, Alberta
Bibliographic record
Abstract
Voluntary geographic information (VGI) platforms have rapidly grown in recent years due to the advancement of technologies, providing more and more accurate and up-to-date versions of geo-referenced data over large areas (Goodchild, 2007).Despite large quantities of geospatial data and many applications produced by VGI projects, users are often unaware of their quality.Among the various VGI projects on the Internet, OpenStreetMap (OSM) has achieved the highest popularity (Yan et al., 2020).OSM is a platform where people can voluntarily create or edit maps of various types, such as streets and roads, from all around the world.These maps are created by uploading GPS tracks or by tracing and converting features from high-resolution satellite images into digital form (Haklay and Weber, 2008).In the OSM database, road networks are one of the most frequently occurring spatial contents.However, these representations' quality can vary from location to location (Brovelli et al., 2017).In recent years, geospatial data quality in OSM has become an important research topic as a result of the large size of the dataset and multiple access channels (Flanagin and Metzger, 2008).Thus, the primary objective of this project is to examine the overall reliability of the OSM road network in Calgary.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".