Supporting Accessibility for Blind and Vision‐impaired People With a Localized Gazetteer and Open Source Geotechnology
Bibliographic record
Abstract
Abstract Disabled people, especially the blind and vision‐impaired, are challenged by many transitory hazards in urban environments such as construction barricades, temporary fencing across walkways, and obstacles along curbs. These hazards present a problem for navigation, because they typically appear in an unplanned manner and are seldom included in databases used for accessibility mapping. Tactile maps are a traditional tool used by blind and vision‐impaired people for navigation through urban environments, but such maps are not automatically updated with transitory hazards. As an alternative approach to static content on tactile maps, we use volunteered geographic information (VGI) and an Open Source system to provide updates of local infrastructure. These VGI updates, contributed via voice, text message, and e‐mail, use geographic descriptions containing place names to describe changes to the local environment. After they have been contributed and stored in a database, we georeference VGI updates with a detailed gazetteer of local place names including buildings, administrative offices, landmarks, roadways, and dormitories. We publish maps and alerts showing transitory hazards, including location‐based alerts delivered to mobile devices. Our system is built with several technologies including PHP, JavaScript, AJAX, Google Maps API, PostgreSQL, an Open Source database, and PostGIS, the PostgreSQL's spatial extension. This article provides insight into the integration of user‐contributed geospatial information into a comprehensive system for use by the blind and vision‐impaired, focusing on currently developed methods for geoparsing and georeferencing using a gazetteer.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".