ViSnow: Snow-covered Urban Roads Dataset for Computer Vision Applications
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
We present ViSnow: a large image dataset for snow-covered roads in an urban setting. The dataset includes an extensive collection of images from traffic surveillance cameras installed in Montreal, Quebec, Canada, during the winters of 2022 and 2023. ViSnow dataset aims to enable computer vision applications in intelligent transportation and winter road maintenance. ViSnow comprises 294,000 images describing various settings spanning day and night periods, different weather conditions (snow, rain, clear), and multiple urban areas (residential, commercial, industrial). We attach a metadata JSON file recording the timestamp and weather data to each image to provide more contextual information. ViSnow images are annotated to describe four snow cover classes: “clear surface”, “light-covered surface”, “medium-to-heavy-covered surface”, and “plowed surface”, matching possible snow removal operations.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.185 |
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