Systematic Street View Sampling: High Quality Annotation of Power Infrastructure in Rural Ontario
Bibliographic record
Abstract
Google Street View and the emergence of self-driving vehicles afford an unprecedented capacity to observe our planet. Fused with dramatic advances in artificial intelligence, the capability to extract patterns and meaning from those data streams heralds an era of insights into the physical world. In order to draw appropriate inferences about and between environments, the systematic selection of these data is necessary to create representative and unbiased samples. To this end, we introduce the Systematic Street View Sampler (S3) framework, enabling researchers to produce their own user-defined datasets of Street View imagery. We describe the algorithm and express its asymptotic complexity in relation to a new limiting computational resource (Google API Call Count). Using the Amazon Mechanical Turk distributed annotation environment, we demonstrate the utility of S3 in generating high quality representative datasets useful for machine vision applications. The S3 algorithm is open-source and available at github.com/CU-BIC/S3 along with the high quality dataset representing power infrastructure in rural regions of southern Ontario, Canada.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".