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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".