Validating Google Earth Pro as a Scientific Utility for Use in Accident Reconstruction
Bibliographic record
Abstract
With the prevalence of satellite imagery in the analysis of collision events growing in the field of accident reconstruction, this research aims to quantify, refine, and compare the accuracies of measurements obtained utilizing conventional instruments to the measurements obtained using Google Earth Pro software. Researchers documented and obtained 1305 unique measurements from 68 locations in 25 states and provinces in the United States, Canada, and Australia using measuring wheels and tape measures. Measurements of relevant features at each location (crosswalks, curved roadways, off-road features, etc.) were documented and subdivided into three groups: On-Road, Off-Road, and Curved Path measurements. These measurements were compared to the measurements obtained of the same features from current and historical satellite imagery within Google Earth Pro. The accuracy of the relative measurements was divided into distance-based subsets within the three groups; for example, the error of measurements ranging from 0 - 12 feet to over 1000 feet both On- and Off-road were quantified and compared. These comparisons established the rate of error for each distance-based subset for the On-Road, Off-Road, and Curved Path measurement groupings taken from Google Earth Pro versus conventionally-obtained measurements.
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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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".