Does Google Earth CRS induce bias with increasing UTM zone number?
Bibliographic record
Abstract
Google Earth (GE) has been very popular since 2005 amongst remote-sensing science enthusiasts as well as administrators for its ease of use and the large archive of multiresolution temporal data covering the entire globe. With time, Google Earth has evolved to publish layers of imagery with spatial resolution estimated to be better than 30 centimeters(since GE does not publish any information related to accuracy of the imagery). Many scholars have studied the positional accuracy of Google Earth imagery from 2008 till 2018. Improvements in the positional accuracy(absolute as well as relative) were reported by many.In this study,it was attempted to understand if the CRS of Google Earth(GE) is contributing to positional inaccuracies when its imagery is assessed using reliable satellite reference data or GPS surveyed data. Relative assessment of GE location coordinates of ground check points was done using Sentine1-2B satellite imagery. Test imagery over regions of Africa, Canada, India and Australia were used and RMSE values were reported. The radial RMSE for the image check points was computed as 12.5 metres; min-max values in Easting direction were [-23.48 m,8.26 m] while min-max values in Northing direction were [-5.77m, 17. 26m]. The paper focuses on planimetric mislocation/accuracy between Google Earth and Sentine1-2B and explores direct or indirect relationship between increasing UTM zone numbers and mislocation values. The paper attempts to emphasize that there is a need for the end-user/consumer to understand that, though Google Earth is undeniably the best go-to-resource with open access and high usability rating, it has its own quality issues and should be used with caution in applications where quality and reliability are at stake. GE is indeed an excellent source of ancillary information but it is expected that the end-user be aware of inherent accuracy issues of Google Earth imagery when consuming in applications ranging from as small as a geo-spatial survey by a school community to larger projects requiring high accuracy and precision like mapping, autonomous navigation, control point surveying or satellite calibration.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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 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".