Measuring Real-World Ground Distance Using High-Spatial Resolution Remotely Sensed Data: A Student-Focused Hands-on Study
Bibliographic record
Abstract
Students under the direction of geospatial science faculty, 30 real-world distances were measured on the campus of Stephen F. Austin State University in the field with tape. Students were then instructed on how to measure all 30 real-world features remotely using drone imagery, point cloud data, pictometry data and the Google Earth Pro online interface. Real-world measurements were compared to remote sensing measurements taken by the students to calculate the root mean square error (RMSE). In addition, an ANOVA was conducted on the absolute errors to determine the statistical significance of the variation among the remotely sensed methods, while a Tukey test was performed to assess the statistical significance between the methods. Students discovered that the RMSE results indicate that the pictometry measurements were the most accurate, with an RMSE of 0.68 meters, and that the point cloud data were the least accurate, with an RMSE of 1.27 meters. The ANOVA results indicate that there was a significant difference in the mean absolute error among the methods, whereas the point cloud data, with a mean absolute error of 1.0423 meters, were significantly less accurate than those of the other methods, which was confirmed by the Tukey test.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".