Evaluation of Urban Vegetation Mapping Using High Spatial Resolution Image:Pixel Versus Object Classification Comparison
Why this work is in the frame
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Bibliographic record
Abstract
On two different scales of USGS classification system,the pixel and object based approaches to classification of urban vegetation covers were evaluated using high spatial resolution aerial orthoimagery and LiDAR data.Using conventional pixel-based supervised maximum likelihood classification,the overall accuracy(OA)is 70.5% and the Kappa coefficient is 63.5% for the low classification level.For the high classification level,the OA of 84% is achieved with a 75.8% Kappa.In comparison,an object-based classification approach achieves overall accuracies of 86%(Kappa:82.3%)for the low classification level and 90.8%(Kappa:86.2%)for the high classification level.The results show that the object-based classification has a significant improvement over the pixel based approach and is suggested as an alternative to pixel based classification in urban vegetation mapping for high space resolution images.
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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 | 0.002 | 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 | 0.001 |
| 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 it