Texture-Integrated Classification of Urban Treed Areas in High-Resolution Color-Infrared Imagery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
lkaditional multispectral classification methods have not provided satisfying results for treed area extraction from highresolution digital imagery because trees are characterized not only by their spectral but also by their textural properties. Treed areas in urban regions are especially dificult to extract due to their small area. Many other urban objects, such as lawn and playgrounds, cause confusion because they display similar, even identical, spectral properties. In this study a texture integrated classification method is proposed. To effectively extract tree textural features and eliminate noise, an algorithm of conditional variance detection is developed, which consists of a directional variance detection and a local variance detection. This algorithm detects tree features with higher accuracy than common texture algorithms. By integrating the new algorithm with traditional multispectral classification, treed areas in urban regions can be extracted with sufficiently high accuracy. Application of the new approach in different urban areas indicates that the average accuracy of treed area extraction was increased from 67 percent, using a multispectral classification, to 96 percent, using the texture integrated classification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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