OBJECT-BASED URBAN TREE COVER EXTRACTION FROM HIGH SPATIAL RESOLUTION OPTICAL AND LIDAR IMAGERY: TECHNIQUES AND DATA INTEGRATION
Bibliographic record
Abstract
Tree canopy cover is a fundamental measure of the urban forest, which benefits a city socially and environmentally. In this thesis, methods are proposed to map urban tree cover. Chapter 2 presents an object-based tree cover extraction method using some new techniques for commonly available high-spatial-resolution colour-infrared imagery. The overall accuracy achieved for the 23 645 ha urban growth area of London, Ontario was 89.73%. This accuracy can be improved further by integrating LiDAR surface information. However, tall objects appear displaced in traditional orthoimages, causing misclassification. Chapter 3 presents a new method for correcting horizontal relief displacement of tall objects in orthorectified imagery without requiring the original aerial images and flight parameters. An object-based tree cover extraction method was developed to test the effectiveness of this correction. The overall accuracy for a 1600 ha sub-scene was improved significantly: from 94.66% (uncorrected) to 96.07% (empirically corrected) and 96.98% (geometrically corrected).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".