RADARSAT SAR data for landuse/land-cover classification in the rural-urban fringe of the greater Toronto area
Bibliographic record
Abstract
This research investigates the capability of the multitemporal RADARSAT Fine-Beam C-HH SAR imagery for extracting landuse/land-cover information in the rural-urban fringe of the Greater Toronto Area (GTA) using various image processing techniques and classification algorithms. Five-date RADARSAT fine-beam SAR images were acquired during May to August in 2002. The major landuse/land-cover classes were high-density built-up areas, low-density built-up areas, roads, forests, parks, golf courses, water and three types of agricultural lands. These ten classes were chosen to characterize the complex landuse/land-cover types in the rural-urban fringe of the GTA. The results demonstrated that, for identifying landuse/land-cover classes, five-date raw SAR imagery yielded very poor result due to speckles. The best result was achieved for combined Mean, Standard Deviation and Correlation texture images using artificial neural networks (ANN) (overall accuracy: 89.7 % and Kappa: 0.886). These high accuracies indicated that RADARSAT fine-beam SAR has the potential for operational landuse/land-cover mapping in urban environments.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".