Urban land use/land cover mapping with high-resolution SAR imagery by integrating support vector machines into object-based analysis
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
This paper investigates the capability of high-resolution SAR data for urban landuse/land-cover mapping by integrating support vector machines (SVMs) into object-based analysis. Five-date RADARSAT fine-beam C-HH SAR images with a pixel spacing of 6.25 meter were acquired over the rural-urban fringe of the Great Toronto Area (GTA) during May to August in 2002. First, the SAR images were segmented using multi-resolution segmentation algorithm and two segmentation levels were created. Next, a range of spectral, shape and texture features were selected and calculated for all image objects on both levels. The objects on the lower level then inherited features of their super objects. In this way, the objects on the lower level received detailed descriptions about their neighbours and contexts. Finally, SVM classifiers were used to classify the image objects on the lower level based on the selected features. For training the SVM, sample image objects on the lower level were used. One-against-one approach was chosen to apply SVM to multiclass classification of SAR images in this research. The results show that the proposed method can achieve a high accuracy for the classification of high-resolution SAR images over urban areas.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it