Stereo-based building detection in very high resolution satellite imagery using IHS color system
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
Automatic detection of buildings out of urban objects is not a straightforward task due to the existing spectral and textural similarities. The problem gets even worse in buildings with pitched roofs. Pitched roof buildings receive dissimilar amount of solar radiation on their different faces causing different brightness values for a single roof. Thus, in object based classification methods, each side will probably be assigned to different segments preventing proper building boundary detection. In this study, in order to detect the proper building boundaries through image segmentation, IHS (Intensity, Hue, and Saturation) color system is used. Then, to detect buildings out of the segmented image, elevation information extracted from stereo satellite imagery is benefited. The presented method was tested on GeoEye stereo imagery and 92% of the image buildings were detected precisely.
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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 | 0.000 |
| 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 | 0.000 | 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