Building Detection in Very High Spatial Resolution Multispectral Images Using the Hit-or-Miss Transform
Why this work is in the frame
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Bibliographic record
Abstract
A method for building detection in very high spatial resolution multispectral images is presented. Buildings are detected using spectral and contextual information. First, potential building locations are enhanced on the basis of the spectral similarity between their roofs. To do this, the eigenvalue-based spectral similarity ratio is proposed. Next, the hit-or-miss transform (HMT) from mathematical morphology is used to assign pixels to buildings. To compute the HMT, fuzzy erosion and dilation are used. Additional processing based on size criteria is needed in some cases to separate buildings from roads. The method is tested on GeoEye and pan-sharpened Ikonos images. The preliminary results are promising.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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