Automatic Rooftop Extraction in Nadir Aerial Imagery of Suburban Regions Using Corners and Variational Level Set Evolution
Why this work is in the frame
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Bibliographic record
Abstract
Building profile extraction from aerial imagery constitutes a key element in numerous geospatial applications. Rooftop detection has been addressed through a variety of approaches that are, however, rarely capable of coping with conditions such as arbitrary illumination, variant reflections, and complex building profiles. This paper proposes a new method for extracting 2-D rooftop footprints from nadir aerial imagery through a fully automatic approach that handles arbitrary illumination, variant reflections, and complex building profiles without shape priors. The proposed method combines the strength of energy-based approaches with distinctiveness of corners. Corners are assessed using multiple color and color-invariance spaces. A rooftop outline is generated from selected corner candidates and further refined to fit the best possible boundaries through level-set curve evolution that is enhanced via a mean squared error map. Experimental results confirm the ability of the presented system to effectively extract rooftop profiles with an overall average shape accuracy of 84%, correctness of 94%, completeness of 92 %, and quality of 88%.
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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