Three-Dimensional Polygonal Building Model Estimation From Single Satellite Images
Bibliographic record
Abstract
This paper introduces a novel system for automatic detection and height estimation of buildings with polygonal shape roofs in singular satellite images. The system is capable of detecting multiple flat polygonal buildings with no angular constraints or shape priors. The proposed approach employs image primitives such as lines, and line intersections, and examines their relationships with each other using a graph-based search to establish a set of rooftop hypotheses. The height (mean height from rooftop edges to the ground) of each rooftop hypothesis is estimated using shadows and acquisition geometry. The potential ambiguities in identification of shadows in an image and the uncertainty in identifying true shadows of a building have motivated for a fuzzy logic-based approach that estimates buildings heights according to the strength of shadows and the overlap between identified shadows in the image and expected shadows according to the building profile. To reduce the time complexity of the implemented system, a maximum number of eight sides for polygonal rooftops is assumed. Promising experimental results verify the effectiveness of the presented system with overall mean shape accuracy of 94% and mean height error of 0.53 m on QuickBird satellite (0.6 m/pixel) imageries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".