A stereo image matching method to improve the DSM accuracy inside building boundaries
Bibliographic record
Abstract
In a digital surface model derived from high-resolution stereo images, the accuracies of building rooftop elevations are usually much lower than that of open areas without tall objects. This problem makes building height estimation difficult from the stereo images. The inaccuracy of building rooftop elevation is caused by many factors including shadows, occlusions, smoothing constraints in the matching algorithms, and the mismatch on extremely high buildings. To improve the accuracy of building rooftop elevations, this study enhances the existing image matching methods by adding building footprint maps as a constraint. The proposed image matching method consists of three steps. First, a building footprint was used to identify the corresponding building rooftop locations in the stereo images. Second, the left and right stereo images were matched according to color and shape. Third, the left and right sub-images of the building were further matched at pixel level to generate detailed roof elevations. Validation using surveyed rooftop elevations demonstrated that the proposed method can estimate building rooftop elevation with a one-third error using the current commercial software. In addition, the errors for low-rise and high-rise buildings were consistent in the proposed method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".