A Multicriteria Evaluation Method for 3-D Building Reconstruction
Bibliographic record
Abstract
This letter proposes a multicriteria system to evaluate the accuracy of reconstructed 3-D buildings. Current 3-D evaluation methods are derived from 2-D pixel-based evaluation; however, the difference between 2-D and 3-D evaluation methods is not well presented in previous literature. Most 3-D building evaluation methods concentrate solely on rooftop accuracy while ignoring the degree of accuracy found with regard to walls. To address these problems, this letter designs a multicriteria evaluation system based on three components: volume, surface, and point. The volume accuracy component represents the traditional classification accuracy based on random samples. The surface accuracy component evaluates shape similarity which compares sample and reference buildings, including rooftops and walls, in a true 3-D environment. The point accuracy component measures distance at feature points between the sample building and the reference building. This multicriteria system aims to provide an improved evaluation method for building reconstruction using advanced algorithms and multiplatform data. The system is also expected to provide valuable information to guide applications with different accuracy requirements.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".