Roof report from automatically generated 3D building models by straight skeleton computation
Bibliographic record
Abstract
3D building models are important in several fields, such as urban planning and roof report for insurance industries. However, enormous time and labor has to be consumed to create these 3D models, using a 3D modeling software such as 3ds Max or SketchUp. In order to automate laborious steps, a GIS and CG integrated system is proposed for automatically generating 3D building models, based on building polygons (building footprints) on digital maps. Digital maps show most building polygons' edges meet at right angles (orthogonal polygon). In the digital map, however, not all building polygons are orthogonal. In either orthogonal or non-orthogonal polygons, the new system is proposed for automatically generating 3D building models with general shaped roofs by straight skeleton computation. In this paper, the algorithm for shrinking a polygon and forming a straight skeleton are clarified and, the new methodology is proposed for constructing roof models by assuming `the third event' and, at the end of the shrinking process, the shrinking polygon is converged to `a line of convergence'. In our research, extended straight skeleton computation is used for automatic generation of roof models. Based on the monotone polygons which straight skeleton computation forms, roof boards are automatically generated, and a top view of these roof boards can be a roof report for natural disaster damage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".