Digital twins of building extraction from dual-channel airborne LiDAR point clouds
Bibliographic record
Abstract
The new dual-channel airborne LiDAR system can acquire dense point clouds of roofs and facades at the same time, RIEGL VQ-1560i has the most advanced dual-channel LiDAR system with unique and innovative bi-directional scanning angles, which provides better building instance extraction than traditional Scanner for more complete and precise data.This abstract presents the first point cloud building extraction for urban digital twins using dual-channel airborne LiDAR data.The main challenges of this lidar data are the large number of points, complex data structure, and multiple classes of objects.We propose a preprocessing-free architectural extraction method.It consists of three steps, namely point cloud slicing, projection, and constraint-based extraction of labels.Point cloud slices consist of top-down merging of elevation and 3D semantic segmentation to reorganize point cloud scenes into interrelated point groups.This greatly reduces the processing difficulty and computational burden of complex structures while removing multiple classes of non-building points.Second, we project the point group into an image to further reduce computational complexity while improving processing efficiency.Finally, we label the building with its up-down relationship and remap it as a 3D building.Experimental results show that the proposed method achieves an average recall rate of 95.36% and an average F1 score of 93.59%.For digital twin instance segmentation, the quality of the two public test scenarios reaches 92.86% and 98.31%, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".