Improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data based on a multiscale cylindrical neighborhood
Bibliographic record
Abstract
Laser point cloud filtering is a fundamental step in various applications of light detection and ranging (LiDAR) data. The progressive triangulated irregular network (TIN) densification (PTD) filtering algorithm is a classic method and is widely used due to its robustness and effectiveness. However, the performance of the PTD filtering algorithm depends on the quality of the initial TIN-based digital terrain model (DTM). The filtering effect is also limited by the tuning of a number of parameters to cope with various terrains. Therefore, an improved PTD filtering algorithm based on a multiscale cylindrical neighborhood (PTD-MSCN) is proposed and implemented to enhance the filtering effect in complex terrains. In the PTD-MSCN algorithm, the multiscale cylindrical neighborhood is used to obtain and densify ground seed points to create a high-quality DTM. By linearly decreasing the radius of the cylindrical neighborhood and the distance threshold, the PTD-MSCN algorithm iteratively finds ground seed points and removes object points. To evaluate the performance of the proposed PTD-MSCN algorithm, it was applied to 15 benchmark LiDAR datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) commission. The experimental results indicated that the average total error can be decreased from 5.31% when using the same parameter set to 3.32% when optimized. Compared with five other publicized PTD filtering algorithms, the proposed PTD-MSCN algorithm is not only superior in accuracy but also more robust.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".