A Local Topological Information Aware Based Deep Learning Method for Ground Filtering from Airborne Lidar Data
Bibliographic record
Abstract
As a foundational preprocessing step for a lot of downstream tasks, ground filtering from airborne LiDAR data is designed to separate the ground points and preserve the off-ground points with complete shape information. However, because of the undulating terrain, it is still a challenge work to filter the ground under complex mountain regions. In this paper, we provide a deep learning based model to improve the ground filtering performance in abrupt slope using airborne LiDAR point clouds. Specifically, we first design a local topological information mining module to extract the local features. Then a modified graph convolutional networks (GCNs) is developed to fusion the local features and global features. Compared with most existing methods, our model not only enjoys the parameter-free advantage, which means it can be applied easily in various areas, but also obtains better ground filtering performance and can preserve more complete information contained in off-ground points. Experiments was implemented on seven forest areas. The proposed method obtains promising ground filtering results with mean total error of 6.46% and the mean kappa coefficient of 86.01%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".